US20100324388A1 - Body-worn pulse oximeter - Google Patents
Body-worn pulse oximeter Download PDFInfo
- Publication number
- US20100324388A1 US20100324388A1 US12/559,403 US55940309A US2010324388A1 US 20100324388 A1 US20100324388 A1 US 20100324388A1 US 55940309 A US55940309 A US 55940309A US 2010324388 A1 US2010324388 A1 US 2010324388A1
- Authority
- US
- United States
- Prior art keywords
- patient
- motion
- ecg
- ppg
- worn
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Abandoned
Links
Images
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/0245—Detecting, measuring or recording pulse rate or heart rate by using sensing means generating electric signals, i.e. ECG signals
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1118—Determining activity level
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/02108—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics
- A61B5/02125—Measuring pressure in heart or blood vessels from analysis of pulse wave characteristics of pulse wave propagation time
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/022—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/145—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
- A61B5/1455—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters
- A61B5/14551—Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue using optical sensors, e.g. spectral photometrical oximeters for measuring blood gases
- A61B5/14552—Details of sensors specially adapted therefor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
- A61B5/681—Wristwatch-type devices
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6825—Hand
- A61B5/6826—Finger
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/683—Means for maintaining contact with the body
- A61B5/6838—Clamps or clips
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/74—Details of notification to user or communication with user or patient ; user input means
- A61B5/746—Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
- A61B5/022—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers
- A61B5/02225—Measuring pressure in heart or blood vessels by applying pressure to close blood vessels, e.g. against the skin; Ophthalmodynamometers using the oscillometric method
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
- A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
- A61B5/1116—Determining posture transitions
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6813—Specially adapted to be attached to a specific body part
- A61B5/6824—Arm or wrist
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
- A61B5/7207—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
- A61B5/721—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using a separate sensor to detect motion or using motion information derived from signals other than the physiological signal to be measured
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7239—Details of waveform analysis using differentiation including higher order derivatives
Definitions
- the present invention relates to medical devices for monitoring vital signs, e.g., saturation of peripheral oxygen, or SpO2.
- SpO2 sometimes referred to as the ‘fifth vital sign’, represents a patient's blood oxygen saturation.
- Medical professionals can detect hypoxemia, i.e. a deficiency of oxygen, by monitoring a patient's SpO2. Values between about 95-100% are considered normal; those below this indicate hypoxemia, and will typically trigger an alarm in a hospital setting.
- a technique called pulse oximetry measures SpO2.
- this parameter is determined from a patient's arterial oxygen saturation, or SaO2, which is a percentage of oxygenated arterial hemoglobin present in their blood. Functional hemoglobin molecules can bind with up to four oxygen molecules to yield ‘oxygenated’ hemoglobin (HbO2).
- hemoglobin molecule bound to less than four oxygen molecules is classified as ‘reduced’ hemoglobin (Hb).
- Conventional pulse oximeters feature algorithms that assume only HbO2 and Hb are present in the blood, and measure SpO2 from the ratio of oxygenated hemoglobin to the total amount of hemoglobin (both oxygenated and reduced) according to equation (1):
- HbO2 and Hb feature different absorption spectra in the visible and infrared regions, and can therefore be measured optically.
- Conventional pulse oximeters thus typically feature light sources (most typically light-emitting diodes, or LEDs) that radiate in the red (near 660 nm) and infrared (typically between 900-950 nm) spectral regions.
- a photodetector measures a portion of radiation at each wavelength that transmits through the patient's pulsating blood, but is not absorbed.
- Hb absorbs about ten times as much radiation as HbO2
- HbO2 absorbs about two times as much radiation as Hb.
- Detection of transmitted radiation at these wavelengths yields two time-dependent waveforms, each called a plethysmogram (PPG), that an oximeter analyzes to solve for SpO2 as defined in equation (1) above.
- PPG plethysmogram
- the oximeter processes PPG waveforms measured with red (RED(PPG)) and infrared (IR(PPG)) wavelengths to determine time-dependent AC signals and time-independent DC signals.
- AC red
- IR infrared
- DC in contrast, are portions of the PPG that are relatively invariant with time, e.g. the portion of the signal originating from scattering off of components such as bone, skin, and non-pulsating components of the patient's blood.
- AC signals are measured from a heartbeat-induced pulse present in both waveforms.
- the pulse represents a pressure wave, launched by the heart, which propagates through the patient's vasculature and causes a time-dependent increase in volume in both arteries and capillaries.
- a temporary volumetric increase results in a relatively large optical absorption according to the Beer-Lambert Law.
- DC signals originate from radiation scattering from static components such as bone, skin, and relatively non-pulsatile components of both arterial and venous blood. Typically only about 0.5-1% of the total signal measured by the photodetector originates from the AC signal, with the remainder originating from the DC signal. Separation of AC and DC signals is typically done with both analog and digital filtering techniques that are well-known in the art.
- r which is sometimes called a ‘ratio of ratios’ (RoR) represents a ratio of Hb to HbO2. It equates an actual SpO2 value, which ranges from 0-100% O2, to an empirical relationship that resembles a non-linear equation. Above about 70% O2 this equation typically yields values that are accurate to a few percent. Measurements below this value, while not necessarily accurate, still indicate a hypoxic patient in need of medical attention.
- RoR ratio of ratios
- Pulse oximeters for measuring SpO2 were originally developed in 1972, and have evolved over the last 30 years to a point where they are commonplace in nearly all vital sign monitors for in-hospital use.
- Typical pulse oximeters feature a probe encased in a clothespin-shaped housing that includes both red and infrared LEDs, and a photodetector that detects radiation from the LEDs after it passes through a portion of the patient's body.
- the probe typically clips to a patient's index finger.
- Most probes operate in a transmission-mode optical geometry, and relay analog waveforms measured by LEDs and the photodetector to an external processing unit.
- the processing unit is typically integrated into a stand-alone monitor that measures only SpO2 and pulse rate (determined from the AC signal of one of the PPG waveforms), or a complete vital sign monitor that measures SpO2 along with systolic (SYS), mean (MAP), and diastolic (DIA) blood pressure, heart rate (HR), respiratory rate (RR), and temperature (TEMP).
- SYS systolic
- MAP mean
- DIA diastolic
- HR heart rate
- RR respiratory rate
- TMP temperature
- the oximeter probe typically connects to the monitor through a cable.
- Alternate configurations of SpO2 monitors include those that operate in reflection-mode optical geometries, probes that clip onto appendages other than the patient's finger (e.g. their ear or forehead), and processing units that are worn directly on the patient's body (e.g. their wrist).
- PPG waveforms, along with SpO2 and pulse rate values are sent wirelessly from the oximeter to
- pulse oximetry can be extremely sensitive to a patient's motion. Activities such as walking, finger tapping, falling, and convulsing can result in a number of artifacts that distort both the AC and DC components of waveforms measured with the oximeter's optical system. Motion-related activities, for example, can cause the oximeter probe to move relative to the patient's finger, change the amount of ambient light that irradiates the photodetector, and disrupt both arterial and venus blood flow in vasculature measured by the optical system. Each of these events can generate artifacts that, in some cases, are similar to the AC and DC signals within the PPG waveforms. Ultimately this can cause the pulse oximeter to generate inaccurate values and false alarms.
- Oximeters suffer other problems outside of their measurement accuracy. Probes encapsulating a patient's index finger can be uncomfortable and awkward, especially when worn for extended periods of time. Pulse oximeters that lack body-worn processing units can only provide measurements when a patient is sedentary and attached to a bedside monitor; they are impractical for ambulatory patients moving about the hospital, making it difficult to provide true continuous monitoring. Most body-worn oximeters typically lack systems for continuously measuring all vital signs, and particularly blood pressure, from a patient.
- the invention described herein provides a body-worn monitor that continuously measures pulse oximetry and other vital signs, along with motion, posture, and activity level, from an ambulatory patient.
- the system features an oximetry probe that comfortably clips to the base of the patient's thumb, thereby freeing up their fingers for conventional activities in a hospital, such as reading and eating.
- the probe reversibly secures to the thumb with, e.g., an easy-to-use Velcro strap, disposable tape, or similar closure, or may be provided in the form of a closed ring which slips over the thumb. It measures time-dependent waveforms (RED/IR(PPG)) corresponding to LEDs typically operating near 660 nm and 905 nm.
- RED/IR(PPG) time-dependent waveforms
- Analog versions of these waveforms pass through a low-profile cable to a wrist-worn transceiver enclosing a processing unit. Also within the wrist-worn transceiver is a wireless system (typically based wireless protocols such as 802.11 and 802.15.4) that sends information through a network to a remote receiver, e.g. a computer located in a central nursing station.
- a wireless system typically based wireless protocols such as 802.11 and 802.15.4
- a remote receiver e.g. a computer located in a central nursing station.
- Clinically accurate pulse oximetry measurements made at the base of the patient's thumb require a set of coefficients relating r (from Eq. 2) to SpO2 that are typically determined with a set of empirical experiments (e.g. a ‘breathe down’ study, described below). These coefficients differ from those used in conventional oximetry measurements because of the differences between vasculature in the base of the thumb and the tip of the index finger. Typically the base of the thumb features relatively fewer capillary beds, and thus the coefficients are preferably adjusted accordingly.
- Three motion-detecting sensors form part of the body-worn monitoring system. They are typically secured to the patient's torso (e.g. chest), upper arm (e.g. bicep), and lower arm (e.g. wrist), and measure time-dependent motion signals (ACC waveforms).
- the wrist-worn transceiver receives and processes these motion signals to determine the patient's degree of motion, posture, and activity level.
- Each sensor typically measures a unique ACC waveform along three axes (x, y, and z), and ultimately yields information that can be processed to determine a separate component of the patient's motion.
- the sensor worn on the lower arm (which may be within the wrist-worn transceiver) monitors movement of the patient's hand and fingers; such motion typically disrupts the RED/IR(PPG) waveforms. It can therefore be processed and used to exclude certain noise-corrupted artifacts from the SpO2 calculation.
- Sensors attached to the lower and upper arms each measure signals that are collectively analyzed to estimate the patient's arm height; this can be used to improve accuracy of a continuous blood pressure measurement (cNIBP), as described below.
- cNIBP continuous blood pressure measurement
- the sensor attached to the patient's chest measures signals that are analyzed to determine the patient's posture and activity level, which can affect measurements for SpO2, cNIBP, and other vital signs.
- a walking patient typically yields noisy PPG waveforms, but also has a low probability of being hypoxic due to their activity state.
- a patient in this condition thus does not typically generate an alarm/alert, regardless of the value of SpO2 that is measured.
- a patient that is convulsing or falling typically yields noisy RED/IR(PPG) waveforms from which it is difficult to extract an SpO2 value. But these activity states, regardless of the patient's SpO2 values, may trigger an alarm/alert because they indicate the patient needs medical assistance.
- the body-worn monitor features systems for continuously monitoring patients in a hospital environment, and as the patient ultimately transfers from the hospital to the home.
- Both SpO2 and cNIBP rely on accurate measurement of PPG and ACC waveforms, along with an electrocardiogram waveform (ECG), from patients that are both moving and at rest.
- ECG electrocardiogram waveform
- cNIBP is typically measured with the ‘Composite Technique’, which is described in detail in the co-pending patent application entitled: VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contents of which are fully incorporated herein by reference.
- the Composite Technique typically uses a single PPG waveform from the SpO2 measurement (typically the IR(PPG) waveform, as this typically has a better signal-to-noise ratio than the RED(PPG) waveform), along with the ECG waveform, to calculate a parameter called ‘pulse transit time’ (PTT) which strongly correlates to blood pressure.
- PPG pulse transit time
- the ECG waveform features a sharply peaked QRS complex that indicates depolarization of the heart's left ventricle, and, informally, provides a time-dependent marker of a heart beat.
- PTT is the time separating the peak of the QRS complex and the onset, or ‘foot’, of the RED/IR(PPG) waveforms; it is typically a few hundred milliseconds.
- the QRS complex, along with the foot of each pulse in the RED/IR(PPG), can be used to more accurately extract AC signals using a mathematical technique described in detail below.
- both the RED/IR(PPG) waveforms are collectively processed to enhance the accuracy of the cNIBP measurement.
- the electrical system for measuring SpO2 features a small-scale, low-power circuit mounted on a circuit board that fits within the wrist-worn transceiver.
- the transceiver can further include a touchpanel display, barcode reader, and wireless systems for ancillary applications described, for example, in the following applications, the contents of which have been previously incorporated by reference: BODY-WORN MONITOR FEATURING ALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No. 12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009).
- the invention provides a system and method for monitoring a physiological property of a patient's blood (e.g. a SpO2 value).
- the invention features a first sensor with two radiation sources that emit optical radiation at first and second wavelengths, and a photodetector configured to detect the radiation after it passes through a portion of the patient.
- a finger-ring housing that houses the radiation sources and the photodetector features a ring-shaped mounting portion that fits or wraps around a base of the patient's thumb.
- a processing unit worn on the patient's wrist and operably connected to the finger-ring sensor, receives signals from the photodetector and includes both a motion sensor and a processor.
- the processor is configured to process: i) the first and second signals to determine AC signals; ii) at least one of the AC signals and the motion signal to determine selected AC signals; and iii) the selected AC signals, or signals derived therefrom, to determine the physiological property of the patient's blood.
- the mounting portion comprises a curved, ring-shaped portion that partially surrounds the base of the patient's thumb, while leaving the tip uncovered.
- the ring-shaped portion can connect to a flexible strap made of, e.g., nylon or fabric.
- the first and second radiation sources are proximal to one another (and are often within the same electronic package) and are separated from the photodetector by an angle between 75-110 degrees.
- the processing unit includes an input port (e.g. a serial port operating a serial protocol, such a control area network, or CAN protocol) configured to receive an electrical signal (e.g. a digitized ECG signal).
- the ECG signal for example, is generated by a series of body-worn electrodes connected to differential amplifier circuit.
- the cable that supplies the ECG signal can include this circuit, and can plug directly into the serial input port.
- the ECG signal includes a time-dependent marker (e.g. a QRS complex) that precedes both the first and second PPG waveforms generated by each heartbeat by less than pre-determined time period (e.g. 500 ms). It can be processed to determine heart rate, and can additionally be processed to detect both the AC and DC signals within the PPG waveforms, along with motion that may disrupt them. If motion is detected, the system can be instructed to simply ignore the AC and DC components; this is typically done if motion exceeds a pre-determined level known to corrupt these signals beyond an acceptable level. Alternatively, if motion is present but is less than the pre-determined level, its influence over the AC and DC components may be removed using frequency-domain filtering, deconvolution, or similar techniques.
- both cNIBP and SpO2 are simultaneously detected from both the PPG and ECG signals.
- cNIBP is determined, for example, from a time difference between a peak of a QRS complex in the ECG signal and an onset point in one of the AC signals.
- the time difference for example, is PTT
- cNIBP is determined according to the Composite Technique.
- cNIBP is most accurately determined when the finger-ring sensor is worn on the base of the patient's thumb.
- parameters relating ratios of the AC and DC signals to SpO2 need to be determined beforehand using, e.g., conventional breathe down studies.
- the invention provides a method for simultaneously measuring both SpO2 and a motion-related event (e.g. a patient's posture, activity level, and degree of motion) from the patient.
- a motion-related event e.g. a patient's posture, activity level, and degree of motion
- posture may be measured with a single sensor (e.g. an analog or digital three-axis accelerometer) mounted on the patient's torso.
- the accelerometer can be mounted alongside the ECG circuit in a terminal portion of the ECG cable.
- posture is typically determined from a vector corresponding to orientation of the patient's torso. Specifically, an angle separating the vector from a pre-determined coordinate system ultimately yields posture, as is described in detail below.
- Activity level (corresponding, e.g., to moving, walking, falling, convulsing) is another motion-related event determined in this embodiment. It can be calculated from a mathematical transform of time-dependent variations of a motion signal that yields a frequency-domain spectrum. Portions of the spectrum (e.g. the power of specific frequency components) are compared to pre-determined frequency-dependent parameters to determine the activity level. Other operations, such as a mathematical derivative of the time-dependent motion signal, or a series of ‘decision rules’ based on a decision-tree algorithm, can also yield the activity level.
- the invention provides a complete body-worn vital sign monitor for measuring all the patient's vital signs, including SpO2, cNIBP, and oscillometric blood pressure (SYS, DIA, and MAP).
- the body-worn monitor features a wrist-worn processing unit that includes, for example, multiple input ports to operably connect with stand-alone systems for measuring some of the vital signs (e.g. to receive cables associated with systems for measuring ECG and oscillometric blood pressure). Additional ports may also be used to collect signals from external sensors that measure, e.g., glucose level, respiration rate, and end-tidal CO 2 .
- each port typically operates on a common communication protocol, such as the CAN protocol.
- Input ports corresponding to ECG and oscillometric blood pressure are typically located on a common side of the processing unit that typically faces away from the patient's hand.
- any cable connecting to an input port may include an accelerometer to characterize the patient's motion.
- the processing unit features a touchpanel display that renders a first user interface that displays information describing oxygen saturation, a second user interface that displays information describing blood pressure, and a third user interface that displays information describing ECG signals.
- the processing unit can also include a barcode scanner that scans a barcode of a medical professional (located, e.g., on their badge).
- the wrist-worn transceiver can render a user interface corresponding to the medical professional. This prevents the patient from viewing medical information that may, for example, cause unnecessary alarm.
- the processing unit includes a speaker for voice communications, or for generating audible voice messages intended for the patient.
- the processing unit can also include a wireless transmitter that communicates through, e.g., a hospital network.
- the invention provides a method for measuring SpO2 and cNIBP by processing ECG, PPG, and motion signals with filters that analyze both PTT and AC signals of the PPG waveforms with a mathematical filter.
- the filter for example, can be a ‘matched filter’, described in detail below.
- Filtering PTT values includes, for example, determining values that lie outside a pre-determined range using statistical filters (e.g. a simple average and standard deviation).
- SpO2 values are typically calculated from ratios describing the AC and DC signals measured from individual pulses using optical systems operating in the red and infrared optical spectral regions. One or more ratios can be calculated for the pulse.
- the invention provides a method for suppressing an alarm/alert based on the SpO2 value by processing the patient's posture and activity state.
- the alarm can be suppressed if the patient's posture is upright (e.g. standing up), as patients having this posture are typically not in immediate need of medical assistance.
- the alarm can be suppressed if the patient's posture changes from lying down to sitting or standing up (or, alternatively, the other way around). In this case the change in posture, which can be determined with the chest-worn accelerometer, may disrupt the PPG waveforms to the point where an alarm/alert would be falsely generated in the absence of such alarm suppression.
- FIG. 1 shows a schematic drawing of a pulse oximeter probe configured as a finger-ring sensor worn around the base of a patient's thumb;
- FIG. 2 shows an expanded, schematic drawing of the pulse oximeter probe of FIG. 1 with a dual-wavelength LED and photodetector separated from the finger-ring sensor;
- FIG. 3A is a schematic drawing of a patient's thumb with the pulse oximeter probe of FIG. 2 attached;
- FIG. 3B shows a cross-sectional, schematic drawing of a patient's thumb measured with the pulse oximeter probe of FIG. 3A ;
- FIG. 4 is a graph showing a relationship between SpO2 and a ‘ratio of ratios’ (RoR) for measurements from theoretical model in a medical textbook, made from the tip of a patient's index finger, and made from the base of a patient's thumb;
- RoR ratio of ratios
- FIG. 5 is a graph comparing measurements from 20 unique patients made with the thumb-worn pulse oximeter probe of FIG. 1 to those made at the tip of the index finger with a commercial oximeter probe;
- FIG. 6 is a graph showing time-dependent waveforms measured with an accelerometer (ACC), ECG system (ECG), and the 660 and 905 nm LEDs (RED(PPG) and IR(PPG), respectively) within the pulse oximeter probe of FIG. 1 ;
- FIG. 7 is a flow chart describing an algorithm for measuring SpO2 and patient motion with the pulse oximeter probe of FIG. 1 ;
- FIG. 8 is a graph showing data collected during a breathe down study indicating correlation between SpO2 measured with the pulse oximeter probe shown in FIG. 2 and SpO2 measured with a blood gas analyzer, which in this case represents a ‘gold standard’;
- FIGS. 9A and 9B are graphs, respectively, of time-dependent IR(PPG), ECG, and ACC waveforms measured when a patient is not undergoing motion;
- FIGS. 10A and 10B are graphs, respectively, of time-dependent IR(PPG), ECG, and ACC waveforms measured when a patient is undergoing minor finger motion;
- FIGS. 11A and 11B are graphs, respectively, of time-dependent IR(PPG), ECG, and ACC waveforms measured when a patient is undergoing major finger motion;
- FIG. 12A is a graph of time-dependent RED/IR(PPG), ECG, and ACC waveforms similar to those shown in FIGS. 9 A,B- 11 A,B measured during periods of motion and no motion;
- FIGS. 12B and 12D are graphs of the IR(PPG) waveforms of FIG. 12A measured, respectively, during periods of motion and no motion, along with a graphical indication of how multiple RoRs are calculated from the waveforms;
- FIG. 12C is a graph of data points representing RoRs between components of the RED(PPG) and IR(PPG) waveforms measured during periods of no motion and at periodic intervals between the foot and peak of the waveform shown in FIG. 12B ;
- FIGS. 12E and 12F are graphs of data points representing RoRs between components of the RED(PPG) and IR(PPG) waveforms measured during periods of motion at periodic intervals between the foot and peak of the waveform shown in FIG. 12D ;
- FIG. 13A shows a graph of time-dependent ECG and IR(PPG) waveforms that are processed to determine a PPT
- FIGS. 13B and 13C are graphs, respectively, of PTT versus heartbeat before and after applying a matched filter to the IR(PPG) waveform used to calculate PTT as shown in FIG. 13A ;
- FIG. 13D is a flow chart showing an algorithm for analyzing PTT and applying a matched filter to an IR(PPG) waveform when motion-related noise is present in the waveform;
- FIG. 14 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a resting patient by, respectively, the ECG system, the optical system, and the accelerometer system of FIG. 1 ;
- ECG time-dependent waveforms
- FIG. 15 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a walking patient by, respectively, the ECG system, the optical system, and the accelerometer system of FIG. 1 ;
- ECG time-dependent waveforms
- FIG. 16 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a convulsing patient by, respectively, the ECG system, the optical system, and the accelerometer system of FIG. 1 ;
- ECG time-dependent waveforms
- FIG. 17 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a falling patient by, respectively, the ECG system, the optical system, and the accelerometer system of FIG. 1 ;
- ECG time-dependent waveforms
- FIG. 18 is a schematic drawing of a coordinate system assigned to a patient wearing three accelerometers and a pulse oximeter probe;
- FIG. 19A is a graph showing time-dependent ACC waveforms corresponding to different posture states and measured with an accelerometer positioned on a patient's chest;
- FIG. 19B is a graph showing posture states calculated using the time-dependent ACC waveforms of FIG. 19A and a mathematical model for determining a patient's posture;
- FIG. 20 is an electrical timing diagram showing how the 660 nm and 905 nm LEDs are driven in the pulse oximeter probe described in FIG. 1 ;
- FIG. 21 is an electrical diagram of a circuit used to drive the 660 nm and 905 nm LEDs according to the timing diagram shown in FIG. 20 ;
- FIG. 22 is an electrical diagram of a circuit used to amplify and filter the RED/IR(PPG) waveforms to generate the RED/IR(AC) and RED/IR(DC) components used to calculate SpO2;
- FIGS. 23A and 23B show images of the body-worn monitor of FIG. 1 attached to a patient with and without, respectively, a cuff-based pneumatic system used for calibrating the cNIBP measurement;
- FIG. 24 shows an image of the wrist-worn transceiver featured in the body-worn monitor of FIGS. 23A and 23B .
- FIGS. 1 and 2 show a pulse oximeter probe 1 shaped as a finger ring that wraps around a base of patient's thumb 3 to measure SpO2 and cNIBP.
- the probe 1 is designed to be comfortably worn for extended periods (e.g. several days) while freeing up the patient's thumb and hands for activities such as reading and eating that are commonplace in, e.g., a hospital. Motion corresponding to these and other activities can affect the SpO2 measurement and is detected with a network of accelerometers worn on the patient's body.
- the probe 1 makes a transmission-mode optical measurement along an inner portion of the thumb 3 with a pair of embedded LEDs 9 , 10 operating at, respectively, 660 and 905 nm, and a single photodetector 12 that detects these wavelengths after they pass through vasculature and other tissue lying beneath the LEDs 9 , 10 .
- both LEDs 9 , 10 and the photodetector 12 are positioned to measure blood pulsing in portions of the princeps pollicis artery, which is the principal artery of the thumb and stems from the radial artery. As described in detail below, measuring blood flowing in this artery enhances the accuracy of the cNIBP measurement.
- a small circuit board 11 supports the photodetector 12 and may additionally include, for example, a trans-impedance amplifier for amplifying photocurrent from the photodetector 12 and converting it into a corresponding voltage.
- the circuit board 12 also includes a resistor 19 that identifies specific wavelengths emitted by the LEDs 9 , 10 ; these wavelengths, in turn, influence values of correlation coefficients that relate RoR to SpO2, as described below.
- a ring-shaped, flexible plastic housing 6 formed into a cylindrical radius of curvature features rectangular openings 18 , 22 that support the LEDs 9 , 10 and circuit board 11 .
- the housing 6 features three cut-out portions 23 A-C, or ‘living hinges’, that make it easily bendable and able to accommodate thumbs of difference sizes. It is held in place around the base of the thumb with a flexible nylon strap 2 threading through two D-ring openings 13 A, 13 B located the housing's distal ends.
- a portion 4 of the strap 2 features a patch 17 of Velcro (containing, e.g., ‘hooks’) that adheres to a mated patch 16 (containing, e.g., ‘loops’) on the strap's main portion; the patches 16 , 17 temporarily adhere to each other when the housing 6 is worn on the patient's thumb 3 , and easily detach so that it can be removed.
- the straps 2 , 4 allow the probe 1 to be securely fastened, which in turn minimizes motion relative to the measurement site.
- a flexible, cable 5 connects the oximeter probe 1 to a wrist-worn transceiver, similar to the one shown in FIG. 24 .
- the cable 5 carries I/O signals from the wrist-worn transceiver that drive the LEDs according to the timing diagram in FIG. 20 , and analog signals measured by the photodetector to an amplifying/filtering circuit within the transceiver, as shown in FIG. 22 . There, the analog signals are amplified, filtered, digitized, and processed to measure SpO2, as described in detail below.
- the LEDs 9 , 10 intermittently emit beams 28 , 29 of radiation at 660 nm and 905 nm at roughly 500 Hz according to the timing diagram in FIG. 20 .
- the beams 28 , 29 pass through the base of the thumb 3 and rapidly diverge to scatter off tissue such as skin 24 , bone 25 , capillaries 26 near the thumb's outer surface, and a portion of the princeps pollicis artery 27 before reaching the photodetector 12 .
- the LEDs 9 , 10 and photodiode 12 are separated by approximately 35-55 degrees.
- Optical components separated at this angle tend to increase the relative contribution of signal coming from the artery 27 ; ultimately this improves the accuracy of the cNIBP measurement, as PTT values measured from arterial components correlate better to blood pressure than those measured from capillary components.
- Both the capillaries 26 and princeps pollicis artery 27 carry blood that pulsates with each heartbeat and absorbs radiation emitted by the LEDs 9 , 10 . This results in separate time-dependent optical waveforms (i.e. RED/IR(PPG), shown in FIG. 6 , generated by the 660 and 905 nm radiation.
- Both waveforms feature AC signals corresponding to the time-dependent pulsating blood, and DC signals corresponding to time-independent scattering off the skin 24 , bone 25 , and non-pulsating components of the capillaries 26 and artery 27 .
- the AC component Prior to any filtering the AC component typically represents about 0.5-1% of the total signal.
- the body-worn monitor calculates these components using a number of signal-processing methodologies that are additionally important for determining PTT-based cNIBP.
- the AC and DC components yield a RoR which then relates to a SpO2 using a series of empirically determined coefficients.
- the RoR is determined by first measuring RED/IR(PPG) waveforms, and then passing them through a low-pass filter characterized by a 20 Hz cutoff. The averaged baseline components of each waveform are sampled and stored in memory, and represent RED/IR(DC).
- Both waveforms are then additionally filtered with high-pass filter having a 0.1 Hz cutoff frequency, which is typically implemented with a finite impulse response function, and finally amplified with a variable gain amplifier.
- high-pass filter having a 0.1 Hz cutoff frequency, which is typically implemented with a finite impulse response function
- amplified with a variable gain amplifier.
- These steps can be done with either analog electronic or digital software filters. Components passing through this filter are isolated as described below with reference to FIGS. 6 and 7 to yield RED/IR(AC). Once determined, the AC and DC signals are processed to yield a RoR value, described in equation (3), which relates to SpO2:
- FIG. 4 shows an empirical relationship between RoR and SpO2 for measurements made at the base of the thumb with the oximeter probe shown in FIGS. 1 and 2 (solid line), along with similar relationships for measurements made at the tip of the index finger with an off-the-shelf oximeter probe (small dashes), and the theoretical curve for measurements made from the tip of the index finger (large dashes). Curves corresponding to measurements made from the index finger and thumb are determined empirically from a group of patients measured under similar conditions. As is clear from the figure, the relationships between RoR and SpO2 are similar, but slightly offset due to differences in the measurement site.
- SpO2 values for this experiment typically range from 70-100%.
- Simultaneous studies are typically done using pulse oximeter probes having LEDs with different red emission spectra.
- the wavelength-dependent values of RoR are related to SpO2, as determined by the blood gas analyzer, to calculate coefficients a, b, c as described in Table 1.
- coefficients a, b, c as described in Table 1.
- a different set of coefficients will result for the different LED wavelengths.
- the wavelengths of the LEDs Prior to manufacturing of the pulse oximeter probe (e.g. the probe shown in FIG. 1 ), the wavelengths of the LEDs are determined, typically with a resolution of about 1 nm, using an emission spectrophotometer. LEDs are then sorted by wavelength and associated with a resistor having a value that is stored in the above-described database. During manufacturing, the resistor is soldered to the circuit board (and optionally trimmed with a laser to give it a well-defined resistance) within the pulse oximeter probe shown in FIG. 1 . During an actual measurement, the wrist-worn transceiver delivers a voltage through the cable connecting it to the pulse oximeter probe that decreases in value after passing through the resistor.
- This voltage drop is sensed by the transceiver using the analog-to-digital converter and processor, and then used to calculate the resistor value. The value is compared to the database, and ultimately used to select the appropriate a, b, and c coefficients described above. Ultimately this maximizes accuracy of the SpO2 calculation.
- SYS and DIA can be explicitly determined for each heartbeat using an algorithm described in the above-mentioned patent applications, the contents of which have been previously incorporated herein by reference.
- PTT values are processed over a 20-40 second time period (often implemented as a ‘rolling average’) using statistical filtering to improve accuracy.
- times corresponding to RED(foot) and IR(foot) are typically averaged together.
- cNIBP measurements made from this particular location were well within the FDA's standards for accuracy ( ⁇ 5 mmHg) and standard deviation (8 mmHg). For this and other reasons the base of the thumb appears to be a uniquely good location for measuring both SpO2 and cNIBP.
- FIG. 5 shows a direct comparison between SpO2 measured from the base of the thumb and tip of the index finger from a group of 20 separate patients. Each patient was measured for a 30-second period from the tip of finger with a commercially available oximeter probe, and then for a comparable period from the base of the thumb with an oximeter probe substantially similar to that shown in FIGS. 1 and 2 . During the measurement an average value for SpO2 was detected from each location. For these patients the relationships between RoR and SpO2 shown in FIG. 4 were used for both sets of measurements. As is clear from the data, the correlation for these measurements is within experimental error (estimated at 1% SpO2 for each measurement) for all 20 patients. The mean difference between the two measurements (thumb SpO2-index finger SpO2) is ⁇ 0.6% O2, and the standard deviation of the differences is 1.39% O2. Measurements were made over a range of 93-100% O2.
- the pulse oximeter system described above is integrated into a complete, body-worn monitoring system, shown in FIGS. 23A and 23B , that measures and analyzes ECG, ACC, RED(PPG) and IR(PPG) waveforms to characterize the patient's vital signs, degree of motion, activity level, and posture.
- FIG. 6 shows the various time-dependent waveforms measured concurrently over a 3-second period using such a system.
- the ACC waveform represents a measurement along a single axis made by one of the three accelerometers incorporated in the body-worn monitor. Typically this is the accelerometer included in the wrist-worn transceiver, as this site is closest to the measurement site for SpO2.
- SpO2 values are determined by measuring the red and infrared waveforms between the peak and foot values, indicated by colored triangles in the figure and described in more detail below.
- these peak values are determined by filtering each waveform as described above, and then taking a first derivative and evaluating a zero-point crossing, indicating the point where the waveform's slope changes from a positive to negative value.
- Values for these parameters can be averaged over several pulses and processed with statistical techniques (e.g. averaging after excluding values outside of ⁇ 1 standard deviation) to yield the RED/IR(peak) values for individual pulses within the RED/IR(PPG) waveforms.
- RED/IR(foot) values for each pulse are used for PTT-based cNIBP measurements, and are typically processed with statistical techniques as described above. The foot of each waveform is best calculated by measuring a peak from the second derivative of the RED/IR(PPG) waveforms.
- RED/IR(DC) values are typically determined by sampling RED/IR(PPG) waveforms after passing analog versions of these waveforms through a low-pass filter as described above.
- Electrodes adhered to the patient's chest and connected to an ECG circuit in the body-worn monitor measure a three-lead ECG, with FIG. 6 showing a time-dependent waveform taken from Lead II.
- the ECG waveform features a series of QRS complexes, indicated by the black triangles, with each QRS complex corresponding to a single heartbeat.
- the QRS complex typically proceeds each pulse in the RED/IR(PPG) by between about 100-200 milliseconds, and is easy to detect because of its sharp, well-defined features. Moreover, as described in more detail below with reference to FIGS.
- each QRS complex can serve as a marker or ‘fiducial’ for detecting AC signals in the two PPGs.
- an algorithm operating on the wrist-worn transceiver detects a QRS complex (e.g. ECG QRS-N) and then sequentially analyzes both the RED/IR(PPG) waveforms at times within 500 ms of this feature.
- the algorithm determines that they are immeasurable (most likely because of noise in the PPG), and begins searching for similar values corresponding to the next heartbeat. If the ACC waveform indicates a high degree of motion, then the RED/IR(PPG) waveforms are processed as described below to determine if foot and peak parameters can be extracted from them. If not, the waveforms are determined to be immeasurable, the first and second derivative tests are not performed, and the ECG and PPG waveforms corresponding to the N+1 heartbeat are investigated. This process is repeated, typically for a total time of between 5-10 seconds, for as many heartbeats as possible.
- the values for RED/IR(foot) and RED/IR(peak) can be processed to determine an RoR for each heartbeat. This value can then be analyzed with the statistical techniques described above to limit artifacts and ultimately generate a SpO2 value with the greatest possible accuracy.
- the amplitude of each pulse in the RED/IR(PPG) waveforms is calculated as shown in equations (5) and (6) below:
- RED(AMP) RED(peak) ⁇ RED(foot) (5)
- IR(AMP) IR(peak) ⁇ IR(foot) (6)
- the DC values for the RED/IR(PPG) waveforms are then determined by first calculating any DC offset values (RED/IR(DC)) supplied to the differential inputs of the analog-to-digital converter; these inputs are indicated, for example, by RED/IR(DC) labels in FIG. 22 . Once digitized, these values can then be used to calculate an ‘effective’ DC value, RED/IR(DC*), for both the red and infrared wavelengths, as described below in equations (7) and (8).
- Ambient light defined as AMBIENT(DC) is accounted for by measuring any radiation incident on the photodetector when neither the red or infrared LEDs are turned on (this occurs, for example, between current pulses that drive the respective LEDs, as shown in FIG. 20 ).
- RED ⁇ ( DC * ) RED ⁇ ( peak ) + RED ⁇ ( foot ) 2 + RED ⁇ ( DC ) - AMBIENT ⁇ ( DC ) ( 7 )
- IR ⁇ ( DC * ) IR ⁇ ( peak ) + IR ⁇ ( foot ) 2 + IR ⁇ ( DC ) - AMBIENT ⁇ ( DC ) ( 8 )
- the ACC waveform in FIG. 6 provides an accurate indication of the patient's motion.
- this waveform along with two others corresponding to additional axes of a coordinate system, is sensed with a solid-state device called an accelerometer.
- An accelerometer is typically a micro electrical-mechanical system (MEMS) device that measures the acceleration of the part of the body that it is attached to.
- the ACC waveform measured by the accelerometer features DC values that indicate the position of the accelerometer relative to a gravity vector, and AC values that indicate movement-induced acceleration.
- Suitable accelerometers typically have a response times less than about 1 microsecond, and are thus more than adequate for detecting most types of patient motion, which typically occurs in less than 15 Hz.
- Processing the ACC waveforms yields at least three valuable pieces of information relevant to the SpO2 measurement: it can determine i) a patient's posture; ii) their activity state (e.g. are they lying down, walking, sitting, standing); and iii) whether or not the patient's hand in moving, thereby indicating that the IR/RED(PPG) waveforms may be corrupted by noise, and thus more likely to yield erroneous values for SpO2. Additional processing of the ACC waveforms yields the patient's arm height, from which hydrostatic changes in blood pressure can be estimated and used to calibrate the cNIBP measurement.
- FIG. 7 shows a flow chart describing an algorithm 33 that collectively determines SpO2, SYS, and DIA, along with a patient's motion, posture, and activity level, by processing the time-dependent RED/IR(PPG), ECG, and ACC waveforms shown in FIG. 6 .
- the algorithm 33 begins with measuring the ECG, RED/IR(PPG), and ACC waveforms (nine total waveforms measured with three separate accelerometers) using a body-worn monitor, similar to that shown in FIGS. 23A , 23 B (step 35 ).
- RED/IR(PPG) waveforms are measured with the pulse oximeter probe described above, and the ECG waveform is measured with an ECG circuit that terminates an ECG cable and attaches to at least three electrodes worn on the patient's chest.
- Three accelerometers are typically disposed, respectively, in the wrist-worn transceiver, in a cable that attaches to the patient's bicep, and on the chest proximal to the ECG circuit.
- a series of analog filters (performed with hardware components described with reference to FIG. 22 ) and digital filters (performed in software using well-known frequency-domain techniques, such as using a ‘matched filter’, described below) process each waveform to remove unwanted and relatively high-frequency electrical and mechanical noise (step 36 ).
- Such filtering increases the accuracy to which data points corresponding to IR/RED(peak) and IR/RED(foot) can be determined.
- RED/IR(DC) values are determined directly from waveforms that pass through a low-pass filter characterized by a 20 hz cutoff frequency, as described in equations (7) and (8), above (step 47 ).
- the ECG QRS for each heartbeat is detected with a beat-picking algorithm described, for example, in the following reference, the contents of which are incorporated herein by reference (step 37 ): ‘ECG Beat Detection Using Filter Banks’, Afonso et al., IEEE Trans. Biomed Eng., 46:192-202 (1999).
- Heart rate is typically determined from the inverse of time separating neighboring QRS complexes.
- Degree of motion, posture, and activity level are then determined from the nine ACC waveforms, as described in detail in the above-referenced patent applications, and briefly below (step 38 ).
- Each of these parameters features its own unique classification. For example, degree of motion can be ranked numerically depending on the magnitude of acceleration detected from one or more accelerometers.
- Posture is typically divided into well-known categories such as standing, lying on back, lying prone, etc.
- Activity levels include activities such as walking, falling, resting, and convulsing.
- the algorithm determines that no significant motion is present (step 39 )
- it proceeds to process the RED/IR(PPG) waveforms, starting at a time corresponding to the ECG QRS generated by a first heartbeat, and continuing the processing until a pre-determined time delta (e.g. 500 ms) or a time corresponding to a neighboring heart beat is reached (step 41 ).
- a pre-determined time delta e.g. 500 ms
- ‘significant motion’ refers to an amount of motion that would render the RED/IR(PPG) waveforms unreliable for calculation of SpO2.
- This processing typically involves further digitally filtering the waveforms to remove any high-frequency noise, and then determining IR/PPG(peak) values from the first derivative of each waveform, and IR/PPG(foot) values from the second derivative of each waveform (step 42 ). If during step 39 motion is determined to be present, the algorithm proceeds to analyze both RED/IR(PPG) waveforms to determine if they are distorted in any way (step 40 ). Such analysis, for example, can involve complex methods such as comparing a pulse from one or both of the PPG waveforms to a ‘known good pulse’ collected during a period of relatively low motion.
- the comparative method can be a linear or non-linear numerical fitting algorithm, such as a least-squares or Levenburg-Marquardt algorithm, or based on a standard correlation algorithm that determines agreement between the immediate pulse and the known good pulse.
- This latter approach can be implemented as a ‘matched filter’, and is described in detail below with regard to equations (13) and (14).
- a matched filter algorithm for example, is preferably implemented in step 36 to improve the signal-to-noise ratio of both the PPG and, to a lesser extent, the ECG waveform prior to processing these signals. Or it could be implemented during step 40 to determine a degree of correlation between the immediate pulse in the PPG waveform and a known good pulse to determine if the immediate pulse is corrupted by motion.
- a derivation of the matched filter is provided, for example, in Turin, ‘An introduction to matched filters’, IRE Transactions of Information Theory 6: 311-329 (1960), the contents of which are incorporated herein by reference.
- a relatively simple method for determining a known good pulse involves, for example, determining the standard deviation of the PPG waveform during a time period commensurate with the ECG QRS (at this point the pulse is not evident in the PPG waveform), and comparing this to a pre-determined metric to estimate the motion-induced noise in the waveform. If either PPG is determined to be significantly distorted by motion, it is not included in the algorithm, and the processes of collecting and analyzing RED/IR(PPG), ECG, and ACC waveforms is repeated (steps 35 - 38 , 47 ).
- RED/IR(foot) and RED/IR(peak) can be used to calculate both SpO2 and cNIBP.
- RED/IR(PTT) values are determined from the time difference separating the ECG QRS and RED/IR(foot).
- Values corresponding to RED/IR(AC) are determined from the waveforms between the RED/IR(foot) and RED/IR(peak) values, as described above in equation (9), and below in equations (10)-(11). This yields a RoR for each heartbeat-induced pulse in the waveforms.
- Both RED/IR(AC) and RED/IR(PTT) values are typically determined for each heartbeat measured over a pre-determined period ranging from about 10-30 seconds, and then subjected to a series of statistical tests (step 44 ) that typically involve taking the average and standard deviation of each value over the time period.
- a ‘rolling average’ can also be used during step 44 so that fresh values are determined, e.g., every second. Values that lie outside of one standard deviation from the average are typically removed, and then the average is recalculated.
- the final average value of PTT is then determined as the average of the averaged RED/IR(PTT) values, while the final average value of RED/IR(AC) is determined in a similar manner from the RoR values determined for each pulse (step 45 ).
- the algorithm calculates cNIBP values of SYS and DIA directly from the averaged PTT value, as described in detail in the above-referenced patent applications (step 46 ). Using equation (4), it calculates SpO2 from the RoR values determined during step 45 .
- an alarm/alert is only generated from SYS, DIA, and SpO2 values after processing the patient's degree of motion, posture, and activity level (determined during step 38 ). For example, if the patient is determined to be walking with a normal gate, it can be assumed that their values of SYS, DIA, and SpO2 do not warrant an alarm/alert, even if one or all of these parameters exceeds a pre-determined alarm threshold.
- SpO2 data collected with the pulse oximeter probe shown in FIG. 2 and processed with the algorithm shown in FIG. 7 correlate well with that analyzed with a blood gas analyzer, which in this case represents a ‘gold standard’.
- Data shown in the figure were collected during a conventional breathe down study, wherein SpO2 values of 15 healthy volunteers were systematically lowered from a normal value near 100% O2 to an abnormal value near about 70% O2 by carefully controlling the subjects' oxygen intake. In total, about 20 data points were measured for each subject over this range.
- Blood samples for the blood gas analyzer were extracted using an in-dwelling catheter, similar to a conventional arterial line, inserted in the subjects' radial artery.
- FIGS. 9 A,B- 11 A,B indicate how different degrees of motion from a patient's fingers can influence both ECG and PPG signals, thereby affecting the accuracy of both SpO2 and cNIBP measurements.
- the IR(PPG) is shown, as this signal typically has a better signal-to-noise ratio than that of the RED(PPG).
- the ACC waveform is typically measured along the vertical axis of the accelerometer embedded in the wrist-worn transceiver. The magnitude of the axes for ECG, PPG, and ACC waveforms are the same for all figures.
- the ACC waveform is relatively flat and lacking any significant time-dependent features, indicating that the patient is not moving and is relatively still. Consequently the IR(PPG) in FIG. 9A , which is strongly affected by motion, features well-defined values for IR(foot), indicated by marker 53 , and IR(peak), indicated by marker 54 . Likewise the ECG waveform features a QRS complex, indicated by marker 52 , which is undistorted. The fidelity of these features indicate that both SpO2 and cNIBP values can typically be accurately determined during periods of little or no motion, as indicated by the ACC waveform in FIG. 9B .
- FIGS. 10 A,B show how minor amounts of finger motion affect both the ECG and IR(PPG) waveforms.
- finger motion is manifested by a time-dependent change in the ACC waveform, the beginning of which is indicated by marker 62 , which persists for a little less than one second. This is in stark contrast to the portion of the ACC waveform in the preceding dashed box 56 , which indicates no motion.
- Finger motion has basically no effect on the ECG waveform and its associated QRS complex, as indicated by marker 61 . But the motion causes a minor amount of distortion of the IR(PPG) waveform. Specifically, when the motion is maximized (i.e.
- a small bump indicated by marker 57 appears in the IR(PPG) waveform.
- This bump is an artifact that, in the absence of the ACC waveform, could be misinterpreted as a pulse containing both a foot and peak in the IR(PPG) waveform. Additionally, the minor finger motion distorts the foot (marker 58 ) and peak (marker 59 ) of the subsequent pulse in the IR(PPG) waveform, making it difficult to accurately determine these parameters using the derivative tests described above.
- an algorithm described herein can disregard them based on the magnitude of the ACC waveform, and continue its calculation of vital signs once the finger motion is reduced to an acceptable level.
- FIGS. 11 A,B show the affects of a major amount of finger motion on both the ECG and IR(PPG) waveforms.
- the period of motion is indicated in both figures by the dashed box 65 , which contrasts with the preceding period where motion is not present, shown in the dashed box 66 .
- the ACC waveform in FIG. 11B indicates the finger motion lasts for roughly one second, beginning and ending at times indicated, respectively, near markers 68 and 69 .
- the motion is complex and peaks in intensity at marker 69 .
- Even for major finger motion the ECG waveform and its QRS complex, indicated by marker 67 , are relatively undistorted.
- IR(PPG) measured during the period of motion is strongly distorted to the point that its peak value, indicated by marker 64 , is relatively flat and basically immeasurable. This makes it difficult to accurately measure IR(AC) and the subsequent SpO2 value calculated from this parameter.
- the IR(foot) value, indicated by marker 63 is also distorted, but to a lesser degree than the corresponding peak value.
- Equation (9), above yields a single RoR value for each pulse in the RED/IR(PPG) waveforms.
- the method for calculating SpO2 based on a single value is limited, as only one RoR can be calculated for each heartbeat; if values from several heartbeats are averaged together it can thus take several seconds to update the SpO2 value.
- the single RoR value can be strongly influenced by motion during pulses within the RED/IR(PPG) waveforms, as described above with reference to FIGS. 9A , B- 11 A, B.
- RoR can be calculated using a method indicated schematically in FIGS. 12A-F .
- multiple ‘sub-ratios’ between AC components corresponding to RED/IR(PPGs) are calculated from the foot of each pulse to its corresponding peak, determined as described above.
- the sub-ratios are calculated for each time interval of ⁇ (typically 33 ms, corresponding to 30 Hz) and only during periods of no motion, as determined from the ACC waveforms.
- ⁇ typically 33 ms, corresponding to 30 Hz
- This RoR can then be further processed for multiple pulses.
- a pulse oximetry measurement based on sub-ratios has the advantage of being relatively accurate and having a faster update rate when compared to the method associated with equation (9), which calculates just a single RoR for each pulse in the RED/IR(PPG) waveforms.
- equation (9) calculates just a single RoR for each pulse in the RED/IR(PPG) waveforms.
- FIG. 12A shows ACC, ECG, and RED/IR(PPG) waveforms measured during periods of no motion (indicated by the dashed box 70 ) and motion (indicated by the dashed box 71 ).
- motion in FIG. 12A is indicated by sharp, abrupt changes in the ACC waveform generated along a vertical axis by an accelerometer mounted in the wrist-worn transceiver.
- the ECG waveforms shown in both dashed boxes 70 , 71 are relatively immune to motion, and thus feature QRS complexes that can be measured easily. They serve as fiducial markers for analyzing the RED/IR(PPG) waveforms which, unlike the ECG waveform, are strongly affected by motion.
- FIG. 12B shows the IR(PPG) from dashed box 70 in FIG. 12A , which is measured when the patient is not moving.
- the waveform features a smooth, systematic rise time that is relatively easy to process with first and second derivative tests to determine its peak (indicated by marker 73 ) and foot (indicated by marker 72 ).
- sub-ratios are only calculated between these markers 72 , 73 , as they are characterized by a relatively large amplitude change in the RED/IR(PPG) and are more indicative of a patient's actual SpO2 value than sub-ratios calculated in the second half of the waveform, characterized by a relatively gradual decrease in intensity.
- a single sub-ratio is calculated from both the RED/IR(PPG) waveforms for each time interval ⁇ , as indicated by the dashed lines 78 .
- FIG. 12C shows the resulting sub-ratios, indicated as dark circles with values of RoR(n) in the graph; the values of each of these data points are indicated below by equation (10):
- RoR ⁇ ( n ) [ RED ⁇ ( PPG : n + ⁇ ) - RED ⁇ ( PPG : n ) RED ⁇ ( PPG : n + ⁇ / 2 ) + RED ⁇ ( DC ) - AMBIENT ⁇ ( DC ) ] [ IR ⁇ ( PPG : n + ⁇ ) - IR ⁇ ( PPG : n ) IR ⁇ ( PPG : n + ⁇ / 2 ) + IR ⁇ ( DC ) - AMBIENT ⁇ ( DC ) ] ( 10 )
- RED/IR(DC) and AMBIENT(DC) are assumed to be constant throughout the entire pulse, and are described above.
- each value of RoR(n) from equation (10) can be processed with a weighted average defined by wt(n) to determine the effective RoR, as shown below in equation (11):
- each weight wt(n) is determined by comparing an SpO2 calculated from its corresponding RoR(n) to a preceding value for SpO2 and determining the weight based on the correlation. For example, if the preceding value for SpO2 is 98% O2, a value for SpO2 in the range of 70-80% O2 calculated from RoR(n) is likely erroneous; the RoR(n) is therefore give a relatively low weight wt(n). Additionally, a relatively large change in the RED/IR(PPG) amplitude during the sub-ratio measurement period n typically indicates that the corresponding value of RoR(n) has a relatively high accuracy. Such values are thus given a relatively high weight wt(n). In general, a number of established statistical techniques can be used to weight the collection of RoR(n) values to generate the effective RoR, as defined above in equation (11).
- the collection of RoR(n) values are processed to determine an average and standard deviation. Values lying more than one standard deviation outside the average are then removed from the calculation, and the average is then recalculated.
- This technique while typically less accurate than that indicated by equation (11), has the advantage of not requiring a series of weights with arbitrary definitions.
- the collection of RoR(n) values are fit with a numerical function, such as a linear or non-linear function, and the effective RoR value can be estimated from the coefficients derived from the fit.
- the dashed box 71 in FIG. 12A indicates a more complicated situation where profound motion is present in the ACC waveform; this, in turn, strongly influences the morphology of the RED/IR(PPG) waveforms, while having little affect on the ECG waveform.
- FIG. 12D shows the resulting IR(PPG) waveform, along with markers 74 - 77 indicating various points along the waveform where its foot and peak, in theory, could be determined.
- application of the first and second first derivative tests could indicate two successive pulses, with the pulse between markers 74 , 75 and 76 , 77 yielding sub-ratios indicated, respectively, by the dashed lines 79 A and 79 B.
- the actual pulse while still affected by motion-related noise, lies roughly between the markers 74 , 75 , and results in the sub-ratios calculated according to dashed lines 79 A and shown in FIG. 12E .
- These sub-ratios are relatively noisy compared to those for the motion-free measurements indicated in FIG. 12C , but if processed with a weighted average like that indicated in equation (11) would likely yield a SpO2 value with suitable accuracy.
- the ‘pulse’ between markers 76 and 77 is not caused by an actual heartbeat, and instead is an artifact resulting solely from motion.
- sub-ratios calculated between these markers 76 , 77 result in artificial data points characterized by a large variation, as shown in FIG. 12F .
- the presence of motion, as indicated by the ACC waveform, drives the algorithm to remove these data points from a SpO2 calculation, as they will not result in an accurate value.
- FIGS. 13A-D show an alternate embodiment of the invention that uses PTT to estimate if a pulse is corrupted by noise, and if so deploys a ‘matched filter’ to process both the RED/IR(PPG) waveforms to maximize signal-to-noise ratios of the pulses therein.
- the body-worn monitor can determine accurate SpO2 and cNIBP values even when motion-related noise is present.
- FIGS. 13A-C feature a series of graphs on its left-hand side that show: i) how PTT is calculated from the time separating a peak of the ECG QRS and corresponding foot of the IR(PPG) ( FIG.
- FIG. 13A shows an alternative series of steps 105 - 111 for processing the ECG, RED/IR(PPG), and ACC waveforms that replace steps 37 - 41 in FIG. 7 .
- FIG. 13A shows PTT calculated from the time difference ( ⁇ T) separating the ECG QRS and the foot of the IR(PPG) waveform.
- This waveform is typically used in place of the RED(PPG) because of its superior signal-to-noise ratio.
- the QRS features a sharp, easily measurable peak informally marking the beginning of the cardiac cycle.
- the IR(PPG) waveform follows this feature by about 220 ms; its foot is typically calculated by taking the second derivative of the waveform after performing some initial filtering to remove high-frequency noise, and then looking for a zero-point crossing.
- PTT is determined during step 105 , and the process is repeated for consecutive heartbeats during a measurement interval, which is typically 10-20 seconds (step 106 ).
- a series of simple statistical filters are applied to detect pulses that may be corrupted by motion, and can thus potentially yield inaccurate values.
- a simple average (AVE) and standard deviation (STDEV) are first calculated in a rolling manner, beginning at pulse N and extending out ⁇ pulses, where ⁇ is the number of pulses within the above-described measurement interval. These statistical parameters can be updated for each subsequent pulse because of the rolling calculation. If the PTT value for the immediate pulse is more than 1 STDEV greater than the AVE for the preceding ⁇ pulses, as shown in step 108 , it is flagged as potentially originating from a IR(PPG) that is corrupted by motion.
- step 107 This simple filtering process is shown schematically by the window 112 in FIG. 13B (data points outside this window 112 are flagged), and is indicated in the flow chart by step 107 .
- a rapid change in PTT may be a real occurrence resulting from an actual fluctuation in blood pressure.
- the algorithm processes a corresponding ACC waveform generated by the accelerometer within the wrist-worn transceiver to determine if motion was indeed present during a time interval consistent with the PTT and its associated pulse (step 109 ).
- step 109 it may be determined that motion exceeded a pre-determined coefficient (e.g. M max ).
- M max can be determined, for example, from the power spectrum of time-dependent ACC waveforms that are known to corrupt RED/IR(PPG) waveforms, such as those shown in FIGS. 14-17 and described below.
- the pulse is considered to be corrupted to an extent that neither PTT nor parameters associated with SpO2 (e.g. RED/IR(foot) and RED/IR(peak)) can be accurately measured.
- the algorithm then returns to step 105 to resume calculating PTT from the ECG and IR(PPG) waveforms.
- the pulse is filtered with a matched filter (also referred to as a ‘North filter’ when used in telecommunications).
- a matched filter is one which features an ideal frequency response that maximizes the signal-to-noise ratio for a given signal of a known shape in the time domain, particularly when the signal is subject to random, stochastic noise, such as that caused by motion.
- a digital matched filter features an impulse response characterized by coefficients h(k). This function represents the time-reversed replica of the ideal signal to be detected, i.e. a pulse in the IR(PPG) measured during a time period where motion (as determined from the ACC waveform) is not present.
- h(k) can be determined from a standard, pre-programmed pulse, determined from waveforms measured from a large group of patients, which represents a known good pulse.
- this ‘textbook’ pre-programmed pulse is used initially in the matched filter, and then updated as subsequent known good pulses are measured from the patient.
- the subsequent known good pulse can be just a section of a pulse (e.g., near the foot or peak) that is known to be uncorrupted by noise.
- this pulse is represented by the pulse template function x tp (k)
- equation (13) the coefficients h(k) of the matched filter are given by equation (13):
- the digital matched filter can be represented as a finite impulse response filter with a typical transversal structure, with the output y(i) of the filter shown in equation (14):
- x(k) are the samples of the immediate pulse (i.e. the input pulse requiring filtering)
- x tp (k) are the samples of the pulse template
- N is the filter length
- i is a time shift index
- the matched filter improves the signal-to-noise ratio of the immediate input pulse by an amount that is directly related to the length of the filter (N).
- N the length of the filter
- step 110 PTT is calculated from the filtered waveform, and the rolling AVE and STDEV statistics are recalculated (step 107 ). If the difference between the immediate PTT, as calculated from the filtered waveform, is within ⁇ 1 STDEV of the average as per step 108 , then the pulse is considered to be free of motion-related artifacts that may cause erroneous values of SpO2 and cNIBP. Such a case is shown graphically in FIG. 13C . At this point, as indicated by step 111 , the ECG and RED/IR(PPG) are processed as per steps 42 - 46 and 48 - 50 in FIG. 7 , and SpO2 and cNIBP are determined.
- FIGS. 14-17 show time-dependent graphs of ECG, PPG, and ACC waveforms for a patient who is resting ( FIG. 14 ), walking ( FIG. 15 ), convulsing ( FIG. 16 ), and falling ( FIG. 17 ).
- Each graph includes a single ECG waveform 80 , 85 , 90 , 95 , PPG waveform 81 , 86 , 91 , 96 , and three ACC waveforms 82 , 87 , 92 , 97 .
- the PPG waveforms correspond to the IR(PPG) for the reasons described above.
- the ACC waveforms correspond to signals measured along the x, y, and z axes by a single accelerometer worn on the patient's wrist, similar to the accelerometer used to generate ACC waveforms in FIGS. 9B-11B .
- FIG. 14 shows data collected from a patient at rest. This state is clearly indicated by the ACC waveforms 82 , which feature a relatively stable baseline along all three axes of the accelerometer. High-frequency noise in all the ACC waveforms 82 , 87 , 92 , 97 shown in FIGS.
- ECG 80 and PPG 81 waveforms for this patient are correspondingly stable, thus allowing algorithms operating on the body-worn monitor to accurately determine SpO2 (from the PPG waveform 81 ), along with heart rate and respiratory rate (from the ECG waveform 80 ), cNIBP (from a PTT extracted from both the ECG 80 and PPG 81 waveforms).
- algorithms operating on the body-worn monitor assume that vital signs calculated from a resting patient are relatively stable; the algorithm therefore deploys normal threshold criteria for alarms/alerts, described below in Table 3, for patients in this state.
- FIG. 15 shows ECG 85 , PPG 86 , and ACC 87 waveforms measured from a walking patient wearing the body-worn monitor.
- the ACC waveform 87 clearly indicates a quasi-periodic modulation, with each ‘bump’ in the modulation corresponding to a particular step.
- the ‘gaps’ in the modulation shown near 10, 19, 27, and 35 seconds, correspond to periods when the patient stops walking and changes direction.
- Each bump in the ACC waveform includes relatively high-frequency features (other than those associated with electrical noise, described above) that correspond to walking-related movements of the patient's wrist.
- the ECG waveform 85 measured from the walking patient is relatively unaffected by motion, other than indicating an increase in heart rate (i.e., a shorter time separation between neighboring QRS complexes) and respiratory rate (i.e. a higher frequency modulation of the waveform's envelope) caused by the patient's exertion.
- the PPG waveform 86 in contrast, is strongly affected by this motion, and pulses within it become basically immeasurable. Its distortion is likely due in part to a quasi-periodic change in light levels, caused by the patient's swinging arm, and detected by the photodetector within the thumb-worn sensor. Movement of the patient's arm additionally affects blood flow in the thumb and can cause the optical sensor to move relative to the patient's skin.
- the photodetector measures all of these artifacts, along with a conventional PPG signal (like the one shown in FIG. 14 ) caused by volumetric expansion in the underlying arteries and capillaries within the patient's thumb.
- the artifacts produce radiation-induced photocurrent that is difficult to distinguish from normal PPG signals used to calculate SpO2 and cNIBP. These vital signs are thus difficult or impossible to accurately measure when the patient is walking.
- the body-worn monitor deploys multiple strategies to avoid generating false alarms/alerts during a walking activity state. As described in detail below, the monitor can detect this state by processing the ACC waveforms shown in FIG. 15 along with similar waveforms measured from the patient's bicep and chest. Walking typically elevates heart rate, respiratory rate, and blood pressure, and thus alarm thresholds for these parameters, as indicated by Table 2, are systematically and temporarily increased when this state is detected. Values above the modified thresholds are considered abnormal, and trigger an alarm. SpO2, unlike heart rate, respiratory rate and blood pressure, does not typically increase with exertion. Thus the alarm thresholds for this parameter, as shown in Table 2, do not change when the patient is walking. Body temperature measured with the body-worn monitor typically increases between 1-5%, depending on the physical condition of the patient and the speed at which they are walking.
- software associated with the body-worn monitor or remote monitor can deploy a series of ‘heuristic rules’ determined beforehand using practical, empirical studies. These rules, for example, can indicate that a walking patient is likely healthy, breathing, and characterized by a normal SpO2. Accordingly, the rules dictate that respiratory rate, blood pressure, and SpO2 values measured during a walking state that exceed predetermined alarm/alert thresholds are likely corrupted by artifacts; the system, in turn, does not sound the alarm/alert in this case. Heart rate, as indicated by FIG. 15 , and body temperature can typically be accurately measured even when a patient is walking; the heuristic rules therefore dictate the modified thresholds listed in Table 2 be used to generate alarms/alerts for these particular vital signs.
- FIG. 16 shows ECG 90 , PPG 91 , and ACC 92 waveforms measured from a patient that is simulating convulsing by rapidly moving their arm back and forth.
- the patient is at rest for the initial 10 seconds shown in the graph, during which the ECG 90 and PPG 91 waveforms are uncorrupted by motion.
- the patient then begins a period of simulated, rapid convulsing that lasts for about 12 seconds.
- a brief 5-second period of rest follows, and then convulsing begins for another 12 seconds or so.
- Convulsing modulates the ACC waveform 92 due to rapid motion of the patient's arm, as measured by the wrist-worn accelerometer. This modulation is strongly coupled into the PPG waveform 91 , likely because of the phenomena described above, i.e.: 1) ambient light coupling into the oximetry probe's photodiode; 2) movement of the photodiode relative to the patient's skin; and 3) disrupted blow flow underneath the probe. Note that from about 23-28 seconds the ACC waveform 92 is not modulated, indicating that the patient's arm is at rest. During this period the ambient light is constant and the optical sensor is stationary relative to the patient's skin.
- both ECG and PPG waveforms similar to those shown in FIG. 16 can be analyzed in conjunction with ACC waveforms measured from groups of stationary and moving patients. These data can then be analyzed to estimate the effects of specific motions and activities on the ECG and PPG waveforms, and then deconvolute these factors using known mathematical techniques to effectively remove any motion-related artifacts. The deconvoluted ECG and PPG waveforms can then be used to calculate vital signs, as described in detail below.
- the ECG waveform 90 is modulated by the patient's arm movement, but to a lesser degree than the PPG waveform 91 .
- modulation is caused primarily by electrical ‘muscle noise’ instigated by the convulsion and detected by the ECG electrodes, and well as by convulsion-induced motion in the ECG cables and electrodes relative to the patient's skin. Such motion is expected to have a similar affect on temperature measurements, which are determined by a sensor that also includes a cable.
- Table 3 shows the modified threshold values and heuristic rules for alarms/alerts generated by a convulsing patient.
- Heart rate determined from the ECG waveform for example, is typically erroneously high due to high-frequency convulsions, and respiratory rate is immeasurable from the distorted waveform. Strong distortion of the optical waveform also makes both SpO2 and PPT-based cNIBP difficult or impossible to measure. For this reason, algorithms operating on either the body-worn monitor or a remote monitor will not generate alarms/alerts based on vital signs when a patient is convulsing, as these vital signs will almost certainly be corrupted by motion-related artifacts.
- Table 3 also shows the heuristic rules for convulsing patients.
- the overriding rule is that a convulsing patient needs assistance, and thus an alarm/alert for this patient is generated regardless of their vital signs (which, as described above, are likely inaccurate due to motion-related artifacts).
- the system always generates an alarm/alert for a convulsing patient.
- FIG. 17 shows ECG 95 , PPG 96 , and ACC 97 waveforms measured from a patient that experiences a fall roughly 13 seconds into the measuring period.
- the ACC waveform 97 clearly indicates the fall with a sharp decrease in its signal, followed by a short-term oscillatory signal, due (literally) to the patient bouncing on the floor.
- ACC waveforms 97 associated with the x, y, and z axes also show a prolonged decrease in value due to the resulting change in the patient's posture.
- both the ECG 95 and PPG 96 waveforms are uncorrupted by motion prior to the fall, but basically immeasurable during the fall, which typically takes only 1-2 seconds.
- this activity adds very high frequency noise to the ECG waveform 95 , making it impossible to extract heart rate and respiratory rate during this short time period.
- Falling causes a sharp drop in the PPG waveform 96 , presumably for the same reasons as described above (i.e. changes in ambient light, sensor movement, and disruption of blood flow) for walking and convulsing, making it difficult to measure SpO2 and cNIBP.
- both the ECG 95 and PPG 96 waveforms are free from artifacts, but both indicate an accelerated heart rate and relatively high heart rate variability for roughly 10 seconds.
- the PPG waveform 96 also shows distortion and a decrease in pulse amplitude.
- the increase in heart rate may be due to the patient's baroreflex, which is the body's haemostatic mechanism for regulating and maintaining blood pressure.
- the baroreflex for example, is initiated when a patient begins faint. In this case, the patient's fall may cause a rapid drop in blood pressure, thereby depressing the baroreflex.
- the body responds by accelerating heart rate (indicated by the ECG waveform 95 ) and increasing blood pressure (indicated by a reduction in PTT, as measured from the ECG 95 and PPG 96 waveforms) in order to deliver more blood to the patient's extremities.
- Table 4 shows the heuristic rules and modified alarm thresholds for a falling patient. Falling, similar to convulsing, makes it difficult to measure waveforms and the vital signs calculated from them. Because of this and the short time duration associated with a fall, alarms/alerts based on vital signs thresholds are not generated during an actual falls. However, this activity, optionally coupled with prolonged stationary period or convulsion (both determined from the following ACC waveform), generates an alarm/alert according to the heuristic rules.
- a patient's posture can influence how the above-described system generates alarms/alerts from SpO2, cNIBP, and other vital signs.
- the alarms/alerts related to both SpO2 and cNIBP may vary depending on whether the patient is lying down or standing up.
- FIG. 18 indicates how the body-worn monitor can determine motion-related parameters (e.g. degree of motion, posture, and activity level) from a patient 110 using time-dependent ACC waveforms continuously generated from the three accelerometers 112 , 113 , 114 worn, respectively, on the patient's chest, bicep, and wrist.
- motion-related parameters e.g. degree of motion, posture, and activity level
- arm height can be determined using DC signals from the accelerometers 113 , 114 disposed, respectively, on the patient's bicep and wrist.
- Posture in contrast, can be exclusively determined by the accelerometer 112 worn on the patient's chest.
- An algorithm operating on the wrist-worn transceiver extracts DC values from waveforms measured from this accelerometer and processes them with an algorithm described below to determine posture.
- posture is determined for a patient 110 using angles determined between the measured gravitational vector and the axes of a torso coordinate space 111 .
- the axes of this space 111 are defined in a three-dimensional Euclidean space where ⁇ right arrow over (R) ⁇ CV is the vertical axis, ⁇ right arrow over (R) ⁇ CH is the horizontal axis, and ⁇ right arrow over (R) ⁇ CN is the normal axis. These axes must be identified relative to a ‘chest accelerometer coordinate space’ before the patient's posture can be determined.
- the first step in determining a patient's posture is to identify alignment of ⁇ right arrow over (R) ⁇ CV in the chest accelerometer coordinate space. This can be determined in either of two approaches.
- ⁇ right arrow over (R) ⁇ CV is assumed based on a typical alignment of the body-worn monitor relative to the patient. During a manufacturing process, these parameters are then preprogrammed into firmware operating on the wrist-worn transceiver. In this procedure it is assumed that accelerometers within the body-worn monitor are applied to each patient with essentially the same configuration.
- ⁇ right arrow over (R) ⁇ CV is identified on a patient-specific basis.
- an algorithm operating on the wrist-worn transceiver prompts the patient (using, e.g., video instruction operating on the wrist-worn transceiver, or audio instructions transmitted through a speaker) to assume a known position with respect to gravity (e.g., standing up with arms pointed straight down).
- the algorithm calculates ⁇ right arrow over (R) ⁇ CV from DC values corresponding to the x, y, and z axes of the chest accelerometer while the patient is in this position. This case, however, still requires knowledge of which arm (left or right) the monitor is worn on, as the chest accelerometer coordinate space can be rotated by 180 degrees depending on this orientation. A medical professional applying the monitor can enter this information using the GUI, described above.
- the second step in the procedure is to identify the alignment of ⁇ right arrow over (R) ⁇ CN in the chest accelerometer coordinate space.
- the monitor can determine this vector, similar to how it determines ⁇ right arrow over (R) ⁇ CV , with one of two approaches. In the first approach the monitor assumes a typical alignment of the chest-worn accelerometer on the patient. In the second approach, the alignment is identified by prompting the patient to assume a known position with respect to gravity. The monitor then calculates ⁇ right arrow over (R) ⁇ CN from the DC values of the time-dependent ACC waveform.
- the third step in the procedure is to identify the alignment of ⁇ right arrow over (R) ⁇ CH in the chest accelerometer coordinate space.
- This vector is typically determined from the vector cross product of ⁇ right arrow over (R) ⁇ CV and ⁇ right arrow over (R) ⁇ CN , or it can be assumed based on the typical alignment of the accelerometer on the patient, as described above.
- a patient's posture is determined using the coordinate system described above and in FIG. 18 , along with a gravitational vector ⁇ right arrow over (R) ⁇ G that extends normal from the patient's chest.
- the angle between ⁇ right arrow over (R) ⁇ CV and ⁇ right arrow over (R) ⁇ G is given by equation (14):
- ⁇ VG ⁇ [ n ] arccos ( R ⁇ G ⁇ [ n ] ⁇ R ⁇ CV ⁇ R ⁇ G ⁇ [ n ] ⁇ ⁇ ⁇ R ⁇ CV ⁇ ) ( 14 )
- the monitor compares the vertical angle ⁇ VG to a threshold angle to determine whether the patient is vertical (i.e. standing upright) or lying down:
- the patient is assumed to be upright, and their torso state, which is a numerical value equated to the patient's posture, is equal to 0.
- the patient is assumed to be lying down if the condition in equation (18) is not met, i.e. ⁇ VG >45 degrees. Their lying position is then determined from angles separating the two remaining vectors, as defined below.
- the angle ⁇ NG between ⁇ right arrow over (R) ⁇ CN and ⁇ right arrow over (R) ⁇ G determines if the patient is lying in the supine position (chest up), prone position (chest down), or on their side. Based on either an assumed orientation or a patient-specific calibration procedure, as described above, the alignment of ⁇ right arrow over (R) ⁇ CN is given by equation (19), where i, j, k represent the unit vectors of the x, y, and z axes of the chest accelerometer coordinate space respectively:
- ⁇ NG ⁇ [ n ] arccos ( R ⁇ G ⁇ [ n ] ⁇ R ⁇ CN ⁇ R ⁇ G ⁇ [ n ] ⁇ ⁇ ⁇ R ⁇ CN ⁇ ) ( 20 )
- the body-worn monitor determines the normal angle ⁇ NG and then compares it to a set of predetermined threshold angles to determine which position the patient is lying in, as shown in equation (21):
- ⁇ right arrow over (R) ⁇ CH is determined using either an assumed orientation, or from the vector cross-product of ⁇ right arrow over (R) ⁇ CV and ⁇ right arrow over (R) ⁇ CN as given by equation (22), where i, j, k represent the unit vectors of the x, y, and z axes of the accelerometer coordinate space respectively. Note that the orientation of the calculated vector is dependent on the order of the vectors in the operation. The order below defines the horizontal axis as positive towards the right side of the patient's body.
- ⁇ HG ⁇ [ n ] arccos ( R ⁇ G ⁇ [ n ] ⁇ R ⁇ CH ⁇ R ⁇ G ⁇ [ n ] ⁇ ⁇ ⁇ R ⁇ CH ⁇ ) ( 23 )
- the monitor compares this angle to a set of predetermined threshold angles to determine if the patient is lying on their right or left side, as given by equation (24):
- Table 5 describes each of the above-described postures, along with a corresponding numerical torso state used to render, e.g., a particular icon:
- FIGS. 19A and 19B show, respectively, graphs of time-dependent ACC waveforms 100 measured along the x, y, and z-axes, and the torso states (i.e. postures) 101 determined from these waveforms for a moving patient, as described above.
- the DC values of the ACC waveforms measured by the chest accelerometer vary accordingly, as shown by the graph 100 in FIG. 19A .
- the body-worn monitor processes these values as described above to continually determine ⁇ right arrow over (R) ⁇ G and the various quantized torso states for the patient, as shown in the graph 101 in FIG. 19B .
- the torso states yield the patient's posture as defined in Table 5.
- the patient rapidly alternated between standing, lying on their back, chest, right side, and left side within a time period of about 160 seconds.
- different alarm/alert conditions e.g. threshold values
- different alarm/alert conditions e.g. threshold values
- the time-dependent properties of the graph 101 can be analyzed (e.g. changes in the torso states can be counted) to determine, for example, how often the patient moves in their hospital bed. This number can then be equated to various metrics, such as a ‘bed sore index’ indicating a patient that is so stationary in their bed that lesions may result. Such a state could then be used to trigger an alarm/alert to the supervising medical professional.
- FIG. 20 is a schematic drawing showing time-dependent current pulses 120 , 121 used to drive the red and infrared LEDs in the pulse oximeter probe.
- both the red and infrared LEDs are alternately driven with separate current pulses 120 , 121 having a magnitude ranging between about 10 and 40 mA, which can be controlled dynamically with a closed-loop system to maximize the RED/IR(PPG) signal strength without saturating it, as described in more detail with regard to FIG. 22 .
- current pulses 120 , 121 typically drive the LEDs at 500 Hz for a time period of 100 ⁇ s, yielding a duty cycle of 5%.
- the separation between neighboring current pulses for the LEDs is typically maximized according to the drive frequency, and as indicated by the dashed line 122 is 1 ms for a drive frequency of 500 Hz.
- FIG. 21 shows a circuit 175 that generates the current pulses 120 , 121 described above for powering the LEDs.
- the circuit 175 features an operational amplifier 180 that receives a control voltage (V control ) on its gating pin.
- the amplifier 180 is connected to a transistor 182 and resistor 181 that, along with a supply voltage of 3.3V (typically from a Li:ion battery), generate the current pulses 120 , 121 used to drive a dual red/infrared LED 150 operating at two wavelengths (660/905 nm).
- the wavelength of the LED depends on the direction that it is biased.
- the circuit 175 features red control lines 185 , 190 and infrared control lines 187 , 189 that connect directly to I/O lines in a microprocessor within the wrist-worn transceiver.
- the current pulses 120 , 121 flow from the 3.3V supply voltage, across one direction of the LED 150 , and ultimately through the transistor 182 and resistor 181 to ground 183 .
- control lines 187 , 189 for infrared radiation are left open.
- this configuration persists for 100 ⁇ s, after which the red control lines 185 , 190 are switched closed, and the infrared control lines 187 , 189 are switched open.
- the alternating process is repeated at 500 Hz.
- red and infrared radiation that transmits through the patient's thumb is detected by a photodetector 155 that features both an anode and cathode.
- Each black dot in FIG. 20 indicates a separate wire in the cable that connects the oximeter probe to the wrist-worn transceiver.
- the wire associated with the cathode of the photodiode 155 also functions as a shield for the remaining 4 wires.
- a thumb-worn pulse oximeter probe 294 contains the red/infrared LED 150 along with a gain resistor 149 indicating the specific wavelengths of both the red and infrared radiation.
- the microprocessor in the wrist-worn transceiver determines the value of the resistor 149 by monitoring a voltage drop across it; this value, in turn, is compared to a value stored in memory to select the appropriate coefficients relating RoR to SpO2.
- the probe 294 generates alternating red and infrared radiation according to the timing diagram in FIG.
- the amplifier circuit 140 features separate channels for amplifying and filtering signals corresponding to red radiation, infrared radiation, and ambient light detected by the photodiode 155 when the LED is not biased to generate radiation. This occurs, for example, during the time periods shown in FIG. 20 when neither the red or infrared LED is driven. Once detected, the degree of ambient light can be subtracted from both the red and infrared signals to improve their resultant signal-to-noise ratio.
- the amplifier channel corresponding to red radiation is activated by a sample-and-hold integrated circuit 157 that is controlled by the same control lines 185 , 190 that drive the red LED, as shown in FIG. 20 .
- the sample-and-hold circuit 157 When the red LED is driven, the sample-and-hold circuit 157 is switched on, while similar components 164 , 172 corresponding to the infrared signals and ambient light are switched off.
- the sample-and-hold circuit 157 samples and maintains an analog voltage from the transimpedance amplifier 156 , which then passes through a low-pass filter 158 characterized by a 20 Hz cutoff. This filter removes any high-frequency noise (e.g. 60 Hz electrical noise) that is not related to the RED(PPG), and yields a preliminary waveform that is digitized with an analog-to-digital converter 176 , and processed as described above to generate a RED(DC) value.
- high-frequency noise e.g. 60 Hz electrical noise
- the preliminary waveform then passes through a high-pass filter 160 with a cutoff of 0.1 Hz to remove the DC portion and leave only the AC portion, which typically represents about 0.5-1% of the total signal magnitude.
- the AC portion is further amplified with a standard instrumentation amplifier 162 featuring a programmable gain that is controlled with a 1.65 reference voltage and a digital potentiometer (not shown in the figure; this component may be included directly in the instrumentation amplifier) featuring a variable resistance controlled by the microprocessor.
- the microprocessor selects the resistance (according to a predetermined binary command) and corresponding gain to maximize the dynamic range of the analog-to-digital converter 176 . This process results in an amplified version of the RED(AC) signal, which is then digitized with the analog-to-digital converter 176 and then processed as described above.
- the above-described filtering and amplification processes are repeated when the infrared LED and a sample-and-hold integrated circuit 164 corresponding to the infrared channel are activated with infrared I/O control lines 187 , 189 .
- the low-pass 166 and high-pass 168 filters corresponding to this channel are identical to those used for the red channel.
- the instrumentation amplifier 170 is also identical, but is controlled by a separate digital potentiometer to have a unique, uncoupled gain. This is because the IR(PPG) typically has a relatively large amplitude, and thus requires less amplification, than the RED(PPG).
- the channel corresponding to ambient light only requires processing of DC signals, and thus includes a sample-and-hold integrated circuit 172 that passes an analog voltage to a low-pass filter 174 featuring a 20 Hz cutoff.
- the filtered value corresponding to ambient light is then digitized with the analog-to-digital converter and then processed as described above.
- FIGS. 23A and 23B show how the body-worn monitor 190 described above attaches to a patient 270 .
- FIG. 23A shows the system used during the indexing portion of the Composite Technique, and includes a pneumatic, cuff-based system 285
- FIG. 23B shows the system used for subsequent SpO2 and cNIBP measurements.
- the indexing measurement typically takes about 60 seconds, and is typically performed once every 4-8 hours. Once the indexing measurement is complete the cuff-based system 285 is typically removed from the patient. The remainder of the time the system 190 performs the SpO2 and cNIBP measurements.
- the body-worn monitor 190 features a wrist-worn transceiver 272 , described in more detail in FIG. 24 , featuring a touch panel interface 273 that displays SpO2, blood pressure values and other vital signs.
- a wrist strap 290 affixes the transceiver 272 to the patient's wrist like a conventional wristwatch.
- a flexible cable 292 connects the transceiver 272 to a pulse oximeter probe 294 that wraps around the base of the patient's thumb. During a measurement, the probe 294 generates a time-dependent PPG waveform which is processed along with an ECG to measure cNIBP and SpO2. This provides an accurate representation of blood pressure in the central regions of the patient's body, as described above.
- the body-worn monitor 190 features three separate accelerometers located at different portions on the patient's arm and chest.
- the first accelerometer is surface-mounted on a circuit board in the wrist-worn transceiver 272 and measures signals associated with movement of the patient's wrist. As described above, this motion can also be indicative of that originating from the patient's fingers, which will affect the SpO2 measurement.
- the second accelerometer is included in a small bulkhead portion 296 included along the span of the cable 282 .
- a small piece of disposable tape similar in size to a conventional bandaid, affixes the bulkhead portion 296 to the patient's arm.
- the bulkhead portion 296 serves two purposes: 1) it measures a time-dependent ACC waveform from the mid-portion of the patient's arm, thereby allowing their posture and arm height to be determined as described in detail above; and 2) it secures the cable 286 to the patient's arm to increase comfort and performance of the body-worn monitor 190 , particularly when the patient is ambulatory.
- the cuff-based module 285 features a pneumatic system 276 that includes a pump, valve, pressure fittings, pressure sensor, analog-to-digital converter, microcontroller, and rechargeable Li:ion battery.
- the pneumatic system 276 inflates a disposable cuff 284 and performs two measurements according to the Composite Technique: 1) it performs an inflation-based measurement of oscillometry to determine values for SYS, DIA, and MAP; and 2) it determines a patient-specific relationship between PTT and MAP.
- the cuff 284 within the cuff-based pneumatic system 285 is typically disposable and features an internal, airtight bladder that wraps around the patient's bicep to deliver a uniform pressure field.
- pressure values are digitized by the internal analog-to-digital converter, and sent through a cable 286 according to a CAN protocol, along with SYS, DIA, and MAP blood pressures, to the wrist-worn transceiver 272 for processing as described above.
- the cuff-based module 285 is removed from the patient's arm and the cable 282 is disconnected from the wrist-worn transceiver 272 .
- cNIBP is then determined using PTT, as described in detail above.
- the body-worn monitor 190 features a small-scale, three-lead ECG circuit integrated directly into a bulkhead 274 that terminates an ECG cable 282 .
- the ECG circuit features an integrated circuit that collects electrical signals from three chest-worn ECG electrodes 278 a - c connected through cables 280 a - c .
- the ECG electrodes 278 a - c are typically disposed in a conventional ‘Einthoven's Triangle’ configuration which is a triangle-like orientation of the electrodes 278 a - c on the patient's chest that features three unique ECG vectors.
- the ECG circuit determines up to three ECG waveforms, which are digitized using an analog-to-digital converter mounted proximal to the ECG circuit, and sent through a cable 282 to the wrist-worn transceiver 272 according to the CAN protocol. There, the ECG and PPG waveforms are processed to determine the patient's blood pressure. Heart rate and respiratory rate are determined directly from the ECG waveform using known algorithms, such as those described above.
- the cable bulkhead 274 also includes an accelerometer that measures motion associated with the patient's chest as described above.
- a single transmission line in the cable 282 can transmit multiple digital waveforms, each generated by different sensors. This includes multiple ECG waveforms (corresponding, e.g., to vectors associated with three, five, and twelve-lead ECG systems) from the ECG circuit mounted in the bulkhead 274 , along with waveforms associated with the x, y, and z axes of accelerometers mounted in the bulkheads 274 , 296 . Limiting the transmission line to a single cable reduces the number of wires attached to the patient, thereby decreasing the weight and cable-related clutter of the body-worn monitor.
- cable motion induced by an ambulatory patient can change the electrical properties (e.g. electrical impendence) of its internal wires. This, in turn, can add noise to an analog signal and ultimately the vital sign calculated from it.
- a digital signal in contrast, is relatively immune to such motion-induced artifacts.
- More sophisticated ECG circuits can plug into the wrist-worn transceiver to replace the three-lead system shown in FIGS. 23A and 23B .
- These ECG circuits can include, e.g., five and twelve leads.
- FIG. 24 shows a close-up view of the wrist-worn transceiver 272 .
- the transceiver 272 houses portions of the circuits 175 , 140 described in FIGS. 20 and 21 , and additionally features a touchpanel display 200 that renders a GUI 273 which is altered depending on the viewer (typically the patient or a medical professional).
- the transceiver 272 includes a small-scale infrared barcode scanner 202 that, during use, can scan a barcode worn on a badge of a medical professional.
- the barcode indicates to the transceiver's software that, for example, a nurse or doctor is viewing the user interface.
- the GUI 273 displays vital sign data and other medical diagnostic information appropriate for medical professionals.
- the nurse or doctor can view the vital sign information, set alarm parameters, and enter information about the patient (e.g. their demographic information, medication, or medical condition).
- the nurse can press a button on the GUI 273 indicating that these operations are complete.
- the display 200 renders an interface that is more appropriate to the patient, such as time of day and battery power.
- the transceiver 272 features three CAN connectors 204 a - c on the side of its upper portion, each which supports the CAN protocol and wiring schematics, and relays digitized data to the internal CPU.
- Digital signals that pass through the CAN connectors include a header that indicates the specific signal (e.g. ECG, ACC, or pressure waveform from the cuff-based module) and the sensor from which the signal originated. This allows the CPU to easily interpret signals that arrive through the CAN connectors 204 a - c , and means that these connectors are not associated with a specific cable. Any cable connecting to the transceiver can be plugged into any connector 204 a - c . As shown in FIG.
- the first connector 204 a receives the cable 282 that transports a digitized ECG waveform determined from the ECG circuit and electrodes, and digitized ACC waveforms measured by accelerometers in the cable bulkhead 274 and the bulkhead portion 296 associated with the ECG cable 282 .
- the second CAN connector 204 b shown in FIG. 22 receives the cable 286 that connects to the pneumatic cuff-based system 285 used for the pressure-dependent indexing measurement (shown in FIG. 23A ).
- This connector 204 b receives a time-dependent pressure waveform delivered by the pneumatic system 285 to the patient's arm, along with values for SYS, DIA, and MAP values determined during the indexing measurement.
- the cable 286 unplugs from the connector 204 b once the indexing measurement is complete, and is plugged back in after approximately four hours for another indexing measurement.
- the final CAN connector 204 c can be used for an ancillary device, e.g. a glucometer, infusion pump, body-worn insulin pump, ventilator, or end-tidal CO 2 delivery system.
- an ancillary device e.g. a glucometer, infusion pump, body-worn insulin pump, ventilator, or end-tidal CO 2 delivery system.
- digital information generated by these systems will include a header that indicates their origin so that the CPU can process them accordingly.
- the transceiver 272 includes a speaker 201 that allows a medical professional to communicate with the patient using a voice over Internet protocol (VOIP).
- VOIP voice over Internet protocol
- the medical professional could query the patient from a central nursing station or mobile phone connected to a wireless, Internet-based network within the hospital.
- the medical professional could wear a separate transceiver similar to the shown in FIG. 24 , and use this as a communication device.
- the transceiver 272 worn by the patient functions much like a conventional cellular telephone or ‘walkie talkie’: it can be used for voice communications with the medical professional and can additionally relay information describing the patient's vital signs and motion.
- the speaker can also enunciate pre-programmed messages to the patient, such as those used to calibrate the chest-worn accelerometers for a posture calculation, as described above.
- the body-worn monitor can use a number of additional methods to calculate blood pressure and other properties from the optical and electrical waveforms. These are described in the following co-pending patent applications, the contents of which are incorporated herein by reference: 1) CUFFLESS BLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM (U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FOR MEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr.
- processing units and probes for measuring pulse oximetry similar to those described above can be modified and worn on other portions of the patient's body.
- pulse oximetry probes with finger-ring configurations can be worn on fingers other than the thumb.
- they can be modified to attach to other conventional sites for measuring SpO2, such as the ear, forehead, and bridge of the nose.
- the processing unit can be worn in places other than the wrist, such as around the neck (and supported, e.g., by a lanyard) or on the patient's waist (supported, e.g., by a clip that attaches to the patient's belt).
- the probe and processing unit are integrated into a single unit.
- a set of body-worn monitors can continuously monitor a group of patients, wherein each patient in the group wears a body-worn monitor similar to those described herein.
- each body-worn monitor can be augmented with a location sensor.
- the location sensor includes a wireless component and a location-processing component that receives a signal from the wireless component and processes it to determine a physical location of the patient.
- a processing component determines from the time-dependent waveforms at least one vital sign, one motion parameter, and an alarm parameter calculated from the combination of this information.
- a wireless transceiver transmits the vital sign, motion parameter, location of the patient, and alarm parameter through a wireless system.
- a remote computer system featuring a display and an interface to the wireless system receives the information and displays it on a user interface for each patient in the group.
- the interface rendered on the display at the central nursing station features a field that displays a map corresponding to an area with multiple sections.
- Each section corresponds to the location of the patient and includes, e.g., the patient's vital signs, motion parameter, and alarm parameter.
- the field can display a map corresponding to an area of a hospital (e.g. a hospital bay or emergency room), with each section corresponding to a specific bed, chair, or general location in the area.
- the display renders graphical icons corresponding to the motion and alarm parameters for each patient in the group.
- the body-worn monitor includes a graphical display that renders these parameters directly on the patient.
- the location sensor and the wireless transceiver operate on a common wireless system, e.g. a wireless system based on 802.11, 802.15.4, or cellular protocols.
- a location is determined by processing the wireless signal with one or more algorithms known in the art. These include, for example, triangulating signals received from at least three different base stations, or simply estimating a location based on signal strength and proximity to a particular base station.
- the location sensor includes a conventional global positioning system (GPS).
- the body-worn monitor can include a first voice interface
- the remote computer can include a second voice interface that integrates with the first voice interface.
- the location sensor, wireless transceiver, and first and second voice interfaces can all operate on a common wireless system, such as one of the above-described systems based on 802.11 or cellular protocols.
- the remote computer can be a monitor that is essentially identical to the monitor worn by the patient, and can be carried or worn by a medical professional.
- the monitor associated with the medical professional features a GUI wherein the user can select to display information (e.g. vital signs, location, and alarms) corresponding to a particular patient.
- This monitor can also include a voice interface so the medical professional can communicate directly with the patient.
Landscapes
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Molecular Biology (AREA)
- General Health & Medical Sciences (AREA)
- Biophysics (AREA)
- Pathology (AREA)
- Veterinary Medicine (AREA)
- Biomedical Technology (AREA)
- Heart & Thoracic Surgery (AREA)
- Medical Informatics (AREA)
- Public Health (AREA)
- Surgery (AREA)
- Animal Behavior & Ethology (AREA)
- Cardiology (AREA)
- Physiology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Optics & Photonics (AREA)
- Vascular Medicine (AREA)
- Dentistry (AREA)
- Oral & Maxillofacial Surgery (AREA)
- Signal Processing (AREA)
- Ophthalmology & Optometry (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
Abstract
The invention provides a body-worn system that continuously measures pulse oximetry and blood pressure, along with motion, posture, and activity level, from an ambulatory patient. The system features an oximetry probe that comfortably clips to the base of the patient's thumb, thereby freeing up their fingers for conventional activities in a hospital, such as reading and eating. The probe secures to the thumb and measures time-dependent signals corresponding to LEDs operating near 660 and 905 nm. Analog versions of these signals pass through a low-profile cable to a wrist-worn transceiver that encloses a processing unit. Also within the wrist-worn transceiver is an accelerometer, a wireless system that sends information through a network to a remote receiver, e.g. a computer located in a central nursing station.
Description
- This application claims the benefit of U.S. Provisional Application No. 61/218,055, filed Jun. 17, 2009, and to U.S. Provisional Application No. 61/218,057, filed Jun. 17, 2009, and to U.S. Provisional Application No. 61/218,059, filed Jun. 17, 2009, and to U.S. Provisional Application No. 61/218,060, filed Jun. 17, 2009, and to U.S. Provisional Application No. 61/218,061, filed Jun. 17, 2009, and to U.S. Provisional Application No. 61/218,062, filed Jun. 17, 2009, all of which are incorporated herein by reference in their entirety.
- Not Applicable
- 1. Field of the Invention
- The present invention relates to medical devices for monitoring vital signs, e.g., saturation of peripheral oxygen, or SpO2.
- 2. Description of the Related Art
- SpO2, sometimes referred to as the ‘fifth vital sign’, represents a patient's blood oxygen saturation. Medical professionals can detect hypoxemia, i.e. a deficiency of oxygen, by monitoring a patient's SpO2. Values between about 95-100% are considered normal; those below this indicate hypoxemia, and will typically trigger an alarm in a hospital setting. A technique called pulse oximetry measures SpO2. Technically this parameter is determined from a patient's arterial oxygen saturation, or SaO2, which is a percentage of oxygenated arterial hemoglobin present in their blood. Functional hemoglobin molecules can bind with up to four oxygen molecules to yield ‘oxygenated’ hemoglobin (HbO2). A hemoglobin molecule bound to less than four oxygen molecules is classified as ‘reduced’ hemoglobin (Hb). Conventional pulse oximeters feature algorithms that assume only HbO2 and Hb are present in the blood, and measure SpO2 from the ratio of oxygenated hemoglobin to the total amount of hemoglobin (both oxygenated and reduced) according to equation (1):
-
- HbO2 and Hb feature different absorption spectra in the visible and infrared regions, and can therefore be measured optically. Conventional pulse oximeters thus typically feature light sources (most typically light-emitting diodes, or LEDs) that radiate in the red (near 660 nm) and infrared (typically between 900-950 nm) spectral regions. A photodetector measures a portion of radiation at each wavelength that transmits through the patient's pulsating blood, but is not absorbed. At 660 nm, for example, Hb absorbs about ten times as much radiation as HbO2, whereas at 905 nm HbO2 absorbs about two times as much radiation as Hb. Detection of transmitted radiation at these wavelengths yields two time-dependent waveforms, each called a plethysmogram (PPG), that an oximeter analyzes to solve for SpO2 as defined in equation (1) above.
- Specifically, the oximeter processes PPG waveforms measured with red (RED(PPG)) and infrared (IR(PPG)) wavelengths to determine time-dependent AC signals and time-independent DC signals. The term ‘AC’ signals, as used herein, refers to a portion of a PPG waveform that varies relatively rapidly with time, e.g. the portion of the signal originating by pulsations in the patient's blood. ‘DC’ signals, in contrast, are portions of the PPG that are relatively invariant with time, e.g. the portion of the signal originating from scattering off of components such as bone, skin, and non-pulsating components of the patient's blood.
- More specifically, AC signals are measured from a heartbeat-induced pulse present in both waveforms. The pulse represents a pressure wave, launched by the heart, which propagates through the patient's vasculature and causes a time-dependent increase in volume in both arteries and capillaries. When the pressure pulse reaches vasculature irradiated by the oximeter's optical system, a temporary volumetric increase results in a relatively large optical absorption according to the Beer-Lambert Law. DC signals originate from radiation scattering from static components such as bone, skin, and relatively non-pulsatile components of both arterial and venous blood. Typically only about 0.5-1% of the total signal measured by the photodetector originates from the AC signal, with the remainder originating from the DC signal. Separation of AC and DC signals is typically done with both analog and digital filtering techniques that are well-known in the art.
- During pulse oximetry a normalized ‘r’ value is typically calculated from AC and DC signals using equation (2), below:
-
- r, which is sometimes called a ‘ratio of ratios’ (RoR), represents a ratio of Hb to HbO2. It equates an actual SpO2 value, which ranges from 0-100% O2, to an empirical relationship that resembles a non-linear equation. Above about 70% O2 this equation typically yields values that are accurate to a few percent. Measurements below this value, while not necessarily accurate, still indicate a hypoxic patient in need of medical attention.
- Pulse oximeters for measuring SpO2 were originally developed in 1972, and have evolved over the last 30 years to a point where they are commonplace in nearly all vital sign monitors for in-hospital use. Typical pulse oximeters feature a probe encased in a clothespin-shaped housing that includes both red and infrared LEDs, and a photodetector that detects radiation from the LEDs after it passes through a portion of the patient's body. The probe typically clips to a patient's index finger. Most probes operate in a transmission-mode optical geometry, and relay analog waveforms measured by LEDs and the photodetector to an external processing unit. The processing unit is typically integrated into a stand-alone monitor that measures only SpO2 and pulse rate (determined from the AC signal of one of the PPG waveforms), or a complete vital sign monitor that measures SpO2 along with systolic (SYS), mean (MAP), and diastolic (DIA) blood pressure, heart rate (HR), respiratory rate (RR), and temperature (TEMP). In both cases the oximeter probe typically connects to the monitor through a cable. Alternate configurations of SpO2 monitors include those that operate in reflection-mode optical geometries, probes that clip onto appendages other than the patient's finger (e.g. their ear or forehead), and processing units that are worn directly on the patient's body (e.g. their wrist). In some cases PPG waveforms, along with SpO2 and pulse rate values, are sent wirelessly from the oximeter to a remote display.
- Because it is based on an optical measurement, pulse oximetry can be extremely sensitive to a patient's motion. Activities such as walking, finger tapping, falling, and convulsing can result in a number of artifacts that distort both the AC and DC components of waveforms measured with the oximeter's optical system. Motion-related activities, for example, can cause the oximeter probe to move relative to the patient's finger, change the amount of ambient light that irradiates the photodetector, and disrupt both arterial and venus blood flow in vasculature measured by the optical system. Each of these events can generate artifacts that, in some cases, are similar to the AC and DC signals within the PPG waveforms. Ultimately this can cause the pulse oximeter to generate inaccurate values and false alarms.
- Oximeters suffer other problems outside of their measurement accuracy. Probes encapsulating a patient's index finger can be uncomfortable and awkward, especially when worn for extended periods of time. Pulse oximeters that lack body-worn processing units can only provide measurements when a patient is sedentary and attached to a bedside monitor; they are impractical for ambulatory patients moving about the hospital, making it difficult to provide true continuous monitoring. Most body-worn oximeters typically lack systems for continuously measuring all vital signs, and particularly blood pressure, from a patient.
- The invention described herein provides a body-worn monitor that continuously measures pulse oximetry and other vital signs, along with motion, posture, and activity level, from an ambulatory patient. The system features an oximetry probe that comfortably clips to the base of the patient's thumb, thereby freeing up their fingers for conventional activities in a hospital, such as reading and eating. The probe reversibly secures to the thumb with, e.g., an easy-to-use Velcro strap, disposable tape, or similar closure, or may be provided in the form of a closed ring which slips over the thumb. It measures time-dependent waveforms (RED/IR(PPG)) corresponding to LEDs typically operating near 660 nm and 905 nm. Analog versions of these waveforms pass through a low-profile cable to a wrist-worn transceiver enclosing a processing unit. Also within the wrist-worn transceiver is a wireless system (typically based wireless protocols such as 802.11 and 802.15.4) that sends information through a network to a remote receiver, e.g. a computer located in a central nursing station.
- Clinically accurate pulse oximetry measurements made at the base of the patient's thumb require a set of coefficients relating r (from Eq. 2) to SpO2 that are typically determined with a set of empirical experiments (e.g. a ‘breathe down’ study, described below). These coefficients differ from those used in conventional oximetry measurements because of the differences between vasculature in the base of the thumb and the tip of the index finger. Typically the base of the thumb features relatively fewer capillary beds, and thus the coefficients are preferably adjusted accordingly.
- Three motion-detecting sensors (e.g. accelerometers) form part of the body-worn monitoring system. They are typically secured to the patient's torso (e.g. chest), upper arm (e.g. bicep), and lower arm (e.g. wrist), and measure time-dependent motion signals (ACC waveforms). The wrist-worn transceiver receives and processes these motion signals to determine the patient's degree of motion, posture, and activity level. Each sensor typically measures a unique ACC waveform along three axes (x, y, and z), and ultimately yields information that can be processed to determine a separate component of the patient's motion. For example, the sensor worn on the lower arm (which may be within the wrist-worn transceiver) monitors movement of the patient's hand and fingers; such motion typically disrupts the RED/IR(PPG) waveforms. It can therefore be processed and used to exclude certain noise-corrupted artifacts from the SpO2 calculation. Sensors attached to the lower and upper arms each measure signals that are collectively analyzed to estimate the patient's arm height; this can be used to improve accuracy of a continuous blood pressure measurement (cNIBP), as described below. And the sensor attached to the patient's chest measures signals that are analyzed to determine the patient's posture and activity level, which can affect measurements for SpO2, cNIBP, and other vital signs. Algorithms for processing information from the accelerometers for these purposes are described in detail in the following patent applications, the contents of which are fully incorporated herein by reference: BODY-WORN MONITOR FEATURING ALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No. 12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009). As described therein, knowledge of a patient's motion, activity level, and posture can greatly enhance the accuracy of alarms/alerts generated by the body-worn monitor. For example, a walking patient typically yields noisy PPG waveforms, but also has a low probability of being hypoxic due to their activity state. According to the invention, a patient in this condition thus does not typically generate an alarm/alert, regardless of the value of SpO2 that is measured. Similarly, a patient that is convulsing or falling typically yields noisy RED/IR(PPG) waveforms from which it is difficult to extract an SpO2 value. But these activity states, regardless of the patient's SpO2 values, may trigger an alarm/alert because they indicate the patient needs medical assistance.
- The body-worn monitor features systems for continuously monitoring patients in a hospital environment, and as the patient ultimately transfers from the hospital to the home. Both SpO2 and cNIBP rely on accurate measurement of PPG and ACC waveforms, along with an electrocardiogram waveform (ECG), from patients that are both moving and at rest. cNIBP is typically measured with the ‘Composite Technique’, which is described in detail in the co-pending patent application entitled: VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contents of which are fully incorporated herein by reference.
- As described in these applications, the Composite Technique (or, alternatively, the ‘Hybrid Technique’ referred to therein) typically uses a single PPG waveform from the SpO2 measurement (typically the IR(PPG) waveform, as this typically has a better signal-to-noise ratio than the RED(PPG) waveform), along with the ECG waveform, to calculate a parameter called ‘pulse transit time’ (PTT) which strongly correlates to blood pressure. Specifically, the ECG waveform features a sharply peaked QRS complex that indicates depolarization of the heart's left ventricle, and, informally, provides a time-dependent marker of a heart beat. PTT is the time separating the peak of the QRS complex and the onset, or ‘foot’, of the RED/IR(PPG) waveforms; it is typically a few hundred milliseconds. The QRS complex, along with the foot of each pulse in the RED/IR(PPG), can be used to more accurately extract AC signals using a mathematical technique described in detail below. In other embodiments both the RED/IR(PPG) waveforms are collectively processed to enhance the accuracy of the cNIBP measurement.
- The electrical system for measuring SpO2 features a small-scale, low-power circuit mounted on a circuit board that fits within the wrist-worn transceiver. The transceiver can further include a touchpanel display, barcode reader, and wireless systems for ancillary applications described, for example, in the following applications, the contents of which have been previously incorporated by reference: BODY-WORN MONITOR FEATURING ALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No. 12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009).
- In one aspect, the invention provides a system and method for monitoring a physiological property of a patient's blood (e.g. a SpO2 value). The invention features a first sensor with two radiation sources that emit optical radiation at first and second wavelengths, and a photodetector configured to detect the radiation after it passes through a portion of the patient. A finger-ring housing that houses the radiation sources and the photodetector features a ring-shaped mounting portion that fits or wraps around a base of the patient's thumb. A processing unit, worn on the patient's wrist and operably connected to the finger-ring sensor, receives signals from the photodetector and includes both a motion sensor and a processor. The processor is configured to process: i) the first and second signals to determine AC signals; ii) at least one of the AC signals and the motion signal to determine selected AC signals; and iii) the selected AC signals, or signals derived therefrom, to determine the physiological property of the patient's blood.
- In certain embodiments, the mounting portion comprises a curved, ring-shaped portion that partially surrounds the base of the patient's thumb, while leaving the tip uncovered. The ring-shaped portion can connect to a flexible strap made of, e.g., nylon or fabric. Typically the first and second radiation sources are proximal to one another (and are often within the same electronic package) and are separated from the photodetector by an angle between 75-110 degrees. In these and other embodiments the processing unit includes an input port (e.g. a serial port operating a serial protocol, such a control area network, or CAN protocol) configured to receive an electrical signal (e.g. a digitized ECG signal). The ECG signal, for example, is generated by a series of body-worn electrodes connected to differential amplifier circuit. The cable that supplies the ECG signal can include this circuit, and can plug directly into the serial input port. The ECG signal includes a time-dependent marker (e.g. a QRS complex) that precedes both the first and second PPG waveforms generated by each heartbeat by less than pre-determined time period (e.g. 500 ms). It can be processed to determine heart rate, and can additionally be processed to detect both the AC and DC signals within the PPG waveforms, along with motion that may disrupt them. If motion is detected, the system can be instructed to simply ignore the AC and DC components; this is typically done if motion exceeds a pre-determined level known to corrupt these signals beyond an acceptable level. Alternatively, if motion is present but is less than the pre-determined level, its influence over the AC and DC components may be removed using frequency-domain filtering, deconvolution, or similar techniques.
- In other embodiments both cNIBP and SpO2 are simultaneously detected from both the PPG and ECG signals. cNIBP is determined, for example, from a time difference between a peak of a QRS complex in the ECG signal and an onset point in one of the AC signals. The time difference, for example, is PTT, and cNIBP is determined according to the Composite Technique. In this case, cNIBP is most accurately determined when the finger-ring sensor is worn on the base of the patient's thumb. For this configuration, parameters relating ratios of the AC and DC signals to SpO2 need to be determined beforehand using, e.g., conventional breathe down studies.
- In another aspect, the invention provides a method for simultaneously measuring both SpO2 and a motion-related event (e.g. a patient's posture, activity level, and degree of motion) from the patient. Typically posture may be measured with a single sensor (e.g. an analog or digital three-axis accelerometer) mounted on the patient's torso. The accelerometer can be mounted alongside the ECG circuit in a terminal portion of the ECG cable. In this embodiment, posture is typically determined from a vector corresponding to orientation of the patient's torso. Specifically, an angle separating the vector from a pre-determined coordinate system ultimately yields posture, as is described in detail below. Activity level (corresponding, e.g., to moving, walking, falling, convulsing) is another motion-related event determined in this embodiment. It can be calculated from a mathematical transform of time-dependent variations of a motion signal that yields a frequency-domain spectrum. Portions of the spectrum (e.g. the power of specific frequency components) are compared to pre-determined frequency-dependent parameters to determine the activity level. Other operations, such as a mathematical derivative of the time-dependent motion signal, or a series of ‘decision rules’ based on a decision-tree algorithm, can also yield the activity level.
- In another aspect, the invention provides a complete body-worn vital sign monitor for measuring all the patient's vital signs, including SpO2, cNIBP, and oscillometric blood pressure (SYS, DIA, and MAP). Typically in this embodiment the body-worn monitor features a wrist-worn processing unit that includes, for example, multiple input ports to operably connect with stand-alone systems for measuring some of the vital signs (e.g. to receive cables associated with systems for measuring ECG and oscillometric blood pressure). Additional ports may also be used to collect signals from external sensors that measure, e.g., glucose level, respiration rate, and end-tidal CO2. To simplify data collection, each port typically operates on a common communication protocol, such as the CAN protocol. Input ports corresponding to ECG and oscillometric blood pressure are typically located on a common side of the processing unit that typically faces away from the patient's hand. In this embodiment any cable connecting to an input port may include an accelerometer to characterize the patient's motion.
- In certain embodiments the processing unit features a touchpanel display that renders a first user interface that displays information describing oxygen saturation, a second user interface that displays information describing blood pressure, and a third user interface that displays information describing ECG signals. The processing unit can also include a barcode scanner that scans a barcode of a medical professional (located, e.g., on their badge). In response, the wrist-worn transceiver can render a user interface corresponding to the medical professional. This prevents the patient from viewing medical information that may, for example, cause unnecessary alarm.
- In other embodiments the processing unit includes a speaker for voice communications, or for generating audible voice messages intended for the patient. The processing unit can also include a wireless transmitter that communicates through, e.g., a hospital network.
- In another aspect, the invention provides a method for measuring SpO2 and cNIBP by processing ECG, PPG, and motion signals with filters that analyze both PTT and AC signals of the PPG waveforms with a mathematical filter. The filter, for example, can be a ‘matched filter’, described in detail below. Typically only signals that are generated when motion is relatively low are considered in this embodiment. For example, signals are typically not processed further when the motion sensors indicate that motion is greater than an acceptable level. Filtering PTT values includes, for example, determining values that lie outside a pre-determined range using statistical filters (e.g. a simple average and standard deviation). More sophisticated techniques, such as calculating a power value from a frequency-domain spectrum corresponding to the time-dependent motion signal, and then comparing this to a pre-determined value, can be used to estimate if either the PTT values or PPG signals are affected by motion. SpO2 values are typically calculated from ratios describing the AC and DC signals measured from individual pulses using optical systems operating in the red and infrared optical spectral regions. One or more ratios can be calculated for the pulse.
- In another aspect, the invention provides a method for suppressing an alarm/alert based on the SpO2 value by processing the patient's posture and activity state. For example, the alarm can be suppressed if the patient's posture is upright (e.g. standing up), as patients having this posture are typically not in immediate need of medical assistance. Similarly, the alarm can be suppressed if the patient's posture changes from lying down to sitting or standing up (or, alternatively, the other way around). In this case the change in posture, which can be determined with the chest-worn accelerometer, may disrupt the PPG waveforms to the point where an alarm/alert would be falsely generated in the absence of such alarm suppression.
- Still other embodiments are found in the following detailed description of the invention, and in the claims.
-
FIG. 1 shows a schematic drawing of a pulse oximeter probe configured as a finger-ring sensor worn around the base of a patient's thumb; -
FIG. 2 shows an expanded, schematic drawing of the pulse oximeter probe ofFIG. 1 with a dual-wavelength LED and photodetector separated from the finger-ring sensor; -
FIG. 3A is a schematic drawing of a patient's thumb with the pulse oximeter probe ofFIG. 2 attached; -
FIG. 3B shows a cross-sectional, schematic drawing of a patient's thumb measured with the pulse oximeter probe ofFIG. 3A ; -
FIG. 4 is a graph showing a relationship between SpO2 and a ‘ratio of ratios’ (RoR) for measurements from theoretical model in a medical textbook, made from the tip of a patient's index finger, and made from the base of a patient's thumb; -
FIG. 5 is a graph comparing measurements from 20 unique patients made with the thumb-worn pulse oximeter probe ofFIG. 1 to those made at the tip of the index finger with a commercial oximeter probe; -
FIG. 6 is a graph showing time-dependent waveforms measured with an accelerometer (ACC), ECG system (ECG), and the 660 and 905 nm LEDs (RED(PPG) and IR(PPG), respectively) within the pulse oximeter probe ofFIG. 1 ; -
FIG. 7 is a flow chart describing an algorithm for measuring SpO2 and patient motion with the pulse oximeter probe ofFIG. 1 ; -
FIG. 8 is a graph showing data collected during a breathe down study indicating correlation between SpO2 measured with the pulse oximeter probe shown inFIG. 2 and SpO2 measured with a blood gas analyzer, which in this case represents a ‘gold standard’; -
FIGS. 9A and 9B are graphs, respectively, of time-dependent IR(PPG), ECG, and ACC waveforms measured when a patient is not undergoing motion; -
FIGS. 10A and 10B are graphs, respectively, of time-dependent IR(PPG), ECG, and ACC waveforms measured when a patient is undergoing minor finger motion; -
FIGS. 11A and 11B are graphs, respectively, of time-dependent IR(PPG), ECG, and ACC waveforms measured when a patient is undergoing major finger motion; -
FIG. 12A is a graph of time-dependent RED/IR(PPG), ECG, and ACC waveforms similar to those shown in FIGS. 9A,B-11A,B measured during periods of motion and no motion; -
FIGS. 12B and 12D are graphs of the IR(PPG) waveforms ofFIG. 12A measured, respectively, during periods of motion and no motion, along with a graphical indication of how multiple RoRs are calculated from the waveforms; -
FIG. 12C is a graph of data points representing RoRs between components of the RED(PPG) and IR(PPG) waveforms measured during periods of no motion and at periodic intervals between the foot and peak of the waveform shown inFIG. 12B ; -
FIGS. 12E and 12F are graphs of data points representing RoRs between components of the RED(PPG) and IR(PPG) waveforms measured during periods of motion at periodic intervals between the foot and peak of the waveform shown inFIG. 12D ; -
FIG. 13A shows a graph of time-dependent ECG and IR(PPG) waveforms that are processed to determine a PPT; -
FIGS. 13B and 13C are graphs, respectively, of PTT versus heartbeat before and after applying a matched filter to the IR(PPG) waveform used to calculate PTT as shown inFIG. 13A ; -
FIG. 13D is a flow chart showing an algorithm for analyzing PTT and applying a matched filter to an IR(PPG) waveform when motion-related noise is present in the waveform; -
FIG. 14 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a resting patient by, respectively, the ECG system, the optical system, and the accelerometer system ofFIG. 1 ; -
FIG. 15 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a walking patient by, respectively, the ECG system, the optical system, and the accelerometer system ofFIG. 1 ; -
FIG. 16 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a convulsing patient by, respectively, the ECG system, the optical system, and the accelerometer system ofFIG. 1 ; -
FIG. 17 shows a graph of time-dependent waveforms (ECG, PPG, and ACC) generated from a falling patient by, respectively, the ECG system, the optical system, and the accelerometer system ofFIG. 1 ; -
FIG. 18 is a schematic drawing of a coordinate system assigned to a patient wearing three accelerometers and a pulse oximeter probe; -
FIG. 19A is a graph showing time-dependent ACC waveforms corresponding to different posture states and measured with an accelerometer positioned on a patient's chest; -
FIG. 19B is a graph showing posture states calculated using the time-dependent ACC waveforms ofFIG. 19A and a mathematical model for determining a patient's posture; -
FIG. 20 is an electrical timing diagram showing how the 660 nm and 905 nm LEDs are driven in the pulse oximeter probe described inFIG. 1 ; -
FIG. 21 is an electrical diagram of a circuit used to drive the 660 nm and 905 nm LEDs according to the timing diagram shown inFIG. 20 ; -
FIG. 22 is an electrical diagram of a circuit used to amplify and filter the RED/IR(PPG) waveforms to generate the RED/IR(AC) and RED/IR(DC) components used to calculate SpO2; -
FIGS. 23A and 23B show images of the body-worn monitor ofFIG. 1 attached to a patient with and without, respectively, a cuff-based pneumatic system used for calibrating the cNIBP measurement; and -
FIG. 24 shows an image of the wrist-worn transceiver featured in the body-worn monitor ofFIGS. 23A and 23B . -
FIGS. 1 and 2 show apulse oximeter probe 1 shaped as a finger ring that wraps around a base of patient'sthumb 3 to measure SpO2 and cNIBP. Theprobe 1 is designed to be comfortably worn for extended periods (e.g. several days) while freeing up the patient's thumb and hands for activities such as reading and eating that are commonplace in, e.g., a hospital. Motion corresponding to these and other activities can affect the SpO2 measurement and is detected with a network of accelerometers worn on the patient's body. Theprobe 1 makes a transmission-mode optical measurement along an inner portion of thethumb 3 with a pair of embeddedLEDs single photodetector 12 that detects these wavelengths after they pass through vasculature and other tissue lying beneath theLEDs LEDs photodetector 12 are positioned to measure blood pulsing in portions of the princeps pollicis artery, which is the principal artery of the thumb and stems from the radial artery. As described in detail below, measuring blood flowing in this artery enhances the accuracy of the cNIBP measurement. Asmall circuit board 11 supports thephotodetector 12 and may additionally include, for example, a trans-impedance amplifier for amplifying photocurrent from thephotodetector 12 and converting it into a corresponding voltage. Thecircuit board 12 also includes aresistor 19 that identifies specific wavelengths emitted by theLEDs FIGS. 21 and 22 . - A ring-shaped, flexible
plastic housing 6 formed into a cylindrical radius of curvature featuresrectangular openings LEDs circuit board 11. Thehousing 6 features three cut-outportions 23A-C, or ‘living hinges’, that make it easily bendable and able to accommodate thumbs of difference sizes. It is held in place around the base of the thumb with aflexible nylon strap 2 threading through two D-ring openings portion 4 of thestrap 2 features apatch 17 of Velcro (containing, e.g., ‘hooks’) that adheres to a mated patch 16 (containing, e.g., ‘loops’) on the strap's main portion; thepatches housing 6 is worn on the patient'sthumb 3, and easily detach so that it can be removed. Thestraps probe 1 to be securely fastened, which in turn minimizes motion relative to the measurement site. A flexible,cable 5 connects theoximeter probe 1 to a wrist-worn transceiver, similar to the one shown inFIG. 24 . Thecable 5 carries I/O signals from the wrist-worn transceiver that drive the LEDs according to the timing diagram inFIG. 20 , and analog signals measured by the photodetector to an amplifying/filtering circuit within the transceiver, as shown inFIG. 22 . There, the analog signals are amplified, filtered, digitized, and processed to measure SpO2, as described in detail below. - As shown in
FIGS. 3A and B, during a pulse oximetry measurement theLEDs beams FIG. 20 . Once emitted, thebeams thumb 3 and rapidly diverge to scatter off tissue such asskin 24,bone 25,capillaries 26 near the thumb's outer surface, and a portion of the princeps pollicisartery 27 before reaching thephotodetector 12. To increase the amount of radiation that passes through theartery 27 andcapillaries 26, and thereby optimize signal quality, theLEDs photodiode 12 are separated by approximately 35-55 degrees. Optical components separated at this angle tend to increase the relative contribution of signal coming from theartery 27; ultimately this improves the accuracy of the cNIBP measurement, as PTT values measured from arterial components correlate better to blood pressure than those measured from capillary components. Both thecapillaries 26 and princeps pollicisartery 27 carry blood that pulsates with each heartbeat and absorbs radiation emitted by theLEDs FIG. 6 , generated by the 660 and 905 nm radiation. Both waveforms feature AC signals corresponding to the time-dependent pulsating blood, and DC signals corresponding to time-independent scattering off theskin 24,bone 25, and non-pulsating components of thecapillaries 26 andartery 27. Prior to any filtering the AC component typically represents about 0.5-1% of the total signal. - Collectively processing both the AC and DC signals of the RED/IR(PPG) waveforms yields a SpO2 value. The body-worn monitor calculates these components using a number of signal-processing methodologies that are additionally important for determining PTT-based cNIBP. Ultimately the AC and DC components yield a RoR which then relates to a SpO2 using a series of empirically determined coefficients. In one embodiment, for example, the RoR is determined by first measuring RED/IR(PPG) waveforms, and then passing them through a low-pass filter characterized by a 20 Hz cutoff. The averaged baseline components of each waveform are sampled and stored in memory, and represent RED/IR(DC). Both waveforms are then additionally filtered with high-pass filter having a 0.1 Hz cutoff frequency, which is typically implemented with a finite impulse response function, and finally amplified with a variable gain amplifier. These steps can be done with either analog electronic or digital software filters. Components passing through this filter are isolated as described below with reference to
FIGS. 6 and 7 to yield RED/IR(AC). Once determined, the AC and DC signals are processed to yield a RoR value, described in equation (3), which relates to SpO2: -
-
FIG. 4 shows an empirical relationship between RoR and SpO2 for measurements made at the base of the thumb with the oximeter probe shown inFIGS. 1 and 2 (solid line), along with similar relationships for measurements made at the tip of the index finger with an off-the-shelf oximeter probe (small dashes), and the theoretical curve for measurements made from the tip of the index finger (large dashes). Curves corresponding to measurements made from the index finger and thumb are determined empirically from a group of patients measured under similar conditions. As is clear from the figure, the relationships between RoR and SpO2 are similar, but slightly offset due to differences in the measurement site. Without being bound to any theory, these differences may be due to the relatively low density of capillary beds near the base of the thumb as compared to those in the tip of the index finger. The relationship for all curves inFIG. 4 is non-linear, particularly for SpO2 values ranging from about 70-100%. Values below 70% can be accounted for with a different non-linear model, such as one based on a second-order polynomial. Coefficients a, b, and c for this model are determined by fitting the empirical data to a corresponding mathematical function like the second-order polynomial shown in equation (4) below: -
SpO2=(a+b*RoR+c*RoR2)×100 (4) - Optimized values for a, b, and c coefficients corresponding to measurements made at the base of the thumb are shown in Table 1, below:
-
TABLE 1 coefficients for equation 4 relating RoR to SpO2 formeasurements made at the base of the thumb Parameter Value a 107.3 b −3.0 c −20.0 - The exact values of parameters shown in Table 1 will depend of the specific wavelengths of the LEDs used in the pulse oximeter probe. This is because the SpO2 measurement is fundamentally determined by the relative optical absorption of Hb and HbO2 in the red and infrared spectral regions, and absorption, in turn, will depend on the wavelength emitted by the LEDs. The absorption spectra of Hb and HbO2 are relatively flat in the infrared spectral region, but strongly diverge in the red spectral region. The coefficients shown in Table 1 are thus relatively sensitive to the exact wavelength of the red LED. For this reason, a series of empirical studies need to be performed using pulse oximeter probes featuring LEDs of varying wavelengths surrounding the red emission wavelength (e.g. 600-610 nm) prior to manufacturing. Such a study, for example, is described with reference to
FIG. 8 . It is typically classified as a ‘breathe down’ study because it involves lowering the SpO2 values of a series of patients (typically about 10-15) under medical supervision. SpO2 is typically lowered by decreasing the amount of oxygen each patient inhales through a specialized ventilator mask; this is often done in a room with a reduced temperature. Blood from the patients is extracted from an arterial line and analyzed with a blood gas analyzer to determine its oxygen content. Simultaneously, a pulse oximeter probe with known LED wavelengths is attached to each patient (in this case at the base of the thumb) and used to measure the RoR described in equation (3). SpO2 values for this experiment, as measured with the blood gas analyzer, typically range from 70-100%. Simultaneous studies are typically done using pulse oximeter probes having LEDs with different red emission spectra. Upon completion of the studies, the wavelength-dependent values of RoR are related to SpO2, as determined by the blood gas analyzer, to calculate coefficients a, b, c as described in Table 1. In general, a different set of coefficients will result for the different LED wavelengths. These coefficients and the optical wavelengths they correspond to, along with a resistor value described below, are stored in a database in memory on the wrist-worn transceiver. - Prior to manufacturing of the pulse oximeter probe (e.g. the probe shown in
FIG. 1 ), the wavelengths of the LEDs are determined, typically with a resolution of about 1 nm, using an emission spectrophotometer. LEDs are then sorted by wavelength and associated with a resistor having a value that is stored in the above-described database. During manufacturing, the resistor is soldered to the circuit board (and optionally trimmed with a laser to give it a well-defined resistance) within the pulse oximeter probe shown inFIG. 1 . During an actual measurement, the wrist-worn transceiver delivers a voltage through the cable connecting it to the pulse oximeter probe that decreases in value after passing through the resistor. This voltage drop is sensed by the transceiver using the analog-to-digital converter and processor, and then used to calculate the resistor value. The value is compared to the database, and ultimately used to select the appropriate a, b, and c coefficients described above. Ultimately this maximizes accuracy of the SpO2 calculation. - Measurements made at the base of the thumb provide accurate SpO2 values and increase patient comfort. Additionally, the IR(PPG) measured from this site, when processed in combination to the ECG waveform, yields a PTT value that can be processed with the Composite Technique to yield an accurate cNIBP measurement. As described above, an IR(PPG) waveform measured from primarily from the princeps pollicis artery increases the accuracy of the cNIBP measurement. With an initial pressure-based calibration (performed e.g. by the
pneumatic system 285 shown inFIG. 23A ) SYS and DIA can be explicitly determined for each heartbeat using an algorithm described in the above-mentioned patent applications, the contents of which have been previously incorporated herein by reference. Typically, PTT values are processed over a 20-40 second time period (often implemented as a ‘rolling average’) using statistical filtering to improve accuracy. To better define the onset of the PPG waveform, and thus improve the accuracy to which SYS and DIA are determined, times corresponding to RED(foot) and IR(foot) are typically averaged together. When compared to SYS and DIA values measured under clinical conditions with a femoral arterial line, cNIBP measurements made from this particular location were well within the FDA's standards for accuracy (±5 mmHg) and standard deviation (8 mmHg). For this and other reasons the base of the thumb appears to be a uniquely good location for measuring both SpO2 and cNIBP. -
FIG. 5 shows a direct comparison between SpO2 measured from the base of the thumb and tip of the index finger from a group of 20 separate patients. Each patient was measured for a 30-second period from the tip of finger with a commercially available oximeter probe, and then for a comparable period from the base of the thumb with an oximeter probe substantially similar to that shown inFIGS. 1 and 2 . During the measurement an average value for SpO2 was detected from each location. For these patients the relationships between RoR and SpO2 shown inFIG. 4 were used for both sets of measurements. As is clear from the data, the correlation for these measurements is within experimental error (estimated at 1% SpO2 for each measurement) for all 20 patients. The mean difference between the two measurements (thumb SpO2-index finger SpO2) is −0.6% O2, and the standard deviation of the differences is 1.39% O2. Measurements were made over a range of 93-100% O2. - The pulse oximeter system described above is integrated into a complete, body-worn monitoring system, shown in
FIGS. 23A and 23B , that measures and analyzes ECG, ACC, RED(PPG) and IR(PPG) waveforms to characterize the patient's vital signs, degree of motion, activity level, and posture.FIG. 6 , for example, shows the various time-dependent waveforms measured concurrently over a 3-second period using such a system. The ACC waveform represents a measurement along a single axis made by one of the three accelerometers incorporated in the body-worn monitor. Typically this is the accelerometer included in the wrist-worn transceiver, as this site is closest to the measurement site for SpO2. SpO2 values are determined by measuring the red and infrared waveforms between the peak and foot values, indicated by colored triangles in the figure and described in more detail below. Typically these peak values are determined by filtering each waveform as described above, and then taking a first derivative and evaluating a zero-point crossing, indicating the point where the waveform's slope changes from a positive to negative value. Values for these parameters can be averaged over several pulses and processed with statistical techniques (e.g. averaging after excluding values outside of ±1 standard deviation) to yield the RED/IR(peak) values for individual pulses within the RED/IR(PPG) waveforms. RED/IR(foot) values for each pulse are used for PTT-based cNIBP measurements, and are typically processed with statistical techniques as described above. The foot of each waveform is best calculated by measuring a peak from the second derivative of the RED/IR(PPG) waveforms. RED/IR(DC) values (not shown in the figure) are typically determined by sampling RED/IR(PPG) waveforms after passing analog versions of these waveforms through a low-pass filter as described above. - Electrodes adhered to the patient's chest and connected to an ECG circuit in the body-worn monitor measure a three-lead ECG, with
FIG. 6 showing a time-dependent waveform taken from Lead II. The ECG waveform features a series of QRS complexes, indicated by the black triangles, with each QRS complex corresponding to a single heartbeat. The QRS complex typically proceeds each pulse in the RED/IR(PPG) by between about 100-200 milliseconds, and is easy to detect because of its sharp, well-defined features. Moreover, as described in more detail below with reference to FIGS. 9A,B-11A,B, the ECG waveform and its associated QRS complexes are relatively insensitive to motion, in contrast the both the RED/IR(PPG) waveforms. This means each QRS complex can serve as a marker or ‘fiducial’ for detecting AC signals in the two PPGs. Specifically, during a measurement an algorithm operating on the wrist-worn transceiver detects a QRS complex (e.g. ECG QRS-N) and then sequentially analyzes both the RED/IR(PPG) waveforms at times within 500 ms of this feature. If RED/IR(foot) and RED/IR(peak) cannot be determined during this time interval using the first and second derivative tests described above, the algorithm determines that they are immeasurable (most likely because of noise in the PPG), and begins searching for similar values corresponding to the next heartbeat. If the ACC waveform indicates a high degree of motion, then the RED/IR(PPG) waveforms are processed as described below to determine if foot and peak parameters can be extracted from them. If not, the waveforms are determined to be immeasurable, the first and second derivative tests are not performed, and the ECG and PPG waveforms corresponding to the N+1 heartbeat are investigated. This process is repeated, typically for a total time of between 5-10 seconds, for as many heartbeats as possible. - If no more than an acceptable level of motion is present, as indicated by the ACC waveform, then the values for RED/IR(foot) and RED/IR(peak) can be processed to determine an RoR for each heartbeat. This value can then be analyzed with the statistical techniques described above to limit artifacts and ultimately generate a SpO2 value with the greatest possible accuracy. In one embodiment, to convert the RED/IR(peak) and RED/IR(foot) values into AC values used for the RoR calculation, the amplitude of each pulse in the RED/IR(PPG) waveforms is calculated as shown in equations (5) and (6) below:
-
RED(AMP)=RED(peak)−RED(foot) (5) -
IR(AMP)=IR(peak)−IR(foot) (6) - The DC values for the RED/IR(PPG) waveforms are then determined by first calculating any DC offset values (RED/IR(DC)) supplied to the differential inputs of the analog-to-digital converter; these inputs are indicated, for example, by RED/IR(DC) labels in
FIG. 22 . Once digitized, these values can then be used to calculate an ‘effective’ DC value, RED/IR(DC*), for both the red and infrared wavelengths, as described below in equations (7) and (8). Ambient light, defined as AMBIENT(DC), is accounted for by measuring any radiation incident on the photodetector when neither the red or infrared LEDs are turned on (this occurs, for example, between current pulses that drive the respective LEDs, as shown inFIG. 20 ). -
- An RoR value is then determined from equations (5)-(8):
-
- The ACC waveform in
FIG. 6 provides an accurate indication of the patient's motion. Typically this waveform, along with two others corresponding to additional axes of a coordinate system, is sensed with a solid-state device called an accelerometer. An accelerometer is typically a micro electrical-mechanical system (MEMS) device that measures the acceleration of the part of the body that it is attached to. The ACC waveform measured by the accelerometer features DC values that indicate the position of the accelerometer relative to a gravity vector, and AC values that indicate movement-induced acceleration. Suitable accelerometers typically have a response times less than about 1 microsecond, and are thus more than adequate for detecting most types of patient motion, which typically occurs in less than 15 Hz. Processing the ACC waveforms yields at least three valuable pieces of information relevant to the SpO2 measurement: it can determine i) a patient's posture; ii) their activity state (e.g. are they lying down, walking, sitting, standing); and iii) whether or not the patient's hand in moving, thereby indicating that the IR/RED(PPG) waveforms may be corrupted by noise, and thus more likely to yield erroneous values for SpO2. Additional processing of the ACC waveforms yields the patient's arm height, from which hydrostatic changes in blood pressure can be estimated and used to calibrate the cNIBP measurement. The process for detecting each of these scenarios is summarized below, and in detail in the following patent applications, the contents of which have been fully incorporated herein by reference: BODY-WORN MONITOR FEATURING ALARM SYSTEM THAT PROCESSES A PATIENT'S MOTION AND VITAL SIGNS (U.S. Ser. No. 12/469,182; filed May 20, 2009) and BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009). -
FIG. 7 shows a flow chart describing analgorithm 33 that collectively determines SpO2, SYS, and DIA, along with a patient's motion, posture, and activity level, by processing the time-dependent RED/IR(PPG), ECG, and ACC waveforms shown inFIG. 6 . Thealgorithm 33 begins with measuring the ECG, RED/IR(PPG), and ACC waveforms (nine total waveforms measured with three separate accelerometers) using a body-worn monitor, similar to that shown inFIGS. 23A , 23B (step 35). RED/IR(PPG) waveforms are measured with the pulse oximeter probe described above, and the ECG waveform is measured with an ECG circuit that terminates an ECG cable and attaches to at least three electrodes worn on the patient's chest. Three accelerometers are typically disposed, respectively, in the wrist-worn transceiver, in a cable that attaches to the patient's bicep, and on the chest proximal to the ECG circuit. A series of analog filters (performed with hardware components described with reference toFIG. 22 ) and digital filters (performed in software using well-known frequency-domain techniques, such as using a ‘matched filter’, described below) process each waveform to remove unwanted and relatively high-frequency electrical and mechanical noise (step 36). Such filtering, for example, increases the accuracy to which data points corresponding to IR/RED(peak) and IR/RED(foot) can be determined. Typically RED/IR(DC) values are determined directly from waveforms that pass through a low-pass filter characterized by a 20 hz cutoff frequency, as described in equations (7) and (8), above (step 47). After filtering, the ECG QRS for each heartbeat is detected with a beat-picking algorithm described, for example, in the following reference, the contents of which are incorporated herein by reference (step 37): ‘ECG Beat Detection Using Filter Banks’, Afonso et al., IEEE Trans. Biomed Eng., 46:192-202 (1999). Heart rate is typically determined from the inverse of time separating neighboring QRS complexes. Degree of motion, posture, and activity level are then determined from the nine ACC waveforms, as described in detail in the above-referenced patent applications, and briefly below (step 38). Each of these parameters features its own unique classification. For example, degree of motion can be ranked numerically depending on the magnitude of acceleration detected from one or more accelerometers. Posture is typically divided into well-known categories such as standing, lying on back, lying prone, etc. Activity levels include activities such as walking, falling, resting, and convulsing. - If the algorithm determines that no significant motion is present (step 39), it proceeds to process the RED/IR(PPG) waveforms, starting at a time corresponding to the ECG QRS generated by a first heartbeat, and continuing the processing until a pre-determined time delta (e.g. 500 ms) or a time corresponding to a neighboring heart beat is reached (step 41). As used herein, ‘significant motion’ refers to an amount of motion that would render the RED/IR(PPG) waveforms unreliable for calculation of SpO2. This processing typically involves further digitally filtering the waveforms to remove any high-frequency noise, and then determining IR/PPG(peak) values from the first derivative of each waveform, and IR/PPG(foot) values from the second derivative of each waveform (step 42). If during
step 39 motion is determined to be present, the algorithm proceeds to analyze both RED/IR(PPG) waveforms to determine if they are distorted in any way (step 40). Such analysis, for example, can involve complex methods such as comparing a pulse from one or both of the PPG waveforms to a ‘known good pulse’ collected during a period of relatively low motion. In this case, the comparative method can be a linear or non-linear numerical fitting algorithm, such as a least-squares or Levenburg-Marquardt algorithm, or based on a standard correlation algorithm that determines agreement between the immediate pulse and the known good pulse. This latter approach can be implemented as a ‘matched filter’, and is described in detail below with regard to equations (13) and (14). A matched filter algorithm, for example, is preferably implemented instep 36 to improve the signal-to-noise ratio of both the PPG and, to a lesser extent, the ECG waveform prior to processing these signals. Or it could be implemented duringstep 40 to determine a degree of correlation between the immediate pulse in the PPG waveform and a known good pulse to determine if the immediate pulse is corrupted by motion. A derivation of the matched filter is provided, for example, in Turin, ‘An introduction to matched filters’, IRE Transactions of Information Theory 6: 311-329 (1960), the contents of which are incorporated herein by reference. - A relatively simple method for determining a known good pulse involves, for example, determining the standard deviation of the PPG waveform during a time period commensurate with the ECG QRS (at this point the pulse is not evident in the PPG waveform), and comparing this to a pre-determined metric to estimate the motion-induced noise in the waveform. If either PPG is determined to be significantly distorted by motion, it is not included in the algorithm, and the processes of collecting and analyzing RED/IR(PPG), ECG, and ACC waveforms is repeated (steps 35-38, 47).
- As described above, values for RED/IR(foot) and RED/IR(peak) can be used to calculate both SpO2 and cNIBP. For the cNIBP calculation, RED/IR(PTT) values are determined from the time difference separating the ECG QRS and RED/IR(foot). Values corresponding to RED/IR(AC) are determined from the waveforms between the RED/IR(foot) and RED/IR(peak) values, as described above in equation (9), and below in equations (10)-(11). This yields a RoR for each heartbeat-induced pulse in the waveforms. Both RED/IR(AC) and RED/IR(PTT) values are typically determined for each heartbeat measured over a pre-determined period ranging from about 10-30 seconds, and then subjected to a series of statistical tests (step 44) that typically involve taking the average and standard deviation of each value over the time period. A ‘rolling average’ can also be used during
step 44 so that fresh values are determined, e.g., every second. Values that lie outside of one standard deviation from the average are typically removed, and then the average is recalculated. The final average value of PTT is then determined as the average of the averaged RED/IR(PTT) values, while the final average value of RED/IR(AC) is determined in a similar manner from the RoR values determined for each pulse (step 45). The algorithm calculates cNIBP values of SYS and DIA directly from the averaged PTT value, as described in detail in the above-referenced patent applications (step 46). Using equation (4), it calculates SpO2 from the RoR values determined duringstep 45. - To accurately generate alarms/alerts when continuously monitoring a patient, it is often necessary to consider both the patient's vital signs and their motion. Thus, during
step 50, an alarm/alert is only generated from SYS, DIA, and SpO2 values after processing the patient's degree of motion, posture, and activity level (determined during step 38). For example, if the patient is determined to be walking with a normal gate, it can be assumed that their values of SYS, DIA, and SpO2 do not warrant an alarm/alert, even if one or all of these parameters exceeds a pre-determined alarm threshold. Conversely, an alarm/alert for a falling or convulsing patient would likely be generated even if the values for SYS, DIA, and SpO2 fall within the pre-determined alarm thresholds. Specific methodologies for alarms/alerts that consider both vital signs and patient motion are found in the above-referenced patent applications, the contents of which have been previously incorporated by reference. - As shown in
FIG. 8 , SpO2 data collected with the pulse oximeter probe shown inFIG. 2 and processed with the algorithm shown inFIG. 7 correlate well with that analyzed with a blood gas analyzer, which in this case represents a ‘gold standard’. Data shown in the figure were collected during a conventional breathe down study, wherein SpO2 values of 15 healthy volunteers were systematically lowered from a normal value near 100% O2 to an abnormal value near about 70% O2 by carefully controlling the subjects' oxygen intake. In total, about 20 data points were measured for each subject over this range. Blood samples for the blood gas analyzer were extracted using an in-dwelling catheter, similar to a conventional arterial line, inserted in the subjects' radial artery. Data in the graph measured according to the invention described herein (shown along the y-axis) correlate well with the gold standard (x-axis), yielding an r̂2 value of 0.9. The BIAS for this correlation is −0.3% O2, and the standard deviation is 2.56% O2. Data were collected and analyzed with a prototype system, and indicate the efficacy of the invention described herein. They are expected to further improve with a production-quality system. As is clear from the graph, correlation for relatively low SpO2 values (e.g. those near 70% O2) is worse than that for relatively high SpO2 values (e.g. those near 95%); such a measurement response is typical for commercially available pulse oximeters, and is primarily due to a decreasing signal-to-noise ration in the RED(PPG), which decreases with SpO2. - FIGS. 9A,B-11A,B indicate how different degrees of motion from a patient's fingers can influence both ECG and PPG signals, thereby affecting the accuracy of both SpO2 and cNIBP measurements. In these figures the IR(PPG) is shown, as this signal typically has a better signal-to-noise ratio than that of the RED(PPG). The ACC waveform is typically measured along the vertical axis of the accelerometer embedded in the wrist-worn transceiver. The magnitude of the axes for ECG, PPG, and ACC waveforms are the same for all figures.
- In
FIG. 9B , for example, the ACC waveform is relatively flat and lacking any significant time-dependent features, indicating that the patient is not moving and is relatively still. Consequently the IR(PPG) inFIG. 9A , which is strongly affected by motion, features well-defined values for IR(foot), indicated bymarker 53, and IR(peak), indicated by marker 54. Likewise the ECG waveform features a QRS complex, indicated bymarker 52, which is undistorted. The fidelity of these features indicate that both SpO2 and cNIBP values can typically be accurately determined during periods of little or no motion, as indicated by the ACC waveform inFIG. 9B . - FIGS. 10A,B show how minor amounts of finger motion affect both the ECG and IR(PPG) waveforms. As shown by a portion of the dashed
box 60 inFIG. 10B , finger motion is manifested by a time-dependent change in the ACC waveform, the beginning of which is indicated bymarker 62, which persists for a little less than one second. This is in stark contrast to the portion of the ACC waveform in the preceding dashedbox 56, which indicates no motion. Finger motion has basically no effect on the ECG waveform and its associated QRS complex, as indicated bymarker 61. But the motion causes a minor amount of distortion of the IR(PPG) waveform. Specifically, when the motion is maximized (i.e. at the time corresponding to marker 62) a small bump indicated bymarker 57 appears in the IR(PPG) waveform. This bump is an artifact that, in the absence of the ACC waveform, could be misinterpreted as a pulse containing both a foot and peak in the IR(PPG) waveform. Additionally, the minor finger motion distorts the foot (marker 58) and peak (marker 59) of the subsequent pulse in the IR(PPG) waveform, making it difficult to accurately determine these parameters using the derivative tests described above. Thus, rather than erroneously interpreting these features and generating inaccurate values for both SpO2 and cNIBP, an algorithm described herein can disregard them based on the magnitude of the ACC waveform, and continue its calculation of vital signs once the finger motion is reduced to an acceptable level. - FIGS. 11A,B show the affects of a major amount of finger motion on both the ECG and IR(PPG) waveforms. Here, the period of motion is indicated in both figures by the dashed
box 65, which contrasts with the preceding period where motion is not present, shown in the dashedbox 66. The ACC waveform inFIG. 11B indicates the finger motion lasts for roughly one second, beginning and ending at times indicated, respectively, nearmarkers marker 69. Even for major finger motion the ECG waveform and its QRS complex, indicated bymarker 67, are relatively undistorted. But the IR(PPG) measured during the period of motion is strongly distorted to the point that its peak value, indicated bymarker 64, is relatively flat and basically immeasurable. This makes it difficult to accurately measure IR(AC) and the subsequent SpO2 value calculated from this parameter. The IR(foot) value, indicated bymarker 63, is also distorted, but to a lesser degree than the corresponding peak value. - Data shown in FIGS. 9A,B-11A,B indicate that motion can be detected and accounted for during pulse oximetry measurements to minimize the occurrence of false alarms and, additionally, make accurate readings in the presence of motion. For example, Equation (9), above, yields a single RoR value for each pulse in the RED/IR(PPG) waveforms. However the method for calculating SpO2 based on a single value is limited, as only one RoR can be calculated for each heartbeat; if values from several heartbeats are averaged together it can thus take several seconds to update the SpO2 value. And the single RoR value can be strongly influenced by motion during pulses within the RED/IR(PPG) waveforms, as described above with reference to
FIGS. 9A , B-11A, B. - Alternatively, RoR can be calculated using a method indicated schematically in
FIGS. 12A-F . In this method, multiple ‘sub-ratios’ between AC components corresponding to RED/IR(PPGs) are calculated from the foot of each pulse to its corresponding peak, determined as described above. The sub-ratios are calculated for each time interval of α (typically 33 ms, corresponding to 30 Hz) and only during periods of no motion, as determined from the ACC waveforms. Once a group of sub-ratios are determined for a given pulse, they can be processed with a variety of statistical techniques, described in detail below, to estimate an accurate RoR for a given pulse. This RoR can then be further processed for multiple pulses. A pulse oximetry measurement based on sub-ratios has the advantage of being relatively accurate and having a faster update rate when compared to the method associated with equation (9), which calculates just a single RoR for each pulse in the RED/IR(PPG) waveforms. Such a measurement is described in more detail in the following reference, the contents of which are incorporated herein by reference: Wukitsch et al., ‘Pulse Oximetry: Analysis of Theory, Technology, and Practice’, Journal of Clinical Monitoring, 4:290-301 (1988). -
FIG. 12A shows ACC, ECG, and RED/IR(PPG) waveforms measured during periods of no motion (indicated by the dashed box 70) and motion (indicated by the dashed box 71). As withFIGS. 9A-11A , motion inFIG. 12A is indicated by sharp, abrupt changes in the ACC waveform generated along a vertical axis by an accelerometer mounted in the wrist-worn transceiver. The ECG waveforms shown in both dashedboxes -
FIG. 12B shows the IR(PPG) from dashedbox 70 inFIG. 12A , which is measured when the patient is not moving. In this case, the waveform features a smooth, systematic rise time that is relatively easy to process with first and second derivative tests to determine its peak (indicated by marker 73) and foot (indicated by marker 72). Typically sub-ratios are only calculated between thesemarkers FIG. 12C shows the resulting sub-ratios, indicated as dark circles with values of RoR(n) in the graph; the values of each of these data points are indicated below by equation (10): -
- where RED/IR(DC) and AMBIENT(DC) are assumed to be constant throughout the entire pulse, and are described above.
- Because the patient is not moving, the sub-ratios in
FIG. 12C are relatively constant and show little variation in the graph. They can be statistically processed with a variety of statistical techniques, some of which are described in the above-mentioned reference, to determine an ‘effective RoR’ for each pulse in the RED/IR(PPG) waveforms. For example, each value of RoR(n) from equation (10) can be processed with a weighted average defined by wt(n) to determine the effective RoR, as shown below in equation (11): -
- In one embodiment, each weight wt(n) is determined by comparing an SpO2 calculated from its corresponding RoR(n) to a preceding value for SpO2 and determining the weight based on the correlation. For example, if the preceding value for SpO2 is 98% O2, a value for SpO2 in the range of 70-80% O2 calculated from RoR(n) is likely erroneous; the RoR(n) is therefore give a relatively low weight wt(n). Additionally, a relatively large change in the RED/IR(PPG) amplitude during the sub-ratio measurement period n typically indicates that the corresponding value of RoR(n) has a relatively high accuracy. Such values are thus given a relatively high weight wt(n). In general, a number of established statistical techniques can be used to weight the collection of RoR(n) values to generate the effective RoR, as defined above in equation (11).
- In another embodiment, the collection of RoR(n) values, such as those shown in
FIG. 12C , are processed to determine an average and standard deviation. Values lying more than one standard deviation outside the average are then removed from the calculation, and the average is then recalculated. This technique, while typically less accurate than that indicated by equation (11), has the advantage of not requiring a series of weights with arbitrary definitions. In yet another embodiment, the collection of RoR(n) values are fit with a numerical function, such as a linear or non-linear function, and the effective RoR value can be estimated from the coefficients derived from the fit. - The dashed
box 71 inFIG. 12A indicates a more complicated situation where profound motion is present in the ACC waveform; this, in turn, strongly influences the morphology of the RED/IR(PPG) waveforms, while having little affect on the ECG waveform.FIG. 12D shows the resulting IR(PPG) waveform, along with markers 74-77 indicating various points along the waveform where its foot and peak, in theory, could be determined. For example, application of the first and second first derivative tests could indicate two successive pulses, with the pulse betweenmarkers lines markers lines 79A and shown inFIG. 12E . These sub-ratios are relatively noisy compared to those for the motion-free measurements indicated inFIG. 12C , but if processed with a weighted average like that indicated in equation (11) would likely yield a SpO2 value with suitable accuracy. In contrast, the ‘pulse’ betweenmarkers markers lines 79B, result in artificial data points characterized by a large variation, as shown inFIG. 12F . The presence of motion, as indicated by the ACC waveform, drives the algorithm to remove these data points from a SpO2 calculation, as they will not result in an accurate value. -
FIGS. 13A-D show an alternate embodiment of the invention that uses PTT to estimate if a pulse is corrupted by noise, and if so deploys a ‘matched filter’ to process both the RED/IR(PPG) waveforms to maximize signal-to-noise ratios of the pulses therein. With this embodiment the body-worn monitor can determine accurate SpO2 and cNIBP values even when motion-related noise is present. To illustrate this approach,FIGS. 13A-C feature a series of graphs on its left-hand side that show: i) how PTT is calculated from the time separating a peak of the ECG QRS and corresponding foot of the IR(PPG) (FIG. 13A ); 2) PTT measured from an unfiltered PPG waveform plotted as a function of heartbeat (13B); and 3) PTT measured from a PPG waveform processed with a matched filter, and plotted as a function of heartbeat (FIG. 13C ).FIG. 13D , shown in the right-hand side of the figure, is a flowchart corresponding to these graphs that illustrates this two-part method for making measurements when motion-related noise is present. The flowchart, in particular, shows an alternative series of steps 105-111 for processing the ECG, RED/IR(PPG), and ACC waveforms that replace steps 37-41 inFIG. 7 . -
FIG. 13A shows PTT calculated from the time difference (ΔT) separating the ECG QRS and the foot of the IR(PPG) waveform. This waveform is typically used in place of the RED(PPG) because of its superior signal-to-noise ratio. As described above, the QRS features a sharp, easily measurable peak informally marking the beginning of the cardiac cycle. In this case the IR(PPG) waveform follows this feature by about 220 ms; its foot is typically calculated by taking the second derivative of the waveform after performing some initial filtering to remove high-frequency noise, and then looking for a zero-point crossing. Referring to the flow chart shown inFIG. 13D , PTT is determined duringstep 105, and the process is repeated for consecutive heartbeats during a measurement interval, which is typically 10-20 seconds (step 106). - Once PTT is determined for each heartbeat in the measurement interval, a series of simple statistical filters are applied to detect pulses that may be corrupted by motion, and can thus potentially yield inaccurate values. In
step 107, for example, a simple average (AVE) and standard deviation (STDEV) are first calculated in a rolling manner, beginning at pulse N and extending out ε pulses, where ε is the number of pulses within the above-described measurement interval. These statistical parameters can be updated for each subsequent pulse because of the rolling calculation. If the PTT value for the immediate pulse is more than 1 STDEV greater than the AVE for the preceding ε pulses, as shown instep 108, it is flagged as potentially originating from a IR(PPG) that is corrupted by motion. This simple filtering process is shown schematically by thewindow 112 inFIG. 13B (data points outside thiswindow 112 are flagged), and is indicated in the flow chart bystep 107. A rapid change in PTT, however, may be a real occurrence resulting from an actual fluctuation in blood pressure. Thus, the algorithm processes a corresponding ACC waveform generated by the accelerometer within the wrist-worn transceiver to determine if motion was indeed present during a time interval consistent with the PTT and its associated pulse (step 109). Duringstep 109 it may be determined that motion exceeded a pre-determined coefficient (e.g. Mmax). Mmax can be determined, for example, from the power spectrum of time-dependent ACC waveforms that are known to corrupt RED/IR(PPG) waveforms, such as those shown inFIGS. 14-17 and described below. In this case the pulse is considered to be corrupted to an extent that neither PTT nor parameters associated with SpO2 (e.g. RED/IR(foot) and RED/IR(peak)) can be accurately measured. The algorithm then returns to step 105 to resume calculating PTT from the ECG and IR(PPG) waveforms. - On the other hand, if motion is determined to be less than Mmax in
step 109, then it is assumed that the PTT and corresponding pulse may be relatively uncorrupted, but are in need of additional filtering to potentially remove any noise that may have caused the abnormal PTT value. In this case, the pulse is filtered with a matched filter (also referred to as a ‘North filter’ when used in telecommunications). A matched filter is one which features an ideal frequency response that maximizes the signal-to-noise ratio for a given signal of a known shape in the time domain, particularly when the signal is subject to random, stochastic noise, such as that caused by motion. It involves mathematically convolving the immediate pulse with a known good pulse, or ‘pulse template’, using a mathematical cross-correlation algorithm. The cross-correlation yields filtering parameters that, once incorporated, represent a linear filter that in theory can optimize the signal-to-noise ratio of the immediate pulse. Specifically, for this application, a digital matched filter features an impulse response characterized by coefficients h(k). This function represents the time-reversed replica of the ideal signal to be detected, i.e. a pulse in the IR(PPG) measured during a time period where motion (as determined from the ACC waveform) is not present. Alternatively, h(k) can be determined from a standard, pre-programmed pulse, determined from waveforms measured from a large group of patients, which represents a known good pulse. In still other embodiments, this ‘textbook’ pre-programmed pulse is used initially in the matched filter, and then updated as subsequent known good pulses are measured from the patient. The subsequent known good pulse can be just a section of a pulse (e.g., near the foot or peak) that is known to be uncorrupted by noise. In any case, assuming this pulse is represented by the pulse template function xtp(k), then the coefficients h(k) of the matched filter are given by equation (13): -
h(k)=x tp(N−k−1), where k=0, 1, . . . N−1 (13) - The digital matched filter can be represented as a finite impulse response filter with a typical transversal structure, with the output y(i) of the filter shown in equation (14):
-
- where x(k) are the samples of the immediate pulse (i.e. the input pulse requiring filtering), xtp(k) are the samples of the pulse template, N is the filter length, and i is a time shift index. From equations (13) and (14) it is evident that when the pulse template and the immediate pulse are identical, the output of the matched filter will be at its maximum value.
- The matched filter improves the signal-to-noise ratio of the immediate input pulse by an amount that is directly related to the length of the filter (N). A filter length that is greater than or equal to the interval between heart beats is required; preferably the filter length is greater than multiple heart beats.
- Upon completion of
step 110, PTT is calculated from the filtered waveform, and the rolling AVE and STDEV statistics are recalculated (step 107). If the difference between the immediate PTT, as calculated from the filtered waveform, is within ±1 STDEV of the average as perstep 108, then the pulse is considered to be free of motion-related artifacts that may cause erroneous values of SpO2 and cNIBP. Such a case is shown graphically inFIG. 13C . At this point, as indicated bystep 111, the ECG and RED/IR(PPG) are processed as per steps 42-46 and 48-50 inFIG. 7 , and SpO2 and cNIBP are determined. - A patient's activity level, as characterized by ACC waveforms, can have a significant impact on the RED/IR(PPG) and ECG waveforms used to measure both SpO2 and cNIBP. For example,
FIGS. 14-17 show time-dependent graphs of ECG, PPG, and ACC waveforms for a patient who is resting (FIG. 14 ), walking (FIG. 15 ), convulsing (FIG. 16 ), and falling (FIG. 17 ). Each graph includes asingle ECG waveform PPG waveform ACC waveforms FIGS. 9B-11B . - The figures indicate that time-dependent properties of both
ECG PPG ACC waveforms FIG. 14 , for example, shows data collected from a patient at rest. This state is clearly indicated by theACC waveforms 82, which feature a relatively stable baseline along all three axes of the accelerometer. High-frequency noise in all theACC waveforms FIGS. 14-17 is due to electrical noise, and is not indicative of patient motion in any way. TheECG 80 andPPG 81 waveforms for this patient are correspondingly stable, thus allowing algorithms operating on the body-worn monitor to accurately determine SpO2 (from the PPG waveform 81), along with heart rate and respiratory rate (from the ECG waveform 80), cNIBP (from a PTT extracted from both theECG 80 andPPG 81 waveforms). Based on the data shown inFIG. 14 , algorithms operating on the body-worn monitor assume that vital signs calculated from a resting patient are relatively stable; the algorithm therefore deploys normal threshold criteria for alarms/alerts, described below in Table 3, for patients in this state. -
FIG. 15 shows ECG 85,PPG 86, andACC 87 waveforms measured from a walking patient wearing the body-worn monitor. In this case, theACC waveform 87 clearly indicates a quasi-periodic modulation, with each ‘bump’ in the modulation corresponding to a particular step. The ‘gaps’ in the modulation, shown near 10, 19, 27, and 35 seconds, correspond to periods when the patient stops walking and changes direction. Each bump in the ACC waveform includes relatively high-frequency features (other than those associated with electrical noise, described above) that correspond to walking-related movements of the patient's wrist. - The
ECG waveform 85 measured from the walking patient is relatively unaffected by motion, other than indicating an increase in heart rate (i.e., a shorter time separation between neighboring QRS complexes) and respiratory rate (i.e. a higher frequency modulation of the waveform's envelope) caused by the patient's exertion. ThePPG waveform 86, in contrast, is strongly affected by this motion, and pulses within it become basically immeasurable. Its distortion is likely due in part to a quasi-periodic change in light levels, caused by the patient's swinging arm, and detected by the photodetector within the thumb-worn sensor. Movement of the patient's arm additionally affects blood flow in the thumb and can cause the optical sensor to move relative to the patient's skin. The photodetector measures all of these artifacts, along with a conventional PPG signal (like the one shown inFIG. 14 ) caused by volumetric expansion in the underlying arteries and capillaries within the patient's thumb. The artifacts produce radiation-induced photocurrent that is difficult to distinguish from normal PPG signals used to calculate SpO2 and cNIBP. These vital signs are thus difficult or impossible to accurately measure when the patient is walking. - The body-worn monitor deploys multiple strategies to avoid generating false alarms/alerts during a walking activity state. As described in detail below, the monitor can detect this state by processing the ACC waveforms shown in
FIG. 15 along with similar waveforms measured from the patient's bicep and chest. Walking typically elevates heart rate, respiratory rate, and blood pressure, and thus alarm thresholds for these parameters, as indicated by Table 2, are systematically and temporarily increased when this state is detected. Values above the modified thresholds are considered abnormal, and trigger an alarm. SpO2, unlike heart rate, respiratory rate and blood pressure, does not typically increase with exertion. Thus the alarm thresholds for this parameter, as shown in Table 2, do not change when the patient is walking. Body temperature measured with the body-worn monitor typically increases between 1-5%, depending on the physical condition of the patient and the speed at which they are walking. -
TABLE 2 motion-dependent alarm/alert thresholds and heuristic rules for a walking patient Modified Motion Threshold for Heuristic Rules for Vital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure Walking Increase Ignore Threshold; Do (SYS, DIA) (+10-30%) Not Alarm/Alert Heart Rate Walking Increase Use Modified (+10-300%) Threshold; Alarm/Alert if Value Exceeds Threshold Respiratory Walking Increase Ignore Threshold; Do Rate (+10-300%) Not Alarm/Alert SpO2 Walking No Change Ignore Threshold; Do Not Alarm/Alert Temperature Walking Increase Use Original Threshold; (+10-30%) Alarm/Alert if Value Exceeds Threshold - To further reduce false alarms/alerts, software associated with the body-worn monitor or remote monitor can deploy a series of ‘heuristic rules’ determined beforehand using practical, empirical studies. These rules, for example, can indicate that a walking patient is likely healthy, breathing, and characterized by a normal SpO2. Accordingly, the rules dictate that respiratory rate, blood pressure, and SpO2 values measured during a walking state that exceed predetermined alarm/alert thresholds are likely corrupted by artifacts; the system, in turn, does not sound the alarm/alert in this case. Heart rate, as indicated by
FIG. 15 , and body temperature can typically be accurately measured even when a patient is walking; the heuristic rules therefore dictate the modified thresholds listed in Table 2 be used to generate alarms/alerts for these particular vital signs. -
FIG. 16 shows ECG 90,PPG 91, andACC 92 waveforms measured from a patient that is simulating convulsing by rapidly moving their arm back and forth. A patient undergoing a Gran-mal seizure, for example, would exhibit this type of motion. As is clear from the waveforms, the patient is at rest for the initial 10 seconds shown in the graph, during which theECG 90 andPPG 91 waveforms are uncorrupted by motion. The patient then begins a period of simulated, rapid convulsing that lasts for about 12 seconds. A brief 5-second period of rest follows, and then convulsing begins for another 12 seconds or so. - Convulsing modulates the
ACC waveform 92 due to rapid motion of the patient's arm, as measured by the wrist-worn accelerometer. This modulation is strongly coupled into thePPG waveform 91, likely because of the phenomena described above, i.e.: 1) ambient light coupling into the oximetry probe's photodiode; 2) movement of the photodiode relative to the patient's skin; and 3) disrupted blow flow underneath the probe. Note that from about 23-28 seconds theACC waveform 92 is not modulated, indicating that the patient's arm is at rest. During this period the ambient light is constant and the optical sensor is stationary relative to the patient's skin. But thePPG waveform 91 is still strongly modulated, albeit at a different frequency than the modulation that occurred when the patient's arm was moving, and the pulses therein are difficult to resolve. This indicates that the disrupted blood flow underneath the optical sensor continues even after the patient's arm stops moving. Using this information, both ECG and PPG waveforms similar to those shown inFIG. 16 can be analyzed in conjunction with ACC waveforms measured from groups of stationary and moving patients. These data can then be analyzed to estimate the effects of specific motions and activities on the ECG and PPG waveforms, and then deconvolute these factors using known mathematical techniques to effectively remove any motion-related artifacts. The deconvoluted ECG and PPG waveforms can then be used to calculate vital signs, as described in detail below. - The
ECG waveform 90 is modulated by the patient's arm movement, but to a lesser degree than thePPG waveform 91. In this case, modulation is caused primarily by electrical ‘muscle noise’ instigated by the convulsion and detected by the ECG electrodes, and well as by convulsion-induced motion in the ECG cables and electrodes relative to the patient's skin. Such motion is expected to have a similar affect on temperature measurements, which are determined by a sensor that also includes a cable. - Table 3, below, shows the modified threshold values and heuristic rules for alarms/alerts generated by a convulsing patient. In general, when a patient experiences convulsions, such as those simulated during the two 12-second periods in
FIG. 16 , it is virtually impossible to accurately measure any vital signs from theECG 90 andPPG 91 waveforms. For this reason the threshold values corresponding to each vital sign are not adjusted when convulsions are detected. Heart rate determined from the ECG waveform, for example, is typically erroneously high due to high-frequency convulsions, and respiratory rate is immeasurable from the distorted waveform. Strong distortion of the optical waveform also makes both SpO2 and PPT-based cNIBP difficult or impossible to measure. For this reason, algorithms operating on either the body-worn monitor or a remote monitor will not generate alarms/alerts based on vital signs when a patient is convulsing, as these vital signs will almost certainly be corrupted by motion-related artifacts. -
TABLE 3 motion-dependent alarm/alert thresholds and heuristic rules for a convulsing patient Modified Motion Threshold for Heuristic Rules for Vital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure Convulsing No Change Ignore Threshold; (SYS, DIA) Generate Alarm/Alert Because of Convulsion Heart Rate Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because of Convulsion Respiratory Rate Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because of Convulsion SpO2 Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because of Convulsion Temperature Convulsing No Change Ignore Threshold; Generate Alarm/Alert Because of Convulsion - Table 3 also shows the heuristic rules for convulsing patients. Here, the overriding rule is that a convulsing patient needs assistance, and thus an alarm/alert for this patient is generated regardless of their vital signs (which, as described above, are likely inaccurate due to motion-related artifacts). The system always generates an alarm/alert for a convulsing patient.
-
FIG. 17 shows ECG 95,PPG 96, andACC 97 waveforms measured from a patient that experiences a fall roughly 13 seconds into the measuring period. TheACC waveform 97 clearly indicates the fall with a sharp decrease in its signal, followed by a short-term oscillatory signal, due (literally) to the patient bouncing on the floor. After the fall, ACC waveforms 97 associated with the x, y, and z axes also show a prolonged decrease in value due to the resulting change in the patient's posture. In this case, both theECG 95 andPPG 96 waveforms are uncorrupted by motion prior to the fall, but basically immeasurable during the fall, which typically takes only 1-2 seconds. Specifically, this activity adds very high frequency noise to theECG waveform 95, making it impossible to extract heart rate and respiratory rate during this short time period. Falling causes a sharp drop in thePPG waveform 96, presumably for the same reasons as described above (i.e. changes in ambient light, sensor movement, and disruption of blood flow) for walking and convulsing, making it difficult to measure SpO2 and cNIBP. - After a fall, both the
ECG 95 andPPG 96 waveforms are free from artifacts, but both indicate an accelerated heart rate and relatively high heart rate variability for roughly 10 seconds. During this period thePPG waveform 96 also shows distortion and a decrease in pulse amplitude. Without being bound to any theory, the increase in heart rate may be due to the patient's baroreflex, which is the body's haemostatic mechanism for regulating and maintaining blood pressure. The baroreflex, for example, is initiated when a patient begins faint. In this case, the patient's fall may cause a rapid drop in blood pressure, thereby depressing the baroreflex. The body responds by accelerating heart rate (indicated by the ECG waveform 95) and increasing blood pressure (indicated by a reduction in PTT, as measured from theECG 95 andPPG 96 waveforms) in order to deliver more blood to the patient's extremities. - Table 4 shows the heuristic rules and modified alarm thresholds for a falling patient. Falling, similar to convulsing, makes it difficult to measure waveforms and the vital signs calculated from them. Because of this and the short time duration associated with a fall, alarms/alerts based on vital signs thresholds are not generated during an actual falls. However, this activity, optionally coupled with prolonged stationary period or convulsion (both determined from the following ACC waveform), generates an alarm/alert according to the heuristic rules.
-
TABLE 4 motion-dependent alarm/alert thresholds and heuristic rules for a falling patient Modified Motion Threshold for Heuristic Rules for Vital Sign State Alarms/Alerts Alarms/Alerts Blood Pressure Falling No Change Ignore Threshold; Generate (SYS, DIA) Alarm/Alert Because of Fall Heart Rate Falling No Change Ignore Threshold; Generate Alarm/Alert Because of Fall Respiratory Rate Falling No Change Ignore Threshold; Generate Alarm/Alert Because of Fall SpO2 Falling No Change Ignore Threshold; Generate Alarm/Alert Because of Fall Temperature Falling No Change Ignore Threshold; Generate Alarm/Alert Because of Fall - In addition to activity level, as described above and indicated in
FIGS. 14-17 , a patient's posture can influence how the above-described system generates alarms/alerts from SpO2, cNIBP, and other vital signs. For example, the alarms/alerts related to both SpO2 and cNIBP may vary depending on whether the patient is lying down or standing up.FIG. 18 indicates how the body-worn monitor can determine motion-related parameters (e.g. degree of motion, posture, and activity level) from apatient 110 using time-dependent ACC waveforms continuously generated from the threeaccelerometers accelerometer 114 affixed to the wrist. The height of the patient's arm can affect the cNIBP measurement, as blood pressure can vary significantly due to hydrostatic forces induced by changes in arm height. Moreover, this phenomenon can be detected and exploited to calibrate the cNIBP measurement, as described in detail in the above-referenced patent application, the contents of which have been previously incorporated by reference: BODY-WORN VITAL SIGN MONITOR WITH SYSTEM FOR DETECTING AND ANALYZING MOTION (U.S. Ser. No. 12/469,094; filed May 20, 2009). As described in this document, arm height can be determined using DC signals from theaccelerometers accelerometer 112 worn on the patient's chest. An algorithm operating on the wrist-worn transceiver extracts DC values from waveforms measured from this accelerometer and processes them with an algorithm described below to determine posture. - Specifically, posture is determined for a
patient 110 using angles determined between the measured gravitational vector and the axes of a torso coordinatespace 111. The axes of thisspace 111 are defined in a three-dimensional Euclidean space where {right arrow over (R)}CV is the vertical axis, {right arrow over (R)}CH is the horizontal axis, and {right arrow over (R)}CN is the normal axis. These axes must be identified relative to a ‘chest accelerometer coordinate space’ before the patient's posture can be determined. - The first step in determining a patient's posture is to identify alignment of {right arrow over (R)}CV in the chest accelerometer coordinate space. This can be determined in either of two approaches. In the first approach, {right arrow over (R)}CV is assumed based on a typical alignment of the body-worn monitor relative to the patient. During a manufacturing process, these parameters are then preprogrammed into firmware operating on the wrist-worn transceiver. In this procedure it is assumed that accelerometers within the body-worn monitor are applied to each patient with essentially the same configuration. In the second approach, {right arrow over (R)}CV is identified on a patient-specific basis. Here, an algorithm operating on the wrist-worn transceiver prompts the patient (using, e.g., video instruction operating on the wrist-worn transceiver, or audio instructions transmitted through a speaker) to assume a known position with respect to gravity (e.g., standing up with arms pointed straight down). The algorithm then calculates {right arrow over (R)}CV from DC values corresponding to the x, y, and z axes of the chest accelerometer while the patient is in this position. This case, however, still requires knowledge of which arm (left or right) the monitor is worn on, as the chest accelerometer coordinate space can be rotated by 180 degrees depending on this orientation. A medical professional applying the monitor can enter this information using the GUI, described above. This potential for dual-arm attachment requires a set of two pre-determined vertical and normal vectors which are interchangeable depending on the monitor's location. Instead of manually entering this information, the arm on which the monitor is worn can be easily determined following attachment using measured values from the chest accelerometer values, with the assumption that {right arrow over (R)}CV is not orthogonal to the gravity vector.
- The second step in the procedure is to identify the alignment of {right arrow over (R)}CN in the chest accelerometer coordinate space. The monitor can determine this vector, similar to how it determines {right arrow over (R)}CV, with one of two approaches. In the first approach the monitor assumes a typical alignment of the chest-worn accelerometer on the patient. In the second approach, the alignment is identified by prompting the patient to assume a known position with respect to gravity. The monitor then calculates {right arrow over (R)}CN from the DC values of the time-dependent ACC waveform.
- The third step in the procedure is to identify the alignment of {right arrow over (R)}CH in the chest accelerometer coordinate space. This vector is typically determined from the vector cross product of {right arrow over (R)}CV and {right arrow over (R)}CN, or it can be assumed based on the typical alignment of the accelerometer on the patient, as described above.
- A patient's posture is determined using the coordinate system described above and in
FIG. 18 , along with a gravitational vector {right arrow over (R)}G that extends normal from the patient's chest. The angle between {right arrow over (R)}CV and {right arrow over (R)}G is given by equation (14): -
- where the dot product of the two vectors is defined as:
-
{right arrow over (R)}G [n]·{right arrow over (R)} CV=(y Cx [n]×r CVx)+(y Cy [n]×r CVy)+(y Cz [n]×r CVz) (15) - The definition of the norms of {right arrow over (R)}G and {right arrow over (R)}CV are given by equations (16) and (17):
-
∥{right arrow over (R)}G [n]∥=√{square root over ((y Cx [n])2+(y Cy [n])2+(y Cz [n])2)}{square root over ((y Cx [n])2+(y Cy [n])2+(y Cz [n])2)}{square root over ((y Cx [n])2+(y Cy [n])2+(y Cz [n])2)} (16) -
∥{right arrow over (R)}CV∥=√{square root over ((r CVx)2+(r CVy)2+(r CVz)2)}{square root over ((r CVx)2+(r CVy)2+(r CVz)2)}{square root over ((r CVx)2+(r CVy)2+(r CVz)2)} (17) - As indicated in equation (18), the monitor compares the vertical angle θVG to a threshold angle to determine whether the patient is vertical (i.e. standing upright) or lying down:
-
if θVG≦45° then Torso State=0 the patient is upright (18) - If the condition in equation (18) is met the patient is assumed to be upright, and their torso state, which is a numerical value equated to the patient's posture, is equal to 0. The patient is assumed to be lying down if the condition in equation (18) is not met, i.e. θVG>45 degrees. Their lying position is then determined from angles separating the two remaining vectors, as defined below.
- The angle θNG between {right arrow over (R)}CN and {right arrow over (R)}G determines if the patient is lying in the supine position (chest up), prone position (chest down), or on their side. Based on either an assumed orientation or a patient-specific calibration procedure, as described above, the alignment of {right arrow over (R)}CN is given by equation (19), where i, j, k represent the unit vectors of the x, y, and z axes of the chest accelerometer coordinate space respectively:
-
{right arrow over (R)}CN =r CNx î+r CNy ĵ+r CNz {circumflex over (k)} (19) - The angle between {right arrow over (R)}CN and {right arrow over (R)}G determined from DC values extracted from the chest accelerometer ACC waveform is given by equation (20):
-
- The body-worn monitor determines the normal angle θNG and then compares it to a set of predetermined threshold angles to determine which position the patient is lying in, as shown in equation (21):
-
if θNG≦35° then Torso State=1, the patient is supine -
if θNG≧135° then Torso State=2, the patient is prone (21) - If the conditions in equation (21) are not met then the patient is assumed to be lying on their side. Whether they are lying on their right or left side is determined from the angle calculated between the horizontal torso vector and measured gravitational vectors, as described above.
- The alignment of {right arrow over (R)}CH is determined using either an assumed orientation, or from the vector cross-product of {right arrow over (R)}CV and {right arrow over (R)}CN as given by equation (22), where i, j, k represent the unit vectors of the x, y, and z axes of the accelerometer coordinate space respectively. Note that the orientation of the calculated vector is dependent on the order of the vectors in the operation. The order below defines the horizontal axis as positive towards the right side of the patient's body.
-
{right arrow over (R)}CH =r CVx î+r CVy ĵ+r CVz {circumflex over (k)}={right arrow over (R)}CV×{right arrow over (R)}CN (22) - The angle θHG between {right arrow over (R)}CH and {right arrow over (R)}G is determined using equation (23):
-
- The monitor compares this angle to a set of predetermined threshold angles to determine if the patient is lying on their right or left side, as given by equation (24):
-
if θHG≧90° then Torso State=3, the patient is on their right side -
if θNG<90° then Torso State=4, the patient is on their left side (24) - Table 5 describes each of the above-described postures, along with a corresponding numerical torso state used to render, e.g., a particular icon:
-
TABLE 5 postures and their corresponding torso states Posture Torso State Upright 0 supine: lying on back 1 prone: lying on chest 2 lying on right side 3 lying on left side 4 undetermined posture 5 -
FIGS. 19A and 19B show, respectively, graphs of time-dependent ACC waveforms 100 measured along the x, y, and z-axes, and the torso states (i.e. postures) 101 determined from these waveforms for a moving patient, as described above. As the patient moves, the DC values of the ACC waveforms measured by the chest accelerometer vary accordingly, as shown by thegraph 100 inFIG. 19A . The body-worn monitor processes these values as described above to continually determine {right arrow over (R)}G and the various quantized torso states for the patient, as shown in thegraph 101 inFIG. 19B . The torso states yield the patient's posture as defined in Table 5. For this study the patient rapidly alternated between standing, lying on their back, chest, right side, and left side within a time period of about 160 seconds. As described above, different alarm/alert conditions (e.g. threshold values) for vital signs can be assigned to each of these postures, or the specific posture itself may result in an alarm/alert. Additionally, the time-dependent properties of thegraph 101 can be analyzed (e.g. changes in the torso states can be counted) to determine, for example, how often the patient moves in their hospital bed. This number can then be equated to various metrics, such as a ‘bed sore index’ indicating a patient that is so stationary in their bed that lesions may result. Such a state could then be used to trigger an alarm/alert to the supervising medical professional. -
FIG. 20 is a schematic drawing showing time-dependentcurrent pulses current pulses FIG. 22 . To minimize power consumption,current pulses line 122 is 1 ms for a drive frequency of 500 Hz. -
FIG. 21 shows acircuit 175 that generates thecurrent pulses circuit 175 features anoperational amplifier 180 that receives a control voltage (Vcontrol) on its gating pin. Theamplifier 180 is connected to atransistor 182 andresistor 181 that, along with a supply voltage of 3.3V (typically from a Li:ion battery), generate thecurrent pulses infrared LED 150 operating at two wavelengths (660/905 nm). The wavelength of the LED depends on the direction that it is biased. To select the biasing direction, thecircuit 175 featuresred control lines infrared control lines current pulses LED 150, and ultimately through thetransistor 182 andresistor 181 toground 183. TheLED 150 is biased in a forward direction whencontrol lines LED 150 to generate red radiation. Voltage flowing across theLED 150 is also decreased because it is a diode. In this case thecontrol lines FIG. 20 , this configuration persists for 100 μs, after which thered control lines infrared control lines LED 150 in a backwards direction to generate infrared radiation according to the above-described drive current. The alternating process is repeated at 500 Hz. In both cases, red and infrared radiation that transmits through the patient's thumb is detected by aphotodetector 155 that features both an anode and cathode. Each black dot inFIG. 20 (five in total) indicates a separate wire in the cable that connects the oximeter probe to the wrist-worn transceiver. The wire associated with the cathode of thephotodiode 155 also functions as a shield for the remaining 4 wires. - As shown in
FIGS. 21 and 22 , a thumb-wornpulse oximeter probe 294 contains the red/infrared LED 150 along with again resistor 149 indicating the specific wavelengths of both the red and infrared radiation. During a measurement, the microprocessor in the wrist-worn transceiver determines the value of theresistor 149 by monitoring a voltage drop across it; this value, in turn, is compared to a value stored in memory to select the appropriate coefficients relating RoR to SpO2. Theprobe 294 generates alternating red and infrared radiation according to the timing diagram inFIG. 20 that passes through the base of the patient'sthumb 151, where it is partially absorbed by underlying vasculature according to the patient's heart rate and SpO2 values. Radiation that transmits through thethumb 151 illuminates aphotodiode 155 that, in response, generates a photocurrent varying in magnitude with the degree of optical absorption in the patient's thumb. Anamplifier circuit 140 beginning with atransimpedance amplifier 156 receives the photocurrent and converts it to a corresponding voltage which is then amplified and filtered to generate the RED/IR(PPG) waveforms used to determine SpO2 and cNIBP. - The
amplifier circuit 140 features separate channels for amplifying and filtering signals corresponding to red radiation, infrared radiation, and ambient light detected by thephotodiode 155 when the LED is not biased to generate radiation. This occurs, for example, during the time periods shown inFIG. 20 when neither the red or infrared LED is driven. Once detected, the degree of ambient light can be subtracted from both the red and infrared signals to improve their resultant signal-to-noise ratio. The amplifier channel corresponding to red radiation is activated by a sample-and-hold integratedcircuit 157 that is controlled by thesame control lines FIG. 20 . When the red LED is driven, the sample-and-hold circuit 157 is switched on, whilesimilar components hold circuit 157 samples and maintains an analog voltage from thetransimpedance amplifier 156, which then passes through a low-pass filter 158 characterized by a 20 Hz cutoff. This filter removes any high-frequency noise (e.g. 60 Hz electrical noise) that is not related to the RED(PPG), and yields a preliminary waveform that is digitized with an analog-to-digital converter 176, and processed as described above to generate a RED(DC) value. The preliminary waveform then passes through a high-pass filter 160 with a cutoff of 0.1 Hz to remove the DC portion and leave only the AC portion, which typically represents about 0.5-1% of the total signal magnitude. The AC portion is further amplified with astandard instrumentation amplifier 162 featuring a programmable gain that is controlled with a 1.65 reference voltage and a digital potentiometer (not shown in the figure; this component may be included directly in the instrumentation amplifier) featuring a variable resistance controlled by the microprocessor. The microprocessor selects the resistance (according to a predetermined binary command) and corresponding gain to maximize the dynamic range of the analog-to-digital converter 176. This process results in an amplified version of the RED(AC) signal, which is then digitized with the analog-to-digital converter 176 and then processed as described above. - The above-described filtering and amplification processes are repeated when the infrared LED and a sample-and-hold integrated
circuit 164 corresponding to the infrared channel are activated with infrared I/O control lines pass 166 and high-pass 168 filters corresponding to this channel are identical to those used for the red channel. Theinstrumentation amplifier 170 is also identical, but is controlled by a separate digital potentiometer to have a unique, uncoupled gain. This is because the IR(PPG) typically has a relatively large amplitude, and thus requires less amplification, than the RED(PPG). The channel corresponding to ambient light only requires processing of DC signals, and thus includes a sample-and-hold integratedcircuit 172 that passes an analog voltage to a low-pass filter 174 featuring a 20 Hz cutoff. The filtered value corresponding to ambient light is then digitized with the analog-to-digital converter and then processed as described above. -
FIGS. 23A and 23B show how the body-wornmonitor 190 described above attaches to apatient 270. These figures show two configurations of the system:FIG. 23A shows the system used during the indexing portion of the Composite Technique, and includes a pneumatic, cuff-basedsystem 285, whileFIG. 23B shows the system used for subsequent SpO2 and cNIBP measurements. The indexing measurement typically takes about 60 seconds, and is typically performed once every 4-8 hours. Once the indexing measurement is complete the cuff-basedsystem 285 is typically removed from the patient. The remainder of the time thesystem 190 performs the SpO2 and cNIBP measurements. - The body-worn
monitor 190 features a wrist-worntransceiver 272, described in more detail inFIG. 24 , featuring atouch panel interface 273 that displays SpO2, blood pressure values and other vital signs. Awrist strap 290 affixes thetransceiver 272 to the patient's wrist like a conventional wristwatch. Aflexible cable 292 connects thetransceiver 272 to apulse oximeter probe 294 that wraps around the base of the patient's thumb. During a measurement, theprobe 294 generates a time-dependent PPG waveform which is processed along with an ECG to measure cNIBP and SpO2. This provides an accurate representation of blood pressure in the central regions of the patient's body, as described above. - To determine ACC waveforms the body-worn
monitor 190 features three separate accelerometers located at different portions on the patient's arm and chest. The first accelerometer is surface-mounted on a circuit board in the wrist-worntransceiver 272 and measures signals associated with movement of the patient's wrist. As described above, this motion can also be indicative of that originating from the patient's fingers, which will affect the SpO2 measurement. The second accelerometer is included in asmall bulkhead portion 296 included along the span of thecable 282. During a measurement, a small piece of disposable tape, similar in size to a conventional bandaid, affixes thebulkhead portion 296 to the patient's arm. In this way thebulkhead portion 296 serves two purposes: 1) it measures a time-dependent ACC waveform from the mid-portion of the patient's arm, thereby allowing their posture and arm height to be determined as described in detail above; and 2) it secures thecable 286 to the patient's arm to increase comfort and performance of the body-wornmonitor 190, particularly when the patient is ambulatory. - The cuff-based
module 285 features apneumatic system 276 that includes a pump, valve, pressure fittings, pressure sensor, analog-to-digital converter, microcontroller, and rechargeable Li:ion battery. During an indexing measurement, thepneumatic system 276 inflates adisposable cuff 284 and performs two measurements according to the Composite Technique: 1) it performs an inflation-based measurement of oscillometry to determine values for SYS, DIA, and MAP; and 2) it determines a patient-specific relationship between PTT and MAP. These measurements are described in detail in the above-referenced patent application entitled: ‘VITAL SIGN MONITOR FOR MEASURING BLOOD PRESSURE USING OPTICAL, ELECTRICAL, AND PRESSURE WAVEFORMS’ (U.S. Ser. No. 12/138,194; filed Jun. 12, 2008), the contents of which have been previously incorporated herein by reference. - The
cuff 284 within the cuff-basedpneumatic system 285 is typically disposable and features an internal, airtight bladder that wraps around the patient's bicep to deliver a uniform pressure field. During the indexing measurement, pressure values are digitized by the internal analog-to-digital converter, and sent through acable 286 according to a CAN protocol, along with SYS, DIA, and MAP blood pressures, to the wrist-worntransceiver 272 for processing as described above. Once the cuff-based measurement is complete, the cuff-basedmodule 285 is removed from the patient's arm and thecable 282 is disconnected from the wrist-worntransceiver 272. cNIBP is then determined using PTT, as described in detail above. - To determine an ECG, the body-worn
monitor 190 features a small-scale, three-lead ECG circuit integrated directly into abulkhead 274 that terminates anECG cable 282. The ECG circuit features an integrated circuit that collects electrical signals from three chest-worn ECG electrodes 278 a-c connected through cables 280 a-c. The ECG electrodes 278 a-c are typically disposed in a conventional ‘Einthoven's Triangle’ configuration which is a triangle-like orientation of the electrodes 278 a-c on the patient's chest that features three unique ECG vectors. From these electrical signals the ECG circuit determines up to three ECG waveforms, which are digitized using an analog-to-digital converter mounted proximal to the ECG circuit, and sent through acable 282 to the wrist-worntransceiver 272 according to the CAN protocol. There, the ECG and PPG waveforms are processed to determine the patient's blood pressure. Heart rate and respiratory rate are determined directly from the ECG waveform using known algorithms, such as those described above. Thecable bulkhead 274 also includes an accelerometer that measures motion associated with the patient's chest as described above. - There are several advantages of digitizing ECG and ACC waveforms prior to transmitting them through the
cable 282. First, a single transmission line in thecable 282 can transmit multiple digital waveforms, each generated by different sensors. This includes multiple ECG waveforms (corresponding, e.g., to vectors associated with three, five, and twelve-lead ECG systems) from the ECG circuit mounted in thebulkhead 274, along with waveforms associated with the x, y, and z axes of accelerometers mounted in thebulkheads - More sophisticated ECG circuits can plug into the wrist-worn transceiver to replace the three-lead system shown in
FIGS. 23A and 23B . These ECG circuits can include, e.g., five and twelve leads. -
FIG. 24 shows a close-up view of the wrist-worntransceiver 272. As described above, it attaches to the patient's wrist using aflexible strap 290 which threads through two D-ring openings in aplastic housing 206. Thetransceiver 272 houses portions of thecircuits FIGS. 20 and 21 , and additionally features atouchpanel display 200 that renders aGUI 273 which is altered depending on the viewer (typically the patient or a medical professional). Specifically, thetransceiver 272 includes a small-scaleinfrared barcode scanner 202 that, during use, can scan a barcode worn on a badge of a medical professional. The barcode indicates to the transceiver's software that, for example, a nurse or doctor is viewing the user interface. In response, theGUI 273 displays vital sign data and other medical diagnostic information appropriate for medical professionals. Using thisGUI 273, the nurse or doctor, for example, can view the vital sign information, set alarm parameters, and enter information about the patient (e.g. their demographic information, medication, or medical condition). The nurse can press a button on theGUI 273 indicating that these operations are complete. At this point, thedisplay 200 renders an interface that is more appropriate to the patient, such as time of day and battery power. - As described above, the
transceiver 272 features three CAN connectors 204 a-c on the side of its upper portion, each which supports the CAN protocol and wiring schematics, and relays digitized data to the internal CPU. Digital signals that pass through the CAN connectors include a header that indicates the specific signal (e.g. ECG, ACC, or pressure waveform from the cuff-based module) and the sensor from which the signal originated. This allows the CPU to easily interpret signals that arrive through the CAN connectors 204 a-c, and means that these connectors are not associated with a specific cable. Any cable connecting to the transceiver can be plugged into any connector 204 a-c. As shown inFIG. 23A , thefirst connector 204 a receives thecable 282 that transports a digitized ECG waveform determined from the ECG circuit and electrodes, and digitized ACC waveforms measured by accelerometers in thecable bulkhead 274 and thebulkhead portion 296 associated with theECG cable 282. - The
second CAN connector 204 b shown inFIG. 22 receives thecable 286 that connects to the pneumatic cuff-basedsystem 285 used for the pressure-dependent indexing measurement (shown inFIG. 23A ). Thisconnector 204 b receives a time-dependent pressure waveform delivered by thepneumatic system 285 to the patient's arm, along with values for SYS, DIA, and MAP values determined during the indexing measurement. Thecable 286 unplugs from theconnector 204 b once the indexing measurement is complete, and is plugged back in after approximately four hours for another indexing measurement. - The
final CAN connector 204 c can be used for an ancillary device, e.g. a glucometer, infusion pump, body-worn insulin pump, ventilator, or end-tidal CO2 delivery system. As described above, digital information generated by these systems will include a header that indicates their origin so that the CPU can process them accordingly. - The
transceiver 272 includes aspeaker 201 that allows a medical professional to communicate with the patient using a voice over Internet protocol (VOIP). For example, using thespeaker 201 the medical professional could query the patient from a central nursing station or mobile phone connected to a wireless, Internet-based network within the hospital. Or the medical professional could wear a separate transceiver similar to the shown inFIG. 24 , and use this as a communication device. In this application, thetransceiver 272 worn by the patient functions much like a conventional cellular telephone or ‘walkie talkie’: it can be used for voice communications with the medical professional and can additionally relay information describing the patient's vital signs and motion. The speaker can also enunciate pre-programmed messages to the patient, such as those used to calibrate the chest-worn accelerometers for a posture calculation, as described above. - In addition to those methods described above, the body-worn monitor can use a number of additional methods to calculate blood pressure and other properties from the optical and electrical waveforms. These are described in the following co-pending patent applications, the contents of which are incorporated herein by reference: 1) CUFFLESS BLOOD-PRESSURE MONITOR AND ACCOMPANYING WIRELESS, INTERNET-BASED SYSTEM (U.S. Ser. No. 10/709,015; filed Apr. 7, 2004); 2) CUFFLESS SYSTEM FOR MEASURING BLOOD PRESSURE (U.S. Ser. No. 10/709,014; filed Apr. 7, 2004); 3) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WEB SERVICES INTERFACE (U.S. Ser. No. 10/810,237; filed Mar. 26, 2004); 4) VITAL SIGN MONITOR FOR ATHLETIC APPLICATIONS (U.S. Ser. No.; filed Sep. 13, 2004); 5) CUFFLESS BLOOD PRESSURE MONITOR AND ACCOMPANYING WIRELESS MOBILE DEVICE (U.S. Ser. No. 10/967,511; filed Oct. 18, 2004); 6) BLOOD PRESSURE MONITORING DEVICE FEATURING A CALIBRATION-BASED ANALYSIS (U.S. Ser. No. 10/967,610; filed Oct. 18, 2004); 7) PERSONAL COMPUTER-BASED VITAL SIGN MONITOR (U.S. Ser. No. 10/906,342; filed Feb. 15, 2005); 8) PATCH SENSOR FOR MEASURING BLOOD PRESSURE WITHOUT A CUFF (U.S. Ser. No. 10/906,315; filed Feb. 14, 2005); 9) PATCH SENSOR FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/160,957; filed Jul. 18, 2005); 10) WIRELESS, INTERNET-BASED SYSTEM FOR MEASURING VITAL SIGNS FROM A PLURALITY OF PATIENTS IN A HOSPITAL OR MEDICAL CLINIC (U.S. Ser. No. 11/162,719; filed Sep. 9, 2005); 11) HAND-HELD MONITOR FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/162,742; filed Sep. 21, 2005); 12) CHEST STRAP FOR MEASURING VITAL SIGNS (U.S. Ser. No. 11/306,243; filed Dec. 20, 2005); 13) SYSTEM FOR MEASURING VITAL SIGNS USING AN OPTICAL MODULE FEATURING A GREEN LIGHT SOURCE (U.S. Ser. No. 11/307,375; filed Feb. 3, 2006); 14) BILATERAL DEVICE, SYSTEM AND METHOD FOR MONITORING VITAL SIGNS (U.S. Ser. No. 11/420,281; filed May 25, 2006); 15) SYSTEM FOR MEASURING VITAL SIGNS USING BILATERAL PULSE TRANSIT TIME (U.S. Ser. No. 11/420,652; filed May 26, 2006); 16) BLOOD PRESSURE MONITOR (U.S. Ser. No. 11/530,076; filed Sep. 8, 2006); 17) TWO-PART PATCH SENSOR FOR MONITORING VITAL SIGNS (U.S. Ser. No. 11/558,538; filed Nov. 10, 2006); and, 18) MONITOR FOR MEASURING VITAL SIGNS AND RENDERING VIDEO IMAGES (U.S. Ser. No. 11/682,177; filed Mar. 5, 2007).
- Other embodiments are also within the scope of the invention. For example, other measurement techniques, such as conventional oscillometry measured during deflation, can be used to determine SYS for the above-described algorithms. Additionally, processing units and probes for measuring pulse oximetry similar to those described above can be modified and worn on other portions of the patient's body. For example, pulse oximetry probes with finger-ring configurations can be worn on fingers other than the thumb. Or they can be modified to attach to other conventional sites for measuring SpO2, such as the ear, forehead, and bridge of the nose. In these embodiments the processing unit can be worn in places other than the wrist, such as around the neck (and supported, e.g., by a lanyard) or on the patient's waist (supported, e.g., by a clip that attaches to the patient's belt). In still other embodiments the probe and processing unit are integrated into a single unit.
- In other embodiments, a set of body-worn monitors can continuously monitor a group of patients, wherein each patient in the group wears a body-worn monitor similar to those described herein. Additionally, each body-worn monitor can be augmented with a location sensor. The location sensor includes a wireless component and a location-processing component that receives a signal from the wireless component and processes it to determine a physical location of the patient. A processing component (similar to that described above) determines from the time-dependent waveforms at least one vital sign, one motion parameter, and an alarm parameter calculated from the combination of this information. A wireless transceiver transmits the vital sign, motion parameter, location of the patient, and alarm parameter through a wireless system. A remote computer system featuring a display and an interface to the wireless system receives the information and displays it on a user interface for each patient in the group.
- In embodiments, the interface rendered on the display at the central nursing station features a field that displays a map corresponding to an area with multiple sections. Each section corresponds to the location of the patient and includes, e.g., the patient's vital signs, motion parameter, and alarm parameter. For example, the field can display a map corresponding to an area of a hospital (e.g. a hospital bay or emergency room), with each section corresponding to a specific bed, chair, or general location in the area. Typically the display renders graphical icons corresponding to the motion and alarm parameters for each patient in the group. In other embodiments, the body-worn monitor includes a graphical display that renders these parameters directly on the patient.
- Typically the location sensor and the wireless transceiver operate on a common wireless system, e.g. a wireless system based on 802.11, 802.15.4, or cellular protocols. In this case a location is determined by processing the wireless signal with one or more algorithms known in the art. These include, for example, triangulating signals received from at least three different base stations, or simply estimating a location based on signal strength and proximity to a particular base station. In still other embodiments the location sensor includes a conventional global positioning system (GPS).
- The body-worn monitor can include a first voice interface, and the remote computer can include a second voice interface that integrates with the first voice interface. The location sensor, wireless transceiver, and first and second voice interfaces can all operate on a common wireless system, such as one of the above-described systems based on 802.11 or cellular protocols. The remote computer, for example, can be a monitor that is essentially identical to the monitor worn by the patient, and can be carried or worn by a medical professional. In this case the monitor associated with the medical professional features a GUI wherein the user can select to display information (e.g. vital signs, location, and alarms) corresponding to a particular patient. This monitor can also include a voice interface so the medical professional can communicate directly with the patient.
- Still other embodiments are within the scope of the following claims.
Claims (23)
1. A system for monitoring a patient, comprising:
an oximetry sensor comprising a photodetector and a first and second light source, the photodetector configured to generate a first signal by detecting radiation from the first light source after it irradiates the patient, and a second signal by detecting radiation from the second light source after it irradiates the patient, the oximetry sensor comprising a first cable;
an ECG system comprising an electrical circuit connected to at least two electrodes attached to the patient to measure an ECG signal, the ECG system configured to be positioned on a location selected from the patient's arm and chest and comprising a second cable;
an oscillometric blood pressure monitoring system comprising a pump and a cuff attached to the patient to measure an oscillometric blood pressure value, the oscillometric blood pressure monitoring system configured to be positioned on the patient's arm and comprising a third cable;
a motion sensor configured to generate a motion signal representing motion of a part of the patient's body it is attached to;
a processing unit configured to be worn on the patient's body and connected to the oximetry sensor through the first cable to receive the first and second signals, to the ECG system through the second cable to receive the ECG signal, to the oscillometric blood pressure monitoring system through the third cable to receive an oscillometric blood pressure value, and to the motion sensor to receive the motion signal, the processing unit comprising a processor configured to process: i) the first and second signals to determine a value for oxygen saturation; ii) the ECG waveform, oscillometric blood pressure value, and at least one of the first and second signals to determine a blood pressure value; and iii) the motion signal to determine one of the patient's posture and activity level.
2. A system for monitoring oxygen saturation from a patient, comprising:
an optical sensor configured to generate an optical signal;
an ECG sensor configured to be worn on the patient's chest and generate a digital ECG signal;
a motion sensor configured to generate a motion signal; and
a processing unit configured to be worn on the patient's body and comprising separate input ports that connect to the optical sensor, the ECG sensor and motion sensor, the processing unit configured to determine at least two unique vital signs by collectively processing the optical signal, ECG signal, and motion signal.
3. The system of claim 1 , wherein the processing unit is configured to be worn on the patient's wrist.
4. The system of claim 3 , wherein the processing unit comprises a first input port to receive the first cable, a second input port to receive the second cable, and a third input port to receive the third cable.
5. The system of claim 4 , wherein the second and third input ports are both serial ports configured to receive digital signals.
6. The system of claim 5 , wherein the second and third input ports are configured to operate on a common communication protocol.
7. The system of claim 4 , wherein the second and third input ports are located on a common side of the processing unit.
8. These system of claim 7 , wherein a first side portion of the processing unit comprises the first input port, and a second side portion located on an opposite end of the processing unit comprises the second and third input ports.
9. The system of claim 1 , wherein the second cable comprises the motion sensor.
10. The system of claim 9 , wherein the motion sensor is positioned along an axial extent of the second cable.
11. The system of claim 9 , wherein a single housing comprises the ECG system and the motion sensor.
12. The system of claim 11 , wherein the second cable further comprises a second motion sensor positioned along an axial extent of the second cable.
13. The system of claim 12 , wherein the processing unit comprises a third motion sensor.
14. The system of claim 12 , wherein the housing is comprised by a terminal end of the second cable.
15. The system of claim 14 , wherein the first, second, and third motion sensors are accelerometers.
16. The system of claim 1 , wherein the processing unit comprises a touchpanel display.
17. The system of claim 15 , wherein the touchpanel display is configured to render a first user interface that displays information describing oxygen saturation, a second user interface that displays information describing blood pressure, and a third user interface that displays information describing ECG signals.
18. The system of claim 1 , wherein the processing unit further comprises a barcode scanner.
19. The system of claim 18 , wherein the processing unit is further configured to render a user interface corresponding to a medical professional after the barcode scanner scans a barcode.
20. The system of claim 1 , wherein the processing unit comprises a speaker.
21. The system of claim 20 , wherein the processing unit is further configured to generate audible voice messages through the speaker.
22. The system of claim 1 , wherein the processing unit comprises a wireless transmitter.
23. The system of claim 22 , wherein the wireless transmitter is configured to communicate with a network.
Priority Applications (8)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US12/559,403 US20100324388A1 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
EP10790195.1A EP2442709B1 (en) | 2009-06-17 | 2010-06-17 | Body-worn pulse oximeter |
SG2011093507A SG176892A1 (en) | 2009-06-17 | 2010-06-17 | Body-worn pulse oximeter |
PCT/US2010/039000 WO2010148205A1 (en) | 2009-06-17 | 2010-06-17 | Body-worn pulse oximeter |
SG10201404148WA SG10201404148WA (en) | 2009-06-17 | 2010-06-17 | Body-worn pulse oximeter |
US14/092,173 US10085658B2 (en) | 2009-06-17 | 2013-11-27 | Body-worn pulse oximeter |
US16/148,948 US11103148B2 (en) | 2009-06-17 | 2018-10-01 | Body-worn pulse oximeter |
US17/463,373 US20210401304A1 (en) | 2009-06-17 | 2021-08-31 | Body-worn pulse oximeter |
Applications Claiming Priority (7)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
US21805909P | 2009-06-17 | 2009-06-17 | |
US21806009P | 2009-06-17 | 2009-06-17 | |
US21805709P | 2009-06-17 | 2009-06-17 | |
US21805509P | 2009-06-17 | 2009-06-17 | |
US21806109P | 2009-06-17 | 2009-06-17 | |
US21806209P | 2009-06-17 | 2009-06-17 | |
US12/559,403 US20100324388A1 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
Related Child Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US14/092,173 Continuation US10085658B2 (en) | 2009-06-17 | 2013-11-27 | Body-worn pulse oximeter |
Publications (1)
Publication Number | Publication Date |
---|---|
US20100324388A1 true US20100324388A1 (en) | 2010-12-23 |
Family
ID=43354912
Family Applications (12)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/559,392 Active 2034-05-01 US10085657B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,413 Active 2032-05-11 US8554297B2 (en) | 2007-06-12 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,403 Abandoned US20100324388A1 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,386 Active 2030-12-13 US9775529B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,379 Active 2035-07-24 US9596999B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,408 Active 2032-03-07 US8437824B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US14/092,173 Active 2031-03-29 US10085658B2 (en) | 2009-06-17 | 2013-11-27 | Body-worn pulse oximeter |
US15/723,039 Active 2030-06-06 US10813562B2 (en) | 2009-06-17 | 2017-10-02 | Body-worn pulse oximeter |
US16/148,948 Active 2030-03-25 US11103148B2 (en) | 2009-06-17 | 2018-10-01 | Body-worn pulse oximeter |
US16/148,874 Active 2030-06-15 US11134857B2 (en) | 2009-06-17 | 2018-10-01 | Body-worn pulse oximeter |
US17/080,725 Active 2030-08-14 US11638533B2 (en) | 2009-06-17 | 2020-10-26 | Body-worn pulse oximeter |
US17/463,373 Pending US20210401304A1 (en) | 2009-06-17 | 2021-08-31 | Body-worn pulse oximeter |
Family Applications Before (2)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/559,392 Active 2034-05-01 US10085657B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,413 Active 2032-05-11 US8554297B2 (en) | 2007-06-12 | 2009-09-14 | Body-worn pulse oximeter |
Family Applications After (9)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
US12/559,386 Active 2030-12-13 US9775529B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,379 Active 2035-07-24 US9596999B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US12/559,408 Active 2032-03-07 US8437824B2 (en) | 2009-06-17 | 2009-09-14 | Body-worn pulse oximeter |
US14/092,173 Active 2031-03-29 US10085658B2 (en) | 2009-06-17 | 2013-11-27 | Body-worn pulse oximeter |
US15/723,039 Active 2030-06-06 US10813562B2 (en) | 2009-06-17 | 2017-10-02 | Body-worn pulse oximeter |
US16/148,948 Active 2030-03-25 US11103148B2 (en) | 2009-06-17 | 2018-10-01 | Body-worn pulse oximeter |
US16/148,874 Active 2030-06-15 US11134857B2 (en) | 2009-06-17 | 2018-10-01 | Body-worn pulse oximeter |
US17/080,725 Active 2030-08-14 US11638533B2 (en) | 2009-06-17 | 2020-10-26 | Body-worn pulse oximeter |
US17/463,373 Pending US20210401304A1 (en) | 2009-06-17 | 2021-08-31 | Body-worn pulse oximeter |
Country Status (4)
Country | Link |
---|---|
US (12) | US10085657B2 (en) |
EP (1) | EP2442709B1 (en) |
SG (2) | SG10201404148WA (en) |
WO (1) | WO2010148205A1 (en) |
Cited By (58)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8321004B2 (en) | 2009-09-15 | 2012-11-27 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8364250B2 (en) | 2009-09-15 | 2013-01-29 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8437824B2 (en) | 2009-06-17 | 2013-05-07 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US8475370B2 (en) | 2009-05-20 | 2013-07-02 | Sotera Wireless, Inc. | Method for measuring patient motion, activity level, and posture along with PTT-based blood pressure |
US8527038B2 (en) | 2009-09-15 | 2013-09-03 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8545417B2 (en) | 2009-09-14 | 2013-10-01 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US8591411B2 (en) | 2010-03-10 | 2013-11-26 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8594776B2 (en) | 2009-05-20 | 2013-11-26 | Sotera Wireless, Inc. | Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds |
WO2013175314A2 (en) * | 2012-05-23 | 2013-11-28 | Convergent Engineering, Inc. | System and method for detecting preeclampsia |
US8602997B2 (en) | 2007-06-12 | 2013-12-10 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US20140073962A1 (en) * | 2012-09-11 | 2014-03-13 | Nellcor Puritan Bennett Llc | System and method for determining stroke volume of a patient |
US8731649B2 (en) | 2012-08-30 | 2014-05-20 | Covidien Lp | Systems and methods for analyzing changes in cardiac output |
US8740802B2 (en) | 2007-06-12 | 2014-06-03 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US8747330B2 (en) | 2010-04-19 | 2014-06-10 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8888700B2 (en) | 2010-04-19 | 2014-11-18 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8977348B2 (en) | 2012-12-21 | 2015-03-10 | Covidien Lp | Systems and methods for determining cardiac output |
US8979765B2 (en) | 2010-04-19 | 2015-03-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9060745B2 (en) | 2012-08-22 | 2015-06-23 | Covidien Lp | System and method for detecting fluid responsiveness of a patient |
US9173593B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9173594B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9339209B2 (en) | 2010-04-19 | 2016-05-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9357937B2 (en) | 2012-09-06 | 2016-06-07 | Covidien Lp | System and method for determining stroke volume of an individual |
US9364158B2 (en) | 2010-12-28 | 2016-06-14 | Sotera Wirless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US9439574B2 (en) | 2011-02-18 | 2016-09-13 | Sotera Wireless, Inc. | Modular wrist-worn processor for patient monitoring |
US20160262702A1 (en) * | 2013-11-25 | 2016-09-15 | Samsung Electronics Co., Ltd. | Method and apparatus for measuring bio signal |
US20160324477A1 (en) * | 2015-05-08 | 2016-11-10 | Texas Instruments Incorporated | Accuracy of heart rate estimation from photoplethysmographic (ppg) signals |
CN106361348A (en) * | 2015-11-19 | 2017-02-01 | 北京智谷睿拓技术服务有限公司 | Finger type determining method and equipment |
US9582072B2 (en) | 2013-09-17 | 2017-02-28 | Medibotics Llc | Motion recognition clothing [TM] with flexible electromagnetic, light, or sonic energy pathways |
USD788312S1 (en) | 2012-02-09 | 2017-05-30 | Masimo Corporation | Wireless patient monitoring device |
US9833192B2 (en) | 2013-03-15 | 2017-12-05 | Thought Technology Ltd. | Finger mounted physiology sensor |
US20180146865A1 (en) * | 2015-05-29 | 2018-05-31 | Cis Forschungsinstitut Fuer Mikrosensorik Gmbh | Method and device for ascertaining a blood pressure curve |
US10226187B2 (en) | 2015-08-31 | 2019-03-12 | Masimo Corporation | Patient-worn wireless physiological sensor |
US10234934B2 (en) | 2013-09-17 | 2019-03-19 | Medibotics Llc | Sensor array spanning multiple radial quadrants to measure body joint movement |
US10307111B2 (en) | 2012-02-09 | 2019-06-04 | Masimo Corporation | Patient position detection system |
US10321873B2 (en) | 2013-09-17 | 2019-06-18 | Medibotics Llc | Smart clothing for ambulatory human motion capture |
US10357187B2 (en) | 2011-02-18 | 2019-07-23 | Sotera Wireless, Inc. | Optical sensor for measuring physiological properties |
US10420476B2 (en) | 2009-09-15 | 2019-09-24 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10602965B2 (en) | 2013-09-17 | 2020-03-31 | Medibotics | Wearable deformable conductive sensors for human motion capture including trans-joint pitch, yaw, and roll |
US10617302B2 (en) | 2016-07-07 | 2020-04-14 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US10709339B1 (en) * | 2017-07-03 | 2020-07-14 | Senstream, Inc. | Biometric wearable for continuous heart rate and blood pressure monitoring |
US10716510B2 (en) | 2013-09-17 | 2020-07-21 | Medibotics | Smart clothing with converging/diverging bend or stretch sensors for measuring body motion or configuration |
US10779765B2 (en) | 2015-09-28 | 2020-09-22 | Aclaris Medical, Llc | Wearable physiologic sensing apparatus |
US10806351B2 (en) | 2009-09-15 | 2020-10-20 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10898087B2 (en) | 2017-12-08 | 2021-01-26 | Texas Instruments Incorporated | Motion detection and cancellation using ambient light |
US11058303B2 (en) | 2012-09-14 | 2021-07-13 | Covidien Lp | System and method for determining stability of cardiac output |
US11076777B2 (en) | 2016-10-13 | 2021-08-03 | Masimo Corporation | Systems and methods for monitoring orientation to reduce pressure ulcer formation |
US11253169B2 (en) | 2009-09-14 | 2022-02-22 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US11330988B2 (en) | 2007-06-12 | 2022-05-17 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US11382519B2 (en) | 2018-02-05 | 2022-07-12 | Samsung Electronics Co., Ltd. | Blood pressure measuring apparatus and blood pressure measuring method |
USD974193S1 (en) | 2020-07-27 | 2023-01-03 | Masimo Corporation | Wearable temperature measurement device |
USD980091S1 (en) | 2020-07-27 | 2023-03-07 | Masimo Corporation | Wearable temperature measurement device |
US11607152B2 (en) | 2007-06-12 | 2023-03-21 | Sotera Wireless, Inc. | Optical sensors for use in vital sign monitoring |
US11666277B2 (en) | 2016-10-12 | 2023-06-06 | Samsung Electronics Co., Ltd. | Apparatus and method for estimating biometric information |
USD1000975S1 (en) | 2021-09-22 | 2023-10-10 | Masimo Corporation | Wearable temperature measurement device |
US11896350B2 (en) | 2009-05-20 | 2024-02-13 | Sotera Wireless, Inc. | Cable system for generating signals for detecting motion and measuring vital signs |
US11963736B2 (en) | 2009-07-20 | 2024-04-23 | Masimo Corporation | Wireless patient monitoring system |
US11963746B2 (en) | 2009-09-15 | 2024-04-23 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US11974833B2 (en) | 2020-03-20 | 2024-05-07 | Masimo Corporation | Wearable device for noninvasive body temperature measurement |
Families Citing this family (225)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20080221930A1 (en) | 2007-03-09 | 2008-09-11 | Spacelabs Medical, Inc. | Health data collection tool |
US9026370B2 (en) | 2007-12-18 | 2015-05-05 | Hospira, Inc. | User interface improvements for medical devices |
WO2009128914A1 (en) * | 2008-04-15 | 2009-10-22 | Nonin Medical, Inc. | Non-invasive optical sensor |
US8203438B2 (en) | 2008-07-29 | 2012-06-19 | Masimo Corporation | Alarm suspend system |
IN2012DN03108A (en) | 2009-10-16 | 2015-09-18 | Spacelabs Healthcare Llc | |
US9604020B2 (en) | 2009-10-16 | 2017-03-28 | Spacelabs Healthcare Llc | Integrated, extendable anesthesia system |
KR101320545B1 (en) * | 2009-12-21 | 2013-10-28 | 한국전자통신연구원 | Apparatus and method for sensing photoplethysmogram and fall |
WO2011097399A1 (en) | 2010-02-03 | 2011-08-11 | Nellcor Puritan Bennett Llc | Combined physiological sensor systems and methods |
JP2013521054A (en) | 2010-03-01 | 2013-06-10 | マシモ コーポレイション | Adaptive alarm system |
GB2491086B (en) | 2010-03-21 | 2016-10-05 | Spacelabs Healthcare Llc | Multi-display bedside monitoring system |
US20110245688A1 (en) * | 2010-03-31 | 2011-10-06 | General Electric Company | System and method of performing electrocardiography with motion detection |
US8888720B2 (en) * | 2010-04-02 | 2014-11-18 | Stanford P. Hudson | Great toe dorsiflexion detection |
US8659435B2 (en) * | 2010-04-02 | 2014-02-25 | George Anthony McKinney | Waterproof optically-sensing fiberless-optically-communicating vitality monitoring and alarming system, particularly for swimmers and infants |
TWI410235B (en) | 2010-04-21 | 2013-10-01 | Univ Nat Chiao Tung | Apparatus for identifying falls and activities of daily living |
US11980449B2 (en) | 2010-04-22 | 2024-05-14 | Leaf Healthcare, Inc. | Systems and methods for monitoring orientation and biometric data using acceleration data |
US10758162B2 (en) * | 2010-04-22 | 2020-09-01 | Leaf Healthcare, Inc. | Systems, devices and methods for analyzing a person status based at least on a detected orientation of the person |
BR112013012329B1 (en) | 2010-11-19 | 2021-05-04 | Spacelabs Healthcare, Llc | SCREEN DEVICE FOR USE IN A PATIENT MONITORING SYSTEM AND PATIENT MONITORING SYSTEM |
CN102138789B (en) * | 2011-01-24 | 2014-05-14 | 无锡微感科技有限公司 | Dynamic electrocardiogram and motion recording and analyzing system |
WO2012125135A1 (en) * | 2011-03-11 | 2012-09-20 | Spacelabs Healthcare, Llc | Methods and systems to determine multi-parameter managed alarm hierarchy during patient monitoring |
US9629566B2 (en) | 2011-03-11 | 2017-04-25 | Spacelabs Healthcare Llc | Methods and systems to determine multi-parameter managed alarm hierarchy during patient monitoring |
JP6166253B2 (en) * | 2011-03-25 | 2017-07-19 | ゾール メディカル コーポレイションZOLL Medical Corporation | Controller and method for adapting alarms in wearable medical devices |
CA2844807C (en) | 2011-08-19 | 2022-07-26 | Hospira, Inc. | Systems and methods for a graphical interface including a graphical representation of medical data |
WO2013054477A1 (en) * | 2011-10-11 | 2013-04-18 | 株式会社村田製作所 | Portable instrument |
US20130131477A1 (en) * | 2011-11-15 | 2013-05-23 | Oneeros, Inc. | Pulse oximetry system |
US9700222B2 (en) | 2011-12-02 | 2017-07-11 | Lumiradx Uk Ltd | Health-monitor patch |
US9734304B2 (en) * | 2011-12-02 | 2017-08-15 | Lumiradx Uk Ltd | Versatile sensors with data fusion functionality |
WO2013090709A1 (en) | 2011-12-16 | 2013-06-20 | Hospira, Inc. | System for monitoring and delivering medication to a patient and method of using the same to minimize the risks associated with automated therapy |
US9247896B2 (en) | 2012-01-04 | 2016-02-02 | Nellcor Puritan Bennett Ireland | Systems and methods for determining respiration information using phase locked loop |
US9339691B2 (en) | 2012-01-05 | 2016-05-17 | Icon Health & Fitness, Inc. | System and method for controlling an exercise device |
US9995611B2 (en) | 2012-03-30 | 2018-06-12 | Icu Medical, Inc. | Air detection system and method for detecting air in a pump of an infusion system |
US9681836B2 (en) | 2012-04-23 | 2017-06-20 | Cyberonics, Inc. | Methods, systems and apparatuses for detecting seizure and non-seizure states |
JP5641011B2 (en) * | 2012-05-01 | 2014-12-17 | 株式会社デンソー | Blood pressure measurement device |
CA3089257C (en) | 2012-07-31 | 2023-07-25 | Icu Medical, Inc. | Patient care system for critical medications |
US10136823B2 (en) * | 2012-08-28 | 2018-11-27 | Board Of Trustees Of Michigan State University | Methods and apparatus for determining cuff blood pressure |
WO2014038077A1 (en) * | 2012-09-07 | 2014-03-13 | 富士通株式会社 | Pulse wave detection method, pulse wave detection device and pulse wave detection program |
US10413251B2 (en) | 2012-10-07 | 2019-09-17 | Rhythm Diagnostic Systems, Inc. | Wearable cardiac monitor |
US10244949B2 (en) | 2012-10-07 | 2019-04-02 | Rhythm Diagnostic Systems, Inc. | Health monitoring systems and methods |
US10610159B2 (en) | 2012-10-07 | 2020-04-07 | Rhythm Diagnostic Systems, Inc. | Health monitoring systems and methods |
USD850626S1 (en) | 2013-03-15 | 2019-06-04 | Rhythm Diagnostic Systems, Inc. | Health monitoring apparatuses |
JP2016501048A (en) * | 2012-11-11 | 2016-01-18 | ケンコウ ゲーエムベーハーKenkou GmbH | Method and apparatus for determining biological parameters |
US8743052B1 (en) * | 2012-11-24 | 2014-06-03 | Eric Jeffrey Keller | Computing interface system |
US9039614B2 (en) | 2013-01-15 | 2015-05-26 | Fitbit, Inc. | Methods, systems and devices for measuring fingertip heart rate |
US9560978B2 (en) | 2013-02-05 | 2017-02-07 | Covidien Lp | Systems and methods for determining respiration information from a physiological signal using amplitude demodulation |
US9443415B2 (en) * | 2013-02-06 | 2016-09-13 | Michael Nepo | Disseminating information to facilitate user safety |
US9277890B2 (en) * | 2013-02-25 | 2016-03-08 | Dynasthetics, Llc | System for generating noninvasive respiratory monitor signals |
US9554712B2 (en) | 2013-02-27 | 2017-01-31 | Covidien Lp | Systems and methods for generating an artificial photoplethysmograph signal |
US10463283B2 (en) * | 2013-03-14 | 2019-11-05 | Spry Health, Inc. | Systems and methods of multispectral blood measurement |
US9254409B2 (en) | 2013-03-14 | 2016-02-09 | Icon Health & Fitness, Inc. | Strength training apparatus with flywheel and related methods |
US9827431B2 (en) | 2013-04-02 | 2017-11-28 | West Affum Holdings Corp. | Wearable defibrillator with no long-term ECG monitoring |
US10016613B2 (en) | 2013-04-02 | 2018-07-10 | West Affum Holdings Corp. | Wearable cardiac defibrillator system long-term monitoring alternating patient parameters other than ECG |
CA2913421C (en) | 2013-05-24 | 2022-02-15 | Hospira, Inc. | Multi-sensor infusion system for detecting air or an occlusion in the infusion system |
ES2838450T3 (en) | 2013-05-29 | 2021-07-02 | Icu Medical Inc | Infusion set that uses one or more sensors and additional information to make an air determination relative to the infusion set |
WO2014194065A1 (en) | 2013-05-29 | 2014-12-04 | Hospira, Inc. | Infusion system and method of use which prevents over-saturation of an analog-to-digital converter |
US10987026B2 (en) | 2013-05-30 | 2021-04-27 | Spacelabs Healthcare Llc | Capnography module with automatic switching between mainstream and sidestream monitoring |
US20170245773A1 (en) * | 2013-08-30 | 2017-08-31 | Joseph Wiesel | Method and Apparatus for Detecting Atrial Fibrilation |
US11147499B2 (en) | 2013-08-30 | 2021-10-19 | Joseph Wiesel | Method and apparatus for detecting atrial fibrillation |
US20190167139A1 (en) | 2017-12-05 | 2019-06-06 | Gust H. Bardy | Subcutaneous P-Wave Centric Insertable Cardiac Monitor For Long Term Electrocardiographic Monitoring |
US9737224B2 (en) | 2013-09-25 | 2017-08-22 | Bardy Diagnostics, Inc. | Event alerting through actigraphy embedded within electrocardiographic data |
US11213237B2 (en) | 2013-09-25 | 2022-01-04 | Bardy Diagnostics, Inc. | System and method for secure cloud-based physiological data processing and delivery |
US11723575B2 (en) | 2013-09-25 | 2023-08-15 | Bardy Diagnostics, Inc. | Electrocardiography patch |
US10251576B2 (en) | 2013-09-25 | 2019-04-09 | Bardy Diagnostics, Inc. | System and method for ECG data classification for use in facilitating diagnosis of cardiac rhythm disorders with the aid of a digital computer |
US10463269B2 (en) | 2013-09-25 | 2019-11-05 | Bardy Diagnostics, Inc. | System and method for machine-learning-based atrial fibrillation detection |
US9433367B2 (en) | 2013-09-25 | 2016-09-06 | Bardy Diagnostics, Inc. | Remote interfacing of extended wear electrocardiography and physiological sensor monitor |
US10433751B2 (en) | 2013-09-25 | 2019-10-08 | Bardy Diagnostics, Inc. | System and method for facilitating a cardiac rhythm disorder diagnosis based on subcutaneous cardiac monitoring data |
US10736529B2 (en) | 2013-09-25 | 2020-08-11 | Bardy Diagnostics, Inc. | Subcutaneous insertable electrocardiography monitor |
US10799137B2 (en) | 2013-09-25 | 2020-10-13 | Bardy Diagnostics, Inc. | System and method for facilitating a cardiac rhythm disorder diagnosis with the aid of a digital computer |
US9615763B2 (en) | 2013-09-25 | 2017-04-11 | Bardy Diagnostics, Inc. | Ambulatory electrocardiography monitor recorder optimized for capturing low amplitude cardiac action potential propagation |
WO2015048194A1 (en) | 2013-09-25 | 2015-04-02 | Bardy Diagnostics, Inc. | Self-contained personal air flow sensing monitor |
US10165946B2 (en) | 2013-09-25 | 2019-01-01 | Bardy Diagnostics, Inc. | Computer-implemented system and method for providing a personal mobile device-triggered medical intervention |
US10624551B2 (en) | 2013-09-25 | 2020-04-21 | Bardy Diagnostics, Inc. | Insertable cardiac monitor for use in performing long term electrocardiographic monitoring |
US9345414B1 (en) | 2013-09-25 | 2016-05-24 | Bardy Diagnostics, Inc. | Method for providing dynamic gain over electrocardiographic data with the aid of a digital computer |
US9433380B1 (en) | 2013-09-25 | 2016-09-06 | Bardy Diagnostics, Inc. | Extended wear electrocardiography patch |
US10806360B2 (en) | 2013-09-25 | 2020-10-20 | Bardy Diagnostics, Inc. | Extended wear ambulatory electrocardiography and physiological sensor monitor |
US9717432B2 (en) | 2013-09-25 | 2017-08-01 | Bardy Diagnostics, Inc. | Extended wear electrocardiography patch using interlaced wire electrodes |
US10433748B2 (en) | 2013-09-25 | 2019-10-08 | Bardy Diagnostics, Inc. | Extended wear electrocardiography and physiological sensor monitor |
US9730593B2 (en) | 2013-09-25 | 2017-08-15 | Bardy Diagnostics, Inc. | Extended wear ambulatory electrocardiography and physiological sensor monitor |
US9619660B1 (en) | 2013-09-25 | 2017-04-11 | Bardy Diagnostics, Inc. | Computer-implemented system for secure physiological data collection and processing |
US9775536B2 (en) | 2013-09-25 | 2017-10-03 | Bardy Diagnostics, Inc. | Method for constructing a stress-pliant physiological electrode assembly |
US10820801B2 (en) | 2013-09-25 | 2020-11-03 | Bardy Diagnostics, Inc. | Electrocardiography monitor configured for self-optimizing ECG data compression |
US9717433B2 (en) | 2013-09-25 | 2017-08-01 | Bardy Diagnostics, Inc. | Ambulatory electrocardiography monitoring patch optimized for capturing low amplitude cardiac action potential propagation |
US9655537B2 (en) | 2013-09-25 | 2017-05-23 | Bardy Diagnostics, Inc. | Wearable electrocardiography and physiology monitoring ensemble |
US9408545B2 (en) | 2013-09-25 | 2016-08-09 | Bardy Diagnostics, Inc. | Method for efficiently encoding and compressing ECG data optimized for use in an ambulatory ECG monitor |
US9504423B1 (en) | 2015-10-05 | 2016-11-29 | Bardy Diagnostics, Inc. | Method for addressing medical conditions through a wearable health monitor with the aid of a digital computer |
US10667711B1 (en) | 2013-09-25 | 2020-06-02 | Bardy Diagnostics, Inc. | Contact-activated extended wear electrocardiography and physiological sensor monitor recorder |
US9364155B2 (en) | 2013-09-25 | 2016-06-14 | Bardy Diagnostics, Inc. | Self-contained personal air flow sensing monitor |
US10736531B2 (en) | 2013-09-25 | 2020-08-11 | Bardy Diagnostics, Inc. | Subcutaneous insertable cardiac monitor optimized for long term, low amplitude electrocardiographic data collection |
US9700227B2 (en) | 2013-09-25 | 2017-07-11 | Bardy Diagnostics, Inc. | Ambulatory electrocardiography monitoring patch optimized for capturing low amplitude cardiac action potential propagation |
US10888239B2 (en) | 2013-09-25 | 2021-01-12 | Bardy Diagnostics, Inc. | Remote interfacing electrocardiography patch |
US9655538B2 (en) | 2013-09-25 | 2017-05-23 | Bardy Diagnostics, Inc. | Self-authenticating electrocardiography monitoring circuit |
US9408551B2 (en) | 2013-11-14 | 2016-08-09 | Bardy Diagnostics, Inc. | System and method for facilitating diagnosis of cardiac rhythm disorders with the aid of a digital computer |
US10281953B2 (en) * | 2013-11-29 | 2019-05-07 | Motiv Inc. | Wearable device and data transmission method |
EP4071581A1 (en) | 2013-11-29 | 2022-10-12 | Motiv Inc. | Wearable computing device |
US9750463B2 (en) | 2013-12-10 | 2017-09-05 | General Electric Company | Respiratory stress detection |
CN105848733B (en) | 2013-12-26 | 2018-02-13 | 爱康保健健身有限公司 | Magnetic resistance mechanism in hawser apparatus |
US20150190077A1 (en) * | 2014-01-07 | 2015-07-09 | Samsung Electronics Co., Ltd. | Electronic device and photoplethysmography method |
EP3102097B1 (en) | 2014-02-06 | 2020-10-28 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of vital signs |
US9486163B2 (en) * | 2014-02-21 | 2016-11-08 | Verily Life Sciences Llc | Silicon-vacancy-doped nanodiamonds for molecular and cellular imaging |
JP6181576B2 (en) * | 2014-02-25 | 2017-08-16 | 日本光電工業株式会社 | Hemodynamic measurement apparatus and hemodynamic measurement method |
US10342917B2 (en) | 2014-02-28 | 2019-07-09 | Icu Medical, Inc. | Infusion system and method which utilizes dual wavelength optical air-in-line detection |
WO2015138339A1 (en) | 2014-03-10 | 2015-09-17 | Icon Health & Fitness, Inc. | Pressure sensor to quantify work |
AU2015266706B2 (en) | 2014-05-29 | 2020-01-30 | Icu Medical, Inc. | Infusion system and pump with configurable closed loop delivery rate catch-up |
USD756999S1 (en) | 2014-06-02 | 2016-05-24 | Motiv Inc. | Wearable computing device |
US9575560B2 (en) | 2014-06-03 | 2017-02-21 | Google Inc. | Radar-based gesture-recognition through a wearable device |
CN106470739B (en) | 2014-06-09 | 2019-06-21 | 爱康保健健身有限公司 | It is incorporated to the funicular system of treadmill |
EP3157430A4 (en) * | 2014-06-17 | 2018-02-07 | Hewlett-Packard Development Company, L.P. | Rh incompatibility detection |
WO2015195965A1 (en) | 2014-06-20 | 2015-12-23 | Icon Health & Fitness, Inc. | Post workout massage device |
US9435791B2 (en) * | 2014-06-27 | 2016-09-06 | Verily Life Sciences, LLC | Method for using nanodiamonds to detect nearby magnetic nanoparticles |
US9775528B2 (en) | 2014-06-30 | 2017-10-03 | Koninklijke Philips N.V. | Photoplethysmography sensor apparatus and method |
CN105249939A (en) * | 2014-07-17 | 2016-01-20 | 原相科技股份有限公司 | Physiology detection module capable of denoising and physiology detection method thereof |
US20160029898A1 (en) | 2014-07-30 | 2016-02-04 | Valencell, Inc. | Physiological Monitoring Devices and Methods Using Optical Sensors |
US9811164B2 (en) | 2014-08-07 | 2017-11-07 | Google Inc. | Radar-based gesture sensing and data transmission |
US9921660B2 (en) | 2014-08-07 | 2018-03-20 | Google Llc | Radar-based gesture recognition |
US11169988B2 (en) | 2014-08-22 | 2021-11-09 | Google Llc | Radar recognition-aided search |
US9778749B2 (en) | 2014-08-22 | 2017-10-03 | Google Inc. | Occluded gesture recognition |
KR102299361B1 (en) | 2014-09-03 | 2021-09-07 | 삼성전자주식회사 | Apparatus and method for monitoring blood pressure, wearable device having function of blood pressure monitoring |
US10517489B2 (en) | 2014-09-08 | 2019-12-31 | Apple Inc. | Wrist worn accelerometer for pulse transit time (PTT) measurements of blood pressure |
US20160073965A1 (en) * | 2014-09-17 | 2016-03-17 | Covidien Lp | Methods and systems for determining fluid responsiveness |
US10258267B2 (en) * | 2014-09-22 | 2019-04-16 | Capsule Technologies, Inc. | Pulse oximeter with an accelerometer |
CA2962530C (en) * | 2014-09-25 | 2023-02-14 | Aseptika Ltd | Medical devices and related methods |
US10478128B2 (en) * | 2014-09-26 | 2019-11-19 | Pixart Imaging Inc. | Heart rate detection architecture |
US11771375B2 (en) | 2014-09-26 | 2023-10-03 | Pixart Imaging Inc. | Respiration rate detection device and breath detection device adopting motion denoising |
US9794653B2 (en) * | 2014-09-27 | 2017-10-17 | Valencell, Inc. | Methods and apparatus for improving signal quality in wearable biometric monitoring devices |
US9600080B2 (en) | 2014-10-02 | 2017-03-21 | Google Inc. | Non-line-of-sight radar-based gesture recognition |
KR102270209B1 (en) * | 2014-10-28 | 2021-06-29 | 삼성전자주식회사 | Wearable electronic device |
US10201312B2 (en) * | 2014-12-08 | 2019-02-12 | Intel Corporation | Opportunistic measurements and processing of user's context |
US11344668B2 (en) | 2014-12-19 | 2022-05-31 | Icu Medical, Inc. | Infusion system with concurrent TPN/insulin infusion |
KR102411658B1 (en) | 2015-01-15 | 2022-06-21 | 삼성전자주식회사 | Apparatus for detecting information of the living body |
US10064582B2 (en) | 2015-01-19 | 2018-09-04 | Google Llc | Noninvasive determination of cardiac health and other functional states and trends for human physiological systems |
WO2016138307A1 (en) * | 2015-02-25 | 2016-09-01 | Echo Labs, Inc. | Systems and methods for non-invasive blood pressure measurement |
US10391361B2 (en) | 2015-02-27 | 2019-08-27 | Icon Health & Fitness, Inc. | Simulating real-world terrain on an exercise device |
US10850024B2 (en) | 2015-03-02 | 2020-12-01 | Icu Medical, Inc. | Infusion system, device, and method having advanced infusion features |
US10524704B2 (en) * | 2015-03-04 | 2020-01-07 | Nihon Kohden Corporation | Pulse photometer and method for calculating concentration of light absorber in blood |
KR102384225B1 (en) | 2015-03-06 | 2022-04-07 | 삼성전자주식회사 | System and method for sensing blood pressure |
CN106137172A (en) * | 2015-03-23 | 2016-11-23 | 钰太芯微电子科技(上海)有限公司 | Sphygmus measurement system |
US10016162B1 (en) | 2015-03-23 | 2018-07-10 | Google Llc | In-ear health monitoring |
US9848780B1 (en) | 2015-04-08 | 2017-12-26 | Google Inc. | Assessing cardiovascular function using an optical sensor |
US10321879B2 (en) | 2015-04-22 | 2019-06-18 | Diego Alejandro Delia | Multiple sensor wireless wearable pulse oximeter-based device |
WO2016176574A1 (en) | 2015-04-30 | 2016-11-03 | Google Inc. | Wide-field radar-based gesture recognition |
EP3885882A1 (en) | 2015-04-30 | 2021-09-29 | Google LLC | Rf-based micro-motion tracking for gesture tracking and recognition |
JP6517356B2 (en) | 2015-04-30 | 2019-05-22 | グーグル エルエルシー | Type-independent RF signal representation |
US10080528B2 (en) | 2015-05-19 | 2018-09-25 | Google Llc | Optical central venous pressure measurement |
US10088908B1 (en) | 2015-05-27 | 2018-10-02 | Google Llc | Gesture detection and interactions |
US9693592B2 (en) | 2015-05-27 | 2017-07-04 | Google Inc. | Attaching electronic components to interactive textiles |
US10376195B1 (en) | 2015-06-04 | 2019-08-13 | Google Llc | Automated nursing assessment |
US10952638B2 (en) | 2015-06-12 | 2021-03-23 | ChroniSense Medical Ltd. | System and method for monitoring respiratory rate and oxygen saturation |
US10687742B2 (en) | 2015-06-12 | 2020-06-23 | ChroniSense Medical Ltd. | Using invariant factors for pulse oximetry |
US11712190B2 (en) | 2015-06-12 | 2023-08-01 | ChroniSense Medical Ltd. | Wearable device electrocardiogram |
US11160459B2 (en) | 2015-06-12 | 2021-11-02 | ChroniSense Medical Ltd. | Monitoring health status of people suffering from chronic diseases |
US11464457B2 (en) | 2015-06-12 | 2022-10-11 | ChroniSense Medical Ltd. | Determining an early warning score based on wearable device measurements |
US10470692B2 (en) * | 2015-06-12 | 2019-11-12 | ChroniSense Medical Ltd. | System for performing pulse oximetry |
US11160461B2 (en) | 2015-06-12 | 2021-11-02 | ChroniSense Medical Ltd. | Blood pressure measurement using a wearable device |
US20180296163A1 (en) * | 2015-07-27 | 2018-10-18 | Google Inc. | Altering Physiological Signals Based On Patient Movement |
KR102434701B1 (en) | 2015-09-01 | 2022-08-22 | 삼성전자주식회사 | Apparatus and method for acquiring bio- information and apparatus for detecting bio- information |
WO2017044823A1 (en) | 2015-09-11 | 2017-03-16 | Board Of Trustees Of Michigan State University | Method for determining cuff blood pressure |
US10299734B2 (en) * | 2015-10-02 | 2019-05-28 | Covidien Lp | Medical device with adaptive power consumption |
US10817065B1 (en) | 2015-10-06 | 2020-10-27 | Google Llc | Gesture recognition using multiple antenna |
US9717424B2 (en) * | 2015-10-19 | 2017-08-01 | Garmin Switzerland Gmbh | System and method for generating a PPG signal |
WO2017070046A1 (en) | 2015-10-21 | 2017-04-27 | A-Scan Llc | Depth scanning oxygen sensor |
JP6811773B2 (en) | 2015-11-24 | 2021-01-13 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | How to Quantify PhotoPretismogram (PPG) Signal Quality |
WO2017096313A1 (en) | 2015-12-02 | 2017-06-08 | Echo Labs, Inc. | Systems and methods for non-invasive respiratory rate measurement |
US10646144B2 (en) | 2015-12-07 | 2020-05-12 | Marcelo Malini Lamego | Wireless, disposable, extended use pulse oximeter apparatus and methods |
CN105550530B (en) * | 2016-01-29 | 2018-07-17 | 北京心量科技有限公司 | Spirit degree at random obtains device |
US9883800B2 (en) | 2016-02-11 | 2018-02-06 | General Electric Company | Wireless patient monitoring system and method |
US9814388B2 (en) | 2016-02-11 | 2017-11-14 | General Electric Company | Wireless patient monitoring system and method |
US11000235B2 (en) | 2016-03-14 | 2021-05-11 | ChroniSense Medical Ltd. | Monitoring procedure for early warning of cardiac episodes |
US10272317B2 (en) | 2016-03-18 | 2019-04-30 | Icon Health & Fitness, Inc. | Lighted pace feature in a treadmill |
US10625137B2 (en) | 2016-03-18 | 2020-04-21 | Icon Health & Fitness, Inc. | Coordinated displays in an exercise device |
US10493349B2 (en) | 2016-03-18 | 2019-12-03 | Icon Health & Fitness, Inc. | Display on exercise device |
US10018506B2 (en) * | 2016-03-30 | 2018-07-10 | Peel Technologies, Inc. | Multifunctional infrared module |
US11350837B2 (en) * | 2016-03-30 | 2022-06-07 | Elfi-Tech Ltd. | Method and apparatus for optically measuring blood pressure |
US11134901B2 (en) * | 2016-03-30 | 2021-10-05 | Elfi-Tech Ltd. | Method and apparatus for optically measuring blood pressure |
US20220183579A1 (en) * | 2016-03-30 | 2022-06-16 | Elfi-Tech Ltd. | Method and apparatus for optically measuring blood pressure |
WO2017171632A1 (en) * | 2016-04-01 | 2017-10-05 | Nitto Denko Corporation | A method of deriving systolic blood pressure and/or diastolic blood pressure of a subject |
US10098558B2 (en) | 2016-04-25 | 2018-10-16 | General Electric Company | Wireless patient monitoring system and method |
WO2017192167A1 (en) | 2016-05-03 | 2017-11-09 | Google Llc | Connecting an electronic component to an interactive textile |
CN107872965B (en) | 2016-05-09 | 2021-08-06 | 倍灵科技有限公司 | Wearable device for healthcare and method thereof |
ES2912378T3 (en) | 2016-05-13 | 2022-05-25 | Icu Medical Inc | Common Line Auto Purge Infusion Pump System |
US11324888B2 (en) | 2016-06-10 | 2022-05-10 | Icu Medical, Inc. | Acoustic flow sensor for continuous medication flow measurements and feedback control of infusion |
JP6629485B2 (en) * | 2016-08-09 | 2020-01-15 | コーニンクレッカ フィリップス エヌ ヴェKoninklijke Philips N.V. | Processing apparatus and method for determining walking motion of subject |
EP3496604A1 (en) * | 2016-08-09 | 2019-06-19 | Koninklijke Philips N.V. | Device, system and method for monitoring of peripheral arterial perfusion of a subject |
TWI631929B (en) * | 2016-08-16 | 2018-08-11 | 原相科技股份有限公司 | Optical detection apparatuses and method for physiological characteristic(s) |
EP3292813A1 (en) * | 2016-08-29 | 2018-03-14 | SmartCardia SA | Method and device for processing bio-signals |
US10182729B2 (en) * | 2016-08-31 | 2019-01-22 | Medtronics, Inc. | Systems and methods for monitoring hemodynamic status |
US10671705B2 (en) | 2016-09-28 | 2020-06-02 | Icon Health & Fitness, Inc. | Customizing recipe recommendations |
CN110191673A (en) | 2016-11-03 | 2019-08-30 | 巴西勒利夫技术有限公司 | Noninvasive blood glucose sensor |
CN108618763A (en) * | 2017-03-20 | 2018-10-09 | 上海敏传智能科技有限公司 | A kind of ring form of device for medicine detection |
EP3381356A1 (en) * | 2017-03-28 | 2018-10-03 | IMEC vzw | System and method for cuffless blood pressure estimation |
EP3406189B1 (en) * | 2017-05-25 | 2024-05-29 | Tata Consultancy Services Limited | System and method for heart rate estimation |
US20200113498A1 (en) * | 2017-06-23 | 2020-04-16 | 3M Innovative Properties Company | Wireless pulse oximeter device |
BR202017014797U2 (en) * | 2017-07-08 | 2018-02-27 | Tacito Mistrorigo De Almeida | portable oximeter mounting arrangement for continuous use |
US10806933B2 (en) | 2017-09-06 | 2020-10-20 | General Electric Company | Patient monitoring systems and methods that detect interference with pacemaker |
CN107844747A (en) * | 2017-10-13 | 2018-03-27 | 上海斐讯数据通信技术有限公司 | A kind of Weight-detecting device biometric discrimination method and system |
US10089055B1 (en) | 2017-12-27 | 2018-10-02 | Icu Medical, Inc. | Synchronized display of screen content on networked devices |
US10659963B1 (en) | 2018-02-12 | 2020-05-19 | True Wearables, Inc. | Single use medical device apparatus and methods |
KR102544668B1 (en) | 2018-03-12 | 2023-06-16 | 삼성전자주식회사 | Blood pressure measurement apparatus and method |
US11786744B2 (en) | 2018-03-16 | 2023-10-17 | Zoll Medical Corporation | Wearable medical device for continuous heart monitoring with intermittent additional signal data provided via one or more touch-sensitive electrodes |
GB2572626B (en) * | 2018-04-05 | 2021-04-07 | Life Meter Srl | Pulse oximetry device, system and method |
DE102018005024B4 (en) | 2018-06-21 | 2020-06-25 | Insion Gmbh | Procedure for collecting indicators |
US20200015689A1 (en) * | 2018-07-13 | 2020-01-16 | Verily Life Sciences Llc | Wearable blood pressure meter with actuated cuff |
CN112638242A (en) * | 2018-08-24 | 2021-04-09 | 马塞洛·马利尼·拉梅戈 | Monitoring device and system |
US11311237B2 (en) * | 2019-01-28 | 2022-04-26 | Itamar Medical Spry 2021, Limited Partnership | Systems and methods for computationally efficient non-invasive blood quality measurement |
US11331017B2 (en) * | 2019-02-13 | 2022-05-17 | Viavi Solutions Inc. | Calibration-free pulse oximetry |
US10918289B1 (en) * | 2019-06-12 | 2021-02-16 | Fitbit, Inc. | Ring for optically measuring biometric data |
US10966643B1 (en) * | 2019-06-12 | 2021-04-06 | Fitbit, Inc. | Wearable non-invasive carbon monoxide inhalation tracking |
US11471102B2 (en) | 2019-06-20 | 2022-10-18 | Medici Technologies Llc | Hydration assessment system |
US11096579B2 (en) | 2019-07-03 | 2021-08-24 | Bardy Diagnostics, Inc. | System and method for remote ECG data streaming in real-time |
US11696681B2 (en) | 2019-07-03 | 2023-07-11 | Bardy Diagnostics Inc. | Configurable hardware platform for physiological monitoring of a living body |
US11116451B2 (en) | 2019-07-03 | 2021-09-14 | Bardy Diagnostics, Inc. | Subcutaneous P-wave centric insertable cardiac monitor with energy harvesting capabilities |
US11045096B2 (en) | 2019-07-24 | 2021-06-29 | Francis Ndeithi | Finger blood monitor apparatus |
WO2021041961A1 (en) | 2019-08-28 | 2021-03-04 | Rhythm Diagnostic Systems, Inc. | Vital signs or health monitoring systems and methods |
EP3998938A4 (en) * | 2019-09-06 | 2022-10-05 | Valencell, Inc. | Wearable biometric waveform analysis systems and methods |
US11806138B2 (en) | 2019-09-27 | 2023-11-07 | Welch Allyn, Inc. | Patient photosensor |
US11278671B2 (en) | 2019-12-04 | 2022-03-22 | Icu Medical, Inc. | Infusion pump with safety sequence keypad |
US11510619B2 (en) | 2019-12-12 | 2022-11-29 | Jabil Inc. | Health and vital signs monitoring ring with integrated display and making of same |
US20210290170A1 (en) * | 2020-03-20 | 2021-09-23 | Hi Llc | Detection of Motion Artifacts in Signals Output by Detectors of a Wearable Optical Measurement System |
US11523766B2 (en) | 2020-06-25 | 2022-12-13 | Spacelabs Healthcare L.L.C. | Systems and methods of analyzing and displaying ambulatory ECG data |
AU2021311443A1 (en) | 2020-07-21 | 2023-03-09 | Icu Medical, Inc. | Fluid transfer devices and methods of use |
CN114073498A (en) * | 2020-08-13 | 2022-02-22 | 深圳迈瑞生物医疗电子股份有限公司 | Monitoring device and physiological parameter processing method thereof |
US11135360B1 (en) | 2020-12-07 | 2021-10-05 | Icu Medical, Inc. | Concurrent infusion with common line auto flush |
US12042280B2 (en) | 2021-01-25 | 2024-07-23 | Samsung Electronics Co., Ltd. | Apparatus and method for estimating oxygen saturation |
US11864921B2 (en) * | 2021-08-09 | 2024-01-09 | JMad Creations, LLC | Melanin-bias reducing pulse oximeter and patient monitoring systems and devices |
US20230147605A1 (en) * | 2021-11-05 | 2023-05-11 | Vital Connect, Inc. | Method, device, and system for blood oxygen saturation and vital sign measurements using a wearable biosensor |
US20230210417A1 (en) * | 2022-01-05 | 2023-07-06 | Masimo Corporation | Wrist and finger worn pulse oximetry system |
US20230225621A1 (en) * | 2022-01-20 | 2023-07-20 | Guy P. Curtis | System and method for evaluating cardiac pumping function |
US11977025B2 (en) | 2022-07-07 | 2024-05-07 | Ge Infrastructure Technology Llc | System and method for on-board data analysis for multi-gas sensing |
Citations (90)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4086916A (en) * | 1975-09-19 | 1978-05-02 | Joseph J. Cayre | Cardiac monitor wristwatch |
US4263918A (en) * | 1977-03-21 | 1981-04-28 | Biomega Corporation | Methods of and apparatus for the measurement of blood pressure |
US4270547A (en) * | 1978-10-03 | 1981-06-02 | University Patents, Inc. | Vital signs monitoring system |
US4305400A (en) * | 1979-10-15 | 1981-12-15 | Squibb Vitatek Inc. | Respiration monitoring method and apparatus including cardio-vascular artifact detection |
US4577639A (en) * | 1984-11-08 | 1986-03-25 | Spacelabs, Inc. | Apparatus and method for automatic lead selection in electrocardiography |
US4582068A (en) * | 1981-12-21 | 1986-04-15 | American Home Products Corporation | Systems and methods for processing physiological signals |
US4653498A (en) * | 1982-09-13 | 1987-03-31 | Nellcor Incorporated | Pulse oximeter monitor |
US4710164A (en) * | 1984-05-01 | 1987-12-01 | Henry Ford Hospital | Automated hemodialysis control based upon patient blood pressure and heart rate |
US4722351A (en) * | 1981-12-21 | 1988-02-02 | American Home Products Corporation | Systems and methods for processing physiological signals |
US4802486A (en) * | 1985-04-01 | 1989-02-07 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4807638A (en) * | 1987-10-21 | 1989-02-28 | Bomed Medical Manufacturing, Ltd. | Noninvasive continuous mean arterial blood prssure monitor |
US4905697A (en) * | 1989-02-14 | 1990-03-06 | Cook Pacemaker Corporation | Temperature-controlled cardiac pacemaker responsive to body motion |
US5025791A (en) * | 1989-06-28 | 1991-06-25 | Colin Electronics Co., Ltd. | Pulse oximeter with physical motion sensor |
US5140990A (en) * | 1990-09-06 | 1992-08-25 | Spacelabs, Inc. | Method of measuring blood pressure with a photoplethysmograph |
US5190038A (en) * | 1989-11-01 | 1993-03-02 | Novametrix Medical Systems, Inc. | Pulse oximeter with improved accuracy and response time |
US5197489A (en) * | 1991-06-17 | 1993-03-30 | Precision Control Design, Inc. | Activity monitoring apparatus with configurable filters |
US5224928A (en) * | 1983-08-18 | 1993-07-06 | Drug Delivery Systems Inc. | Mounting system for transdermal drug applicator |
US5247931A (en) * | 1991-09-16 | 1993-09-28 | Mine Safety Appliances Company | Diagnostic sensor clasp utilizing a slot, pivot and spring hinge mechanism |
US5289824A (en) * | 1991-12-26 | 1994-03-01 | Instromedix, Inc. | Wrist-worn ECG monitor |
US5316008A (en) * | 1990-04-06 | 1994-05-31 | Casio Computer Co., Ltd. | Measurement of electrocardiographic wave and sphygmus |
US5339818A (en) * | 1989-09-20 | 1994-08-23 | University Of Utah Research Foundation | Method for determining blood pressure utilizing a neural network |
US5465082A (en) * | 1990-07-27 | 1995-11-07 | Executone Information Systems, Inc. | Apparatus for automating routine communication in a facility |
US5482036A (en) * | 1991-03-07 | 1996-01-09 | Masimo Corporation | Signal processing apparatus and method |
US5485838A (en) * | 1992-12-07 | 1996-01-23 | Nihon Kohden Corporation | Non-invasive blood pressure measurement device |
US5490505A (en) * | 1991-03-07 | 1996-02-13 | Masimo Corporation | Signal processing apparatus |
US5515858A (en) * | 1992-02-28 | 1996-05-14 | Myllymaeki; Matti | Wrist-held monitoring device for physical condition |
US5517988A (en) * | 1993-12-11 | 1996-05-21 | Hewlett-Packard Company | Method for detecting an irregular state in a non-invasive pulse oximeter system |
US5524637A (en) * | 1994-06-29 | 1996-06-11 | Erickson; Jon W. | Interactive system for measuring physiological exertion |
US5549650A (en) * | 1994-06-13 | 1996-08-27 | Pacesetter, Inc. | System and method for providing hemodynamically optimal pacing therapy |
US5575284A (en) * | 1994-04-01 | 1996-11-19 | University Of South Florida | Portable pulse oximeter |
US5588427A (en) * | 1995-11-20 | 1996-12-31 | Spacelabs Medical, Inc. | Enhancement of physiological signals using fractal analysis |
US5593431A (en) * | 1995-03-30 | 1997-01-14 | Medtronic, Inc. | Medical service employing multiple DC accelerometers for patient activity and posture sensing and method |
US5632272A (en) * | 1991-03-07 | 1997-05-27 | Masimo Corporation | Signal processing apparatus |
US5645060A (en) * | 1995-06-14 | 1997-07-08 | Nellcor Puritan Bennett Incorporated | Method and apparatus for removing artifact and noise from pulse oximetry |
US5649543A (en) * | 1994-06-06 | 1997-07-22 | Nihon Kohden Corporation | Pulse-wave propagation time basis blood pressure monitor |
US5680870A (en) * | 1995-01-04 | 1997-10-28 | Johnson & Johnson Medical, Inc. | Oscillometric blood pressure monitor which acquires blood pressure signals from composite arterial pulse signal |
US5709205A (en) * | 1994-08-23 | 1998-01-20 | Hewlett-Packard Company | Pulsoximetry sensor |
US5743856A (en) * | 1995-11-06 | 1998-04-28 | Colin Corporation | Apparatus for measuring pulse-wave propagation velocity |
US5766131A (en) * | 1995-08-04 | 1998-06-16 | Seiko Epson Corporation | Pulse-wave measuring apparatus |
US5800349A (en) * | 1996-10-15 | 1998-09-01 | Nonin Medical, Inc. | Offset pulse oximeter sensor |
US5848373A (en) * | 1994-06-24 | 1998-12-08 | Delorme Publishing Company | Computer aided map location system |
US5853370A (en) * | 1996-09-13 | 1998-12-29 | Non-Invasive Technology, Inc. | Optical system and method for non-invasive imaging of biological tissue |
US5857975A (en) * | 1996-10-11 | 1999-01-12 | Dxtek, Inc. | Method and apparatus for non-invasive, cuffless continuous blood pressure determination |
US5865756A (en) * | 1997-06-06 | 1999-02-02 | Southwest Research Institute | System and method for identifying and correcting abnormal oscillometric pulse waves |
US5873834A (en) * | 1994-11-15 | 1999-02-23 | Omron Corporation | Blood pressure detecting device |
US5876353A (en) * | 1997-01-31 | 1999-03-02 | Medtronic, Inc. | Impedance monitor for discerning edema through evaluation of respiratory rate |
US5895359A (en) * | 1997-06-06 | 1999-04-20 | Southwest Research Institute | System and method for correcting a living subject's measured blood pressure |
US5899855A (en) * | 1992-11-17 | 1999-05-04 | Health Hero Network, Inc. | Modular microprocessor-based health monitoring system |
US5913827A (en) * | 1993-03-19 | 1999-06-22 | Gorman; Peter Gregory | Personal monitor and method for monitoring a biomedical condition in the presence of interference |
US5964720A (en) * | 1996-11-29 | 1999-10-12 | Adaptivity Devices Ltd. | Method and system for monitoring the physiological condition of a patient |
US6159147A (en) * | 1997-02-28 | 2000-12-12 | Qrs Diagnostics, Llc | Personal computer card for collection of real-time biological data |
US20020032386A1 (en) * | 2000-04-17 | 2002-03-14 | Sackner Marvin A. | Systems and methods for ambulatory monitoring of physiological signs |
US6388240B2 (en) * | 1999-08-26 | 2002-05-14 | Masimo Corporation | Shielded optical probe and method having a longevity indication |
US6480729B2 (en) * | 2000-04-28 | 2002-11-12 | Alexander K. Mills | Method for determining blood constituents |
US6541756B2 (en) * | 1991-03-21 | 2003-04-01 | Masimo Corporation | Shielded optical probe having an electrical connector |
US6595929B2 (en) * | 2001-03-30 | 2003-07-22 | Bodymedia, Inc. | System for monitoring health, wellness and fitness having a method and apparatus for improved measurement of heat flow |
US6684090B2 (en) * | 1999-01-07 | 2004-01-27 | Masimo Corporation | Pulse oximetry data confidence indicator |
US20040054821A1 (en) * | 2000-08-22 | 2004-03-18 | Warren Christopher E. | Multifunctional network interface node |
US6811538B2 (en) * | 2000-12-29 | 2004-11-02 | Ares Medical, Inc. | Sleep apnea risk evaluation |
US6850787B2 (en) * | 2001-06-29 | 2005-02-01 | Masimo Laboratories, Inc. | Signal component processor |
US20050059870A1 (en) * | 2003-08-25 | 2005-03-17 | Aceti John Gregory | Processing methods and apparatus for monitoring physiological parameters using physiological characteristics present within an auditory canal |
US20050070773A1 (en) * | 1996-10-10 | 2005-03-31 | Nellcor Puritan Bennett Incorporated | Motion compatible sensor for non-invasive optical blood analysis |
US20050234317A1 (en) * | 2004-03-19 | 2005-10-20 | Kiani Massi E | Low power and personal pulse oximetry systems |
US20060047215A1 (en) * | 2004-09-01 | 2006-03-02 | Welch Allyn, Inc. | Combined sensor assembly |
US20060074321A1 (en) * | 2002-08-27 | 2006-04-06 | Kenji Kouchi | Vital sign display and its method |
US7048687B1 (en) * | 1999-04-14 | 2006-05-23 | Ob Scientific, Inc. | Limited use medical probe |
US20060142648A1 (en) * | 2003-01-07 | 2006-06-29 | Triage Data Networks | Wireless, internet-based, medical diagnostic system |
US20060173257A1 (en) * | 2005-01-31 | 2006-08-03 | Konica Minolta Sensing, Inc. | Sleep evaluation method, sleep evaluation system, operation program for sleep evaluation system, pulse oximeter, and sleep support system |
US20060252999A1 (en) * | 2005-05-03 | 2006-11-09 | Devaul Richard W | Method and system for wearable vital signs and physiology, activity, and environmental monitoring |
US20060265246A1 (en) * | 2005-05-10 | 2006-11-23 | Cardinal Health 303, Inc. | Medication safety system featuring a multiplexed RFID interrogator panel |
US7184809B1 (en) * | 2005-11-08 | 2007-02-27 | Woolsthorpe Technologies, Llc | Pulse amplitude indexing method and apparatus |
US7186966B2 (en) * | 1999-08-26 | 2007-03-06 | Masimo Corporation | Amount of use tracking device and method for medical product |
US7215987B1 (en) * | 2005-11-08 | 2007-05-08 | Woolsthorpe Technologies | Method and apparatus for processing signals reflecting physiological characteristics |
US7225007B2 (en) * | 2003-01-24 | 2007-05-29 | Masimo Corporation | Optical sensor including disposable and reusable elements |
US20070185393A1 (en) * | 2006-02-03 | 2007-08-09 | Triage Wireless, Inc. | System for measuring vital signs using an optical module featuring a green light source |
US20070260487A1 (en) * | 2006-05-06 | 2007-11-08 | Irody Inc | System and method for real time management of a drug regimen |
US20070265533A1 (en) * | 2006-05-12 | 2007-11-15 | Bao Tran | Cuffless blood pressure monitoring appliance |
US7355512B1 (en) * | 2002-01-24 | 2008-04-08 | Masimo Corporation | Parallel alarm processor |
US7373191B2 (en) * | 1996-03-05 | 2008-05-13 | Nellcor Puritan Bennett Inc. | Shunt barrier in pulse oximeter sensor |
US7373912B2 (en) * | 2006-06-07 | 2008-05-20 | Ford Global Technologies, Llc | Oil level indicating system for internal combustion engine |
US7377794B2 (en) * | 2005-03-01 | 2008-05-27 | Masimo Corporation | Multiple wavelength sensor interconnect |
US7383070B2 (en) * | 1991-03-07 | 2008-06-03 | Masimo Corporation | Signal processing apparatus |
US7400919B2 (en) * | 2004-02-25 | 2008-07-15 | Nellcor Puritan Bennett Llc | Oximeter ambient light cancellation |
US20080214963A1 (en) * | 2003-07-02 | 2008-09-04 | Commissariat A L'energie Atomique | Method for measuring movements of a person wearing a portable detector |
US7477143B2 (en) * | 2004-07-23 | 2009-01-13 | Innovalarm Corporation | Enhanced personal monitoring and alarm response method and system |
US7530949B2 (en) * | 1999-01-25 | 2009-05-12 | Masimo Corporation | Dual-mode pulse oximeter |
US20090131759A1 (en) * | 2003-11-04 | 2009-05-21 | Nathaniel Sims | Life sign detection and health state assessment system |
US7725147B2 (en) * | 2005-09-29 | 2010-05-25 | Nellcor Puritan Bennett Llc | System and method for removing artifacts from waveforms |
US20100210930A1 (en) * | 2009-02-13 | 2010-08-19 | Saylor Stephen D | Physiological Blood Gas Detection Apparatus and Method |
US7983933B2 (en) * | 2006-12-06 | 2011-07-19 | Microsoft Corporation | Patient monitoring via image capture |
Family Cites Families (334)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4367752A (en) * | 1980-04-30 | 1983-01-11 | Biotechnology, Inc. | Apparatus for testing physical condition of a subject |
GB2161476B (en) | 1984-05-25 | 1988-01-27 | Toyama Chemical Co Ltd | 2-aminothiazolyl-2-methoxyimino acetamides and their use in preparing cephalosporins |
EP0443267A1 (en) | 1990-02-23 | 1991-08-28 | Sentinel Monitoring, Inc. | Method and apparatus for continuous non-invasive blood pressure monitoring |
US5226398A (en) * | 1990-03-08 | 1993-07-13 | Siemens Automotive Limited | Regulated flow canister purge system |
DE4131133B4 (en) | 1991-09-19 | 2005-09-08 | Robert Bosch Gmbh | Method and device for exchanging data in data processing systems |
US6471872B2 (en) | 1991-10-11 | 2002-10-29 | Children's Hospital Medical Center | Hemofiltration system and method based on monitored patient parameters |
US6968375B1 (en) | 1997-03-28 | 2005-11-22 | Health Hero Network, Inc. | Networked system for interactive communication and remote monitoring of individuals |
EP0683641A4 (en) * | 1993-08-24 | 1998-07-15 | Mark R Robinson | A robust accurate non-invasive analyte monitor. |
US5435315A (en) | 1994-01-28 | 1995-07-25 | Mcphee; Ron J. | Physical fitness evalution system |
JP2764702B2 (en) | 1994-03-30 | 1998-06-11 | 日本光電工業株式会社 | Blood pressure monitoring device |
US5734856A (en) * | 1994-04-05 | 1998-03-31 | Seiko Epson Corporation | System and method for generating supplemental ready signals to eliminate wasted cycles between operations |
US6371921B1 (en) | 1994-04-15 | 2002-04-16 | Masimo Corporation | System and method of determining whether to recalibrate a blood pressure monitor |
US5919141A (en) | 1994-11-15 | 1999-07-06 | Life Sensing Instrument Company, Inc. | Vital sign remote monitoring device |
US5577508A (en) * | 1995-01-13 | 1996-11-26 | Johnson & Johnson Medical, Inc. | Determination of oscillometric blood pressure by linear approximation |
US6041783A (en) | 1995-06-07 | 2000-03-28 | Nellcor Puritan Bennett Corporation | Integrated activity sensor |
US5944659A (en) | 1995-11-13 | 1999-08-31 | Vitalcom Inc. | Architecture for TDMA medical telemetry system |
US6790178B1 (en) | 1999-09-24 | 2004-09-14 | Healthetech, Inc. | Physiological monitor and associated computation, display and communication unit |
WO1997047239A1 (en) | 1996-06-12 | 1997-12-18 | Seiko Epson Corporation | Consumed calorie measuring apparatus and body temperature measuring apparatus |
US5941836A (en) | 1996-06-12 | 1999-08-24 | Friedman; Mark B. | Patient position monitor |
US6027452A (en) | 1996-06-26 | 2000-02-22 | Vital Insite, Inc. | Rapid non-invasive blood pressure measuring device |
DE69736622T2 (en) * | 1996-07-03 | 2007-09-13 | Hitachi, Ltd. | Motion detection system |
JP3627243B2 (en) | 1996-09-10 | 2005-03-09 | セイコーエプソン株式会社 | Biological condition measuring device and relaxation instruction device |
KR20000052757A (en) | 1996-10-24 | 2000-08-25 | 자르밀라 제트. 흐르벡 | Patient monitoring finger ring sensor |
US6198394B1 (en) | 1996-12-05 | 2001-03-06 | Stephen C. Jacobsen | System for remote monitoring of personnel |
US6002952A (en) | 1997-04-14 | 1999-12-14 | Masimo Corporation | Signal processing apparatus and method |
US6732064B1 (en) | 1997-07-02 | 2004-05-04 | Nonlinear Solutions, Inc. | Detection and classification system for analyzing deterministic properties of data using correlation parameters |
US6262769B1 (en) | 1997-07-31 | 2001-07-17 | Flashpoint Technology, Inc. | Method and system for auto rotating a graphical user interface for managing portrait and landscape images in an image capture unit |
US7383069B2 (en) * | 1997-08-14 | 2008-06-03 | Sensys Medical, Inc. | Method of sample control and calibration adjustment for use with a noninvasive analyzer |
US6198951B1 (en) | 1997-09-05 | 2001-03-06 | Seiko Epson Corporation | Reflection photodetector and biological information measuring instrument |
GB2329250A (en) | 1997-09-11 | 1999-03-17 | Kevin Doughty | Non-intrusive electronic determination of the orientation and presence of a person or infant in a bed, cot or chair using primary and secondary coils |
US5971930A (en) * | 1997-10-17 | 1999-10-26 | Siemens Medical Systems, Inc. | Method and apparatus for removing artifact from physiological signals |
EP1041923A1 (en) | 1997-12-22 | 2000-10-11 | BTG INTERNATIONAL LIMITED (Company No. 2664412) | Artefact reduction in photoplethysmography |
US7299159B2 (en) | 1998-03-03 | 2007-11-20 | Reuven Nanikashvili | Health monitor system and method for health monitoring |
US7542878B2 (en) | 1998-03-03 | 2009-06-02 | Card Guard Scientific Survival Ltd. | Personal health monitor and a method for health monitoring |
JPH11318841A (en) | 1998-03-09 | 1999-11-24 | Nippon Colin Co Ltd | Blood pressure monitoring device |
JP3109590B2 (en) | 1998-05-15 | 2000-11-20 | 日本電気株式会社 | Method for manufacturing semiconductor device |
US6057758A (en) | 1998-05-20 | 2000-05-02 | Hewlett-Packard Company | Handheld clinical terminal |
US6094592A (en) * | 1998-05-26 | 2000-07-25 | Nellcor Puritan Bennett, Inc. | Methods and apparatus for estimating a physiological parameter using transforms |
EP1082050B1 (en) | 1998-06-03 | 2011-08-24 | Masimo Corporation | Stereo pulse oximeter |
DE19832361A1 (en) | 1998-07-20 | 2000-02-03 | Noehte Steffen | Body function monitor measures bodily conditions, determines environmental stresses, pauses and computes probabilities, before pronouncing on criticality with high confidence level |
US6176831B1 (en) | 1998-07-20 | 2001-01-23 | Tensys Medical, Inc. | Apparatus and method for non-invasively monitoring a subject's arterial blood pressure |
US6199550B1 (en) | 1998-08-14 | 2001-03-13 | Bioasyst, L.L.C. | Integrated physiologic sensor system |
AU1198100A (en) | 1998-09-23 | 2000-04-10 | Keith Bridger | Physiological sensing device |
JP3840818B2 (en) | 1998-10-14 | 2006-11-01 | オムロンヘルスケア株式会社 | Blood pressure monitoring device |
US6160478A (en) | 1998-10-27 | 2000-12-12 | Sarcos Lc | Wireless health monitoring system |
US20030158699A1 (en) | 1998-12-09 | 2003-08-21 | Christopher P. Townsend | Orientation sensor |
US6168569B1 (en) | 1998-12-22 | 2001-01-02 | Mcewen James Allen | Apparatus and method for relating pain and activity of a patient |
US6398727B1 (en) | 1998-12-23 | 2002-06-04 | Baxter International Inc. | Method and apparatus for providing patient care |
US6261247B1 (en) | 1998-12-31 | 2001-07-17 | Ball Semiconductor, Inc. | Position sensing system |
US6117077A (en) | 1999-01-22 | 2000-09-12 | Del Mar Medical Systems, Llc | Long-term, ambulatory physiological recorder |
WO2000042911A1 (en) | 1999-01-25 | 2000-07-27 | Masimo Corporation | Universal/upgrading pulse oximeter |
WO2000064338A2 (en) * | 1999-04-23 | 2000-11-02 | Massachusetts Institute Of Technology | Isolating ring sensor design |
US6251080B1 (en) | 1999-05-13 | 2001-06-26 | Del Mar Medical Systems, Llc | Self contained ambulatory blood pressure cincture |
US7454359B2 (en) | 1999-06-23 | 2008-11-18 | Visicu, Inc. | System and method for displaying a health status of hospitalized patients |
IL130818A (en) | 1999-07-06 | 2005-07-25 | Intercure Ltd | Interventive-diagnostic device |
US7628730B1 (en) | 1999-07-08 | 2009-12-08 | Icon Ip, Inc. | Methods and systems for controlling an exercise apparatus using a USB compatible portable remote device |
EP1204367B1 (en) | 1999-07-21 | 2007-04-18 | Daniel David | Physiological measuring system comprising a garment in the form of a sleeve or glove and sensing apparatus incorporated in the garment |
US7145461B2 (en) | 2001-01-31 | 2006-12-05 | Ilife Solutions, Inc. | System and method for analyzing activity of a body |
US20010013826A1 (en) | 1999-09-24 | 2001-08-16 | Kavlico Corporation | Versatile smart networkable sensor |
MXPA02003412A (en) | 1999-10-07 | 2004-09-10 | K Mills Alexander | Method and apparatus for non invasive continuous determination of physiological parameters of a patient s blood. |
US6527729B1 (en) | 1999-11-10 | 2003-03-04 | Pacesetter, Inc. | Method for monitoring patient using acoustic sensor |
JP2001149349A (en) | 1999-11-26 | 2001-06-05 | Nippon Koden Corp | Sensor for living body |
US6950687B2 (en) | 1999-12-09 | 2005-09-27 | Masimo Corporation | Isolation and communication element for a resposable pulse oximetry sensor |
US7156809B2 (en) | 1999-12-17 | 2007-01-02 | Q-Tec Systems Llc | Method and apparatus for health and disease management combining patient data monitoring with wireless internet connectivity |
US6976958B2 (en) | 2000-12-15 | 2005-12-20 | Q-Tec Systems Llc | Method and apparatus for health and disease management combining patient data monitoring with wireless internet connectivity |
US7296312B2 (en) | 2002-09-06 | 2007-11-20 | Hill-Rom Services, Inc. | Hospital bed |
JP2001190526A (en) | 2000-01-07 | 2001-07-17 | Minolta Co Ltd | Posture detecting device and respiratory function measuring device |
DE60139457D1 (en) | 2000-02-02 | 2009-09-17 | Gen Hospital Corp | UNGEN USING A TISSUE HAZARD CARD |
US6893396B2 (en) | 2000-03-01 | 2005-05-17 | I-Medik, Inc. | Wireless internet bio-telemetry monitoring system and interface |
US6443890B1 (en) | 2000-03-01 | 2002-09-03 | I-Medik, Inc. | Wireless internet bio-telemetry monitoring system |
JP3846844B2 (en) | 2000-03-14 | 2006-11-15 | 株式会社東芝 | Body-mounted life support device |
US6419649B1 (en) | 2000-04-05 | 2002-07-16 | Eric A. Klein | Erotic stimulation device |
FR2808609B1 (en) | 2000-05-05 | 2006-02-10 | Univ Rennes | DEVICE AND METHOD FOR DETECTING ABNORMAL SITUATIONS |
US6533729B1 (en) | 2000-05-10 | 2003-03-18 | Motorola Inc. | Optical noninvasive blood pressure sensor and method |
JP4318062B2 (en) | 2000-05-16 | 2009-08-19 | 日本光電工業株式会社 | Biological signal monitor |
US6616606B1 (en) | 2000-05-19 | 2003-09-09 | Welch Allyn Protocol, Inc. | Patient monitoring system |
US6645155B2 (en) | 2000-05-26 | 2003-11-11 | Colin Corporation | Blood pressure monitor apparatus |
US7485095B2 (en) | 2000-05-30 | 2009-02-03 | Vladimir Shusterman | Measurement and analysis of trends in physiological and/or health data |
US7689437B1 (en) | 2000-06-16 | 2010-03-30 | Bodymedia, Inc. | System for monitoring health, wellness and fitness |
US6605038B1 (en) | 2000-06-16 | 2003-08-12 | Bodymedia, Inc. | System for monitoring health, wellness and fitness |
WO2002016905A2 (en) * | 2000-08-21 | 2002-02-28 | Euro-Celtique, S.A. | Near infrared blood glucose monitoring system |
JP3784630B2 (en) | 2000-10-06 | 2006-06-14 | 株式会社総合医科学研究所 | Mental examination method and mental function examination apparatus |
US6834436B2 (en) * | 2001-02-23 | 2004-12-28 | Microstrain, Inc. | Posture and body movement measuring system |
JP2002253519A (en) | 2001-03-01 | 2002-09-10 | Nippon Koden Corp | Method for measuring blood quantity, and living body signal monitoring device |
US6526310B1 (en) | 2001-03-02 | 2003-02-25 | Ge Medical Systems Information Technologies, Inc. | Patient transceiver system which uses conductors within leads of leadset to provide phased antenna array |
JP4592991B2 (en) * | 2001-03-28 | 2010-12-08 | 古河電気工業株式会社 | Seat belt connecting member |
US20020156354A1 (en) | 2001-04-20 | 2002-10-24 | Larson Eric Russell | Pulse oximetry sensor with improved spring |
US6694177B2 (en) | 2001-04-23 | 2004-02-17 | Cardionet, Inc. | Control of data transmission between a remote monitoring unit and a central unit |
JP3578727B2 (en) | 2001-04-27 | 2004-10-20 | コーリンメディカルテクノロジー株式会社 | Blood pressure waveform monitor |
JP2003047601A (en) | 2001-05-31 | 2003-02-18 | Denso Corp | Organism abnormality monitoring system, blood pressure monitoring system, organism abnormality monitoring method and blood pressure monitoring method |
ES2386003T3 (en) | 2001-06-22 | 2012-08-07 | Invensys Systems, Inc. | Procedure and system to collect and retrieve time series data in real time and in no real time |
JP3495348B2 (en) | 2001-07-02 | 2004-02-09 | 日本コーリン株式会社 | Pulse wave velocity information measurement device |
US7257438B2 (en) | 2002-07-23 | 2007-08-14 | Datascope Investment Corp. | Patient-worn medical monitoring device |
US6503206B1 (en) | 2001-07-27 | 2003-01-07 | Vsm Medtech Ltd | Apparatus having redundant sensors for continuous monitoring of vital signs and related methods |
US20030107487A1 (en) | 2001-12-10 | 2003-06-12 | Ronen Korman | Method and device for measuring physiological parameters at the wrist |
GB0130010D0 (en) | 2001-12-14 | 2002-02-06 | Isis Innovation | Combining measurements from breathing rate sensors |
US6997882B1 (en) | 2001-12-21 | 2006-02-14 | Barron Associates, Inc. | 6-DOF subject-monitoring device and method |
US6912414B2 (en) | 2002-01-29 | 2005-06-28 | Southwest Research Institute | Electrode systems and methods for reducing motion artifact |
US8010174B2 (en) | 2003-08-22 | 2011-08-30 | Dexcom, Inc. | Systems and methods for replacing signal artifacts in a glucose sensor data stream |
US7497827B2 (en) | 2004-07-13 | 2009-03-03 | Dexcom, Inc. | Transcutaneous analyte sensor |
US6648828B2 (en) | 2002-03-01 | 2003-11-18 | Ge Medical Systems Information Technologies, Inc. | Continuous, non-invasive technique for measuring blood pressure using impedance plethysmography |
US20030171662A1 (en) | 2002-03-07 | 2003-09-11 | O'connor Michael William | Non-adhesive flexible electro-optical sensor for fingertip trans-illumination |
US7022070B2 (en) | 2002-03-22 | 2006-04-04 | Mini-Mitter Co., Inc. | Method for continuous monitoring of patients to detect the potential onset of sepsis |
US8790272B2 (en) | 2002-03-26 | 2014-07-29 | Adidas Ag | Method and system for extracting cardiac parameters from plethysmographic signals |
EP1489964A1 (en) * | 2002-04-01 | 2004-12-29 | Aspect Medical Systems, Inc. | System and method of assessment of arousal, pain and stress during anesthesia and sedation |
US7079888B2 (en) | 2002-04-11 | 2006-07-18 | Ansar, Inc. | Method and apparatus for monitoring the autonomic nervous system using non-stationary spectral analysis of heart rate and respiratory activity |
GB2389419B (en) | 2002-06-05 | 2006-02-22 | Diabetes Diagnostics Inc | Analyte testing device |
EP1388321A1 (en) | 2002-08-09 | 2004-02-11 | Instrumentarium Oyj | Method and system for continuous and non-invasive blood pressure measurement |
US6879850B2 (en) | 2002-08-16 | 2005-04-12 | Optical Sensors Incorporated | Pulse oximeter with motion detection |
US6763256B2 (en) | 2002-08-16 | 2004-07-13 | Optical Sensors, Inc. | Pulse oximeter |
US7020508B2 (en) | 2002-08-22 | 2006-03-28 | Bodymedia, Inc. | Apparatus for detecting human physiological and contextual information |
US20070100666A1 (en) | 2002-08-22 | 2007-05-03 | Stivoric John M | Devices and systems for contextual and physiological-based detection, monitoring, reporting, entertainment, and control of other devices |
US20040116969A1 (en) | 2002-08-26 | 2004-06-17 | Owen James M. | Pulse detection using patient physiological signals |
EP1536730A2 (en) * | 2002-09-10 | 2005-06-08 | Euro-Celtique, S.A. | Apparatus and method for non-invasive measurement of blood constituents |
US7118531B2 (en) | 2002-09-24 | 2006-10-10 | The Johns Hopkins University | Ingestible medical payload carrying capsule with wireless communication |
US7226422B2 (en) | 2002-10-09 | 2007-06-05 | Cardiac Pacemakers, Inc. | Detection of congestion from monitoring patient response to a recumbent position |
US6947781B2 (en) | 2002-12-13 | 2005-09-20 | Massachusetts Institute Of Technology | Vibratory venous and arterial oximetry sensor |
US20040133079A1 (en) | 2003-01-02 | 2004-07-08 | Mazar Scott Thomas | System and method for predicting patient health within a patient management system |
JP3760920B2 (en) | 2003-02-28 | 2006-03-29 | 株式会社デンソー | Sensor |
EP1606758B1 (en) | 2003-03-21 | 2015-11-18 | Welch Allyn, Inc. | Personal status physiologic monitor system |
AU2004229488B2 (en) | 2003-04-10 | 2011-07-14 | Adidas Ag | Systems and methods for respiratory event detection |
KR100571811B1 (en) | 2003-05-09 | 2006-04-17 | 삼성전자주식회사 | Ear type measurement apparatus for bio signal |
US20040267099A1 (en) | 2003-06-30 | 2004-12-30 | Mcmahon Michael D. | Pain assessment user interface |
US7455643B1 (en) | 2003-07-07 | 2008-11-25 | Nellcor Puritan Bennett Ireland | Continuous non-invasive blood pressure measurement apparatus and methods providing automatic recalibration |
US9135402B2 (en) | 2007-12-17 | 2015-09-15 | Dexcom, Inc. | Systems and methods for processing sensor data |
AU2003904336A0 (en) | 2003-08-15 | 2003-08-28 | Medcare Systems Pty Ltd | An automated personal alarm monitor |
US7678061B2 (en) | 2003-09-18 | 2010-03-16 | Cardiac Pacemakers, Inc. | System and method for characterizing patient respiration |
BRPI0414345A (en) | 2003-09-12 | 2006-11-07 | Bodymedia Inc | method and apparatus for measuring heart-related parameters |
JP2007510504A (en) | 2003-11-12 | 2007-04-26 | ドレーガー メディカル システムズ インコーポレイテッド | HEALTHCARE TREATMENT DEVICE AND DISPLAY SYSTEM |
WO2005048824A2 (en) | 2003-11-18 | 2005-06-02 | Vivometrics, Inc. | Method and system for processing data from ambulatory physiological monitoring |
US7129891B2 (en) | 2003-11-21 | 2006-10-31 | Xerox Corporation | Method for determining proximity of devices in a wireless network |
US7220230B2 (en) | 2003-12-05 | 2007-05-22 | Edwards Lifesciences Corporation | Pressure-based system and method for determining cardiac stroke volume |
JP4296570B2 (en) | 2003-12-08 | 2009-07-15 | 日本光電工業株式会社 | Vital telemeter |
US20050149350A1 (en) | 2003-12-24 | 2005-07-07 | Kerr Roger S. | Patient information management system and method |
US7301451B2 (en) | 2003-12-31 | 2007-11-27 | Ge Medical Systems Information Technologies, Inc. | Notification alarm transfer methods, system, and device |
US7314451B2 (en) | 2005-04-25 | 2008-01-01 | Earlysense Ltd. | Techniques for prediction and monitoring of clinical episodes |
US7194293B2 (en) | 2004-03-08 | 2007-03-20 | Nellcor Puritan Bennett Incorporated | Selection of ensemble averaging weights for a pulse oximeter based on signal quality metrics |
US7491181B2 (en) | 2004-03-16 | 2009-02-17 | Medtronic, Inc. | Collecting activity and sleep quality information via a medical device |
US20050216199A1 (en) | 2004-03-26 | 2005-09-29 | Triage Data Networks | Cuffless blood-pressure monitor and accompanying web services interface |
US7570979B2 (en) * | 2004-03-30 | 2009-08-04 | Philip George Cooper | Methods and apparatus for patient monitoring |
JP3987053B2 (en) | 2004-03-30 | 2007-10-03 | 株式会社東芝 | Sleep state determination device and sleep state determination method |
US7179228B2 (en) | 2004-04-07 | 2007-02-20 | Triage Wireless, Inc. | Cuffless system for measuring blood pressure |
US20050261598A1 (en) | 2004-04-07 | 2005-11-24 | Triage Wireless, Inc. | Patch sensor system for measuring vital signs |
US20050228297A1 (en) | 2004-04-07 | 2005-10-13 | Banet Matthew J | Wrist-worn System for Measuring Blood Pressure |
US7238159B2 (en) | 2004-04-07 | 2007-07-03 | Triage Wireless, Inc. | Device, system and method for monitoring vital signs |
US20060009698A1 (en) | 2004-04-07 | 2006-01-12 | Triage Wireless, Inc. | Hand-held monitor for measuring vital signs |
US20060009697A1 (en) | 2004-04-07 | 2006-01-12 | Triage Wireless, Inc. | Wireless, internet-based system for measuring vital signs from a plurality of patients in a hospital or medical clinic |
US20050228300A1 (en) | 2004-04-07 | 2005-10-13 | Triage Data Networks | Cuffless blood-pressure monitor and accompanying wireless mobile device |
US7004907B2 (en) | 2004-04-07 | 2006-02-28 | Triage Wireless, Inc. | Blood-pressure monitoring device featuring a calibration-based analysis |
EP1750585A1 (en) | 2004-05-16 | 2007-02-14 | Medic4all AG | Method and device for measuring physiological parameters at the hand |
US8199685B2 (en) | 2004-05-17 | 2012-06-12 | Sonosite, Inc. | Processing of medical signals |
US20050261565A1 (en) | 2004-05-18 | 2005-11-24 | Micron Medical Products | Discretely coated sensor for use in medical electrodes |
CN1698536A (en) | 2004-05-20 | 2005-11-23 | 香港中文大学 | Cuff-less type blood pressure continuous measuring method using automatic compensation |
US20080162496A1 (en) | 2004-06-02 | 2008-07-03 | Richard Postrel | System and method for centralized management and monitoring of healthcare services |
US20050270903A1 (en) | 2004-06-04 | 2005-12-08 | Schlumberger Technology Corporation | Method for continuous interpretation of monitoring data |
US7261697B2 (en) | 2004-06-16 | 2007-08-28 | Bernstein Donald P | Apparatus for determination of stroke volume using the brachial artery |
US7509131B2 (en) | 2004-06-29 | 2009-03-24 | Microsoft Corporation | Proximity detection using wireless signal strengths |
US20060005169A1 (en) | 2004-06-30 | 2006-01-05 | International Business Machines Corporation | Software development system and method |
WO2006005169A1 (en) | 2004-07-09 | 2006-01-19 | Telemedic Inc | Vital sign monitoring system and method |
US7937128B2 (en) * | 2004-07-09 | 2011-05-03 | Masimo Corporation | Cyanotic infant sensor |
US7656287B2 (en) | 2004-07-23 | 2010-02-02 | Innovalarm Corporation | Alert system with enhanced waking capabilities |
US7173525B2 (en) | 2004-07-23 | 2007-02-06 | Innovalarm Corporation | Enhanced fire, safety, security and health monitoring and alarm response method, system and device |
US7115824B2 (en) | 2004-08-03 | 2006-10-03 | Kam Chun Lo | Tilt switch and system |
US9820658B2 (en) | 2006-06-30 | 2017-11-21 | Bao Q. Tran | Systems and methods for providing interoperability among healthcare devices |
US7468036B1 (en) | 2004-09-28 | 2008-12-23 | Impact Sports Technology, Inc. | Monitoring device, method and system |
US7887492B1 (en) * | 2004-09-28 | 2011-02-15 | Impact Sports Technologies, Inc. | Monitoring device, method and system |
US7625344B1 (en) | 2007-06-13 | 2009-12-01 | Impact Sports Technologies, Inc. | Monitoring device, method and system |
US7544168B2 (en) | 2004-09-30 | 2009-06-09 | Jerusalem College Of Technology | Measuring systolic blood pressure by photoplethysmography |
US20060084878A1 (en) | 2004-10-18 | 2006-04-20 | Triage Wireless, Inc. | Personal computer-based vital signs monitor |
US20080004507A1 (en) | 2004-10-27 | 2008-01-03 | E-Z-Em, Inc. | Data collection device, system, method, and computer program product for collecting data related to the dispensing of contrast media |
US20060122469A1 (en) | 2004-11-16 | 2006-06-08 | Martel Normand M | Remote medical monitoring system |
WO2006055917A2 (en) | 2004-11-19 | 2006-05-26 | Vivometrics, Inc. | Methods and systems for real time breath rate determination with limited processor resources |
US7578793B2 (en) | 2004-11-22 | 2009-08-25 | Widemed Ltd. | Sleep staging based on cardio-respiratory signals |
US20080146887A1 (en) | 2004-11-30 | 2008-06-19 | Rao Raman K | Intelligent personal health management appliances for external and internal visualization of the human anatomy and for dental/personal hygiene |
US7658716B2 (en) * | 2004-12-07 | 2010-02-09 | Triage Wireless, Inc. | Vital signs monitor using an optical ear-based module |
US7976480B2 (en) | 2004-12-09 | 2011-07-12 | Motorola Solutions, Inc. | Wearable auscultation system and method |
US20060128263A1 (en) | 2004-12-09 | 2006-06-15 | Baird John C | Computerized assessment system and method for assessing opinions or feelings |
WO2006069264A1 (en) | 2004-12-22 | 2006-06-29 | össur hf | Systems and methods for processing limb motion |
US20060155589A1 (en) | 2005-01-10 | 2006-07-13 | Welch Allyn, Inc. | Portable vital signs measurement instrument and method of use thereof |
EP1850734A4 (en) | 2005-01-13 | 2009-08-26 | Welch Allyn Inc | Vital signs monitor |
EP1688085A1 (en) | 2005-02-02 | 2006-08-09 | Disetronic Licensing AG | Ambulatory medical device and method of communication between medical devices |
US20060200029A1 (en) | 2005-03-04 | 2006-09-07 | Medwave, Inc. | Universal transportable vital signs monitor |
US7616110B2 (en) | 2005-03-11 | 2009-11-10 | Aframe Digital, Inc. | Mobile wireless customizable health and condition monitor |
US7827011B2 (en) | 2005-05-03 | 2010-11-02 | Aware, Inc. | Method and system for real-time signal classification |
WO2006124768A1 (en) | 2005-05-12 | 2006-11-23 | State Of Oregon Acting By And Through The State Board Of Higher Education On Behalf Of The University Of Oregon | Method and apparatus for blood pressure measurement and analysis |
US7925022B2 (en) | 2005-05-23 | 2011-04-12 | The Invention Science Fund I, Llc | Device pairing via device to device contact |
JP2006341062A (en) | 2005-06-09 | 2006-12-21 | Nex1 Future Co Ltd | Emergency sensor |
US7782189B2 (en) | 2005-06-20 | 2010-08-24 | Carestream Health, Inc. | System to monitor the ingestion of medicines |
US8121856B2 (en) | 2005-06-28 | 2012-02-21 | Hill-Rom Services, Inc. | Remote access to healthcare device diagnostic information |
US9504394B2 (en) | 2005-07-28 | 2016-11-29 | The General Hospital Corporation | Electro-optical system, apparatus, and method for ambulatory monitoring |
WO2007024777A2 (en) | 2005-08-22 | 2007-03-01 | Massachusetts Institute Of Technology | Wearable blood pressure sensor and method of calibration |
US7674231B2 (en) | 2005-08-22 | 2010-03-09 | Massachusetts Institute Of Technology | Wearable pulse wave velocity blood pressure sensor and methods of calibration thereof |
US7237446B2 (en) | 2005-09-16 | 2007-07-03 | Raymond Chan | System and method for measuring gait kinematics information |
JP4754915B2 (en) | 2005-09-21 | 2011-08-24 | フクダ電子株式会社 | Blood pressure monitoring device |
WO2007040963A2 (en) | 2005-09-29 | 2007-04-12 | Berkeley Heartlab, Inc. | Internet-based system for monitoring blood test, vital sign, and exercise information from a patient |
US7420472B2 (en) | 2005-10-16 | 2008-09-02 | Bao Tran | Patient monitoring apparatus |
US8706515B2 (en) | 2005-10-20 | 2014-04-22 | Mckesson Information Solutions Llc | Methods, systems, and apparatus for providing a notification of a message in a health care environment |
JP2007117591A (en) | 2005-10-31 | 2007-05-17 | Konica Minolta Sensing Inc | Pulse wave analyzer |
US8366641B2 (en) | 2005-11-18 | 2013-02-05 | Cardiac Pacemakers, Inc. | Posture detector calibration and use |
US20070129769A1 (en) | 2005-12-02 | 2007-06-07 | Medtronic, Inc. | Wearable ambulatory data recorder |
DE102005059435A1 (en) | 2005-12-13 | 2007-06-14 | Robert Bosch Gmbh | Device for noninvasive blood pressure measurement |
US7648463B1 (en) | 2005-12-15 | 2010-01-19 | Impact Sports Technologies, Inc. | Monitoring device, method and system |
US20070142715A1 (en) | 2005-12-20 | 2007-06-21 | Triage Wireless, Inc. | Chest strap for measuring vital signs |
US20070156456A1 (en) | 2006-01-04 | 2007-07-05 | Siemens Medical Solutions Health Services Corporation | System for Monitoring Healthcare Related Activity In A Healthcare Enterprise |
US20100201512A1 (en) | 2006-01-09 | 2010-08-12 | Harold Dan Stirling | Apparatus, systems, and methods for evaluating body movements |
US8109879B2 (en) | 2006-01-10 | 2012-02-07 | Cardiac Pacemakers, Inc. | Assessing autonomic activity using baroreflex analysis |
US7427926B2 (en) | 2006-01-26 | 2008-09-23 | Microsoft Corporation | Establishing communication between computing-based devices through motion detection |
MX2008010761A (en) | 2006-02-21 | 2009-01-21 | Adt Security Services Inc | System and method for remotely attended delivery. |
CN101400296B (en) | 2006-02-28 | 2012-11-07 | 皇家飞利浦电子股份有限公司 | Biometric monitor with electronics disposed on or in a neck collar |
US7668588B2 (en) | 2006-03-03 | 2010-02-23 | PhysioWave, Inc. | Dual-mode physiologic monitoring systems and methods |
US8200320B2 (en) | 2006-03-03 | 2012-06-12 | PhysioWave, Inc. | Integrated physiologic monitoring systems and methods |
CN101416194A (en) | 2006-03-30 | 2009-04-22 | 陶氏环球技术公司 | Method and system for monitoring and analyzing compliance with internal dosing regimen |
DE102006015291B4 (en) | 2006-04-01 | 2015-10-29 | Drägerwerk AG & Co. KGaA | Procedure for setting a patient monitor |
US20070270671A1 (en) | 2006-04-10 | 2007-11-22 | Vivometrics, Inc. | Physiological signal processing devices and associated processing methods |
GB0607270D0 (en) | 2006-04-11 | 2006-05-17 | Univ Nottingham | The pulsing blood supply |
US8886125B2 (en) | 2006-04-14 | 2014-11-11 | Qualcomm Incorporated | Distance-based association |
TW200740409A (en) | 2006-04-18 | 2007-11-01 | Wei-Kung Wang | A physiological signals monitor with digital real time calibration |
US7561960B2 (en) * | 2006-04-20 | 2009-07-14 | Honeywell International Inc. | Motion classification methods for personal navigation |
US20070252853A1 (en) | 2006-04-28 | 2007-11-01 | Samsung Electronics Co., Ltd. | Method and apparatus to control screen orientation of user interface of portable device |
US20070255126A1 (en) | 2006-04-28 | 2007-11-01 | Moberg Sheldon B | Data communication in networked fluid infusion systems |
US7558622B2 (en) | 2006-05-24 | 2009-07-07 | Bao Tran | Mesh network stroke monitoring appliance |
US7993275B2 (en) * | 2006-05-25 | 2011-08-09 | Sotera Wireless, Inc. | Bilateral device, system and method for monitoring vital signs |
US9149192B2 (en) | 2006-05-26 | 2015-10-06 | Sotera Wireless, Inc. | System for measuring vital signs using bilateral pulse transit time |
US8788023B2 (en) * | 2006-05-26 | 2014-07-22 | Cameron Health, Inc. | Systems and methods for sensing vector selection in an implantable medical device |
WO2007143535A2 (en) | 2006-06-01 | 2007-12-13 | Biancamed Ltd. | Apparatus, system, and method for monitoring physiological signs |
US20070282208A1 (en) | 2006-06-06 | 2007-12-06 | Bob Jacobs | Mobile computing device with integrated medical devices |
US7569019B2 (en) | 2006-06-16 | 2009-08-04 | Frank Bour | Analysis and use of cardiographic bioimpedance measurements |
US7783356B2 (en) | 2006-06-29 | 2010-08-24 | Cardiac Pacemakers, Inc. | Automated device programming at changeout |
JP2009544431A (en) | 2006-07-28 | 2009-12-17 | コーニンクレッカ フィリップス エレクトロニクス エヌ ヴィ | Automatic transfer and identification of monitoring data with hierarchical key management infrastructure |
EP2049012B1 (en) | 2006-08-02 | 2015-04-15 | Philips Intellectual Property & Standards GmbH | Sensor for detecting the passing of a pulse wave from a subject´s arterial system |
US8343049B2 (en) | 2006-08-24 | 2013-01-01 | Cardiac Pacemakers, Inc. | Physiological response to posture change |
US8442607B2 (en) | 2006-09-07 | 2013-05-14 | Sotera Wireless, Inc. | Hand-held vital signs monitor |
US20080082004A1 (en) | 2006-09-08 | 2008-04-03 | Triage Wireless, Inc. | Blood pressure monitor |
US7610085B2 (en) | 2006-09-12 | 2009-10-27 | Allgeyer Dean O | Simplified ECG monitoring system |
US20080076972A1 (en) | 2006-09-21 | 2008-03-27 | Apple Inc. | Integrated sensors for tracking performance metrics |
ES2298060B2 (en) | 2006-09-27 | 2009-09-03 | Universidad De Cadiz. | SYSTEM FOR MONITORING AND ANALYSIS OF CARDIORESPIRATORY AND RONQUID SIGNS. |
US8068891B2 (en) * | 2006-09-29 | 2011-11-29 | Nellcor Puritan Bennett Llc | Symmetric LED array for pulse oximetry |
US20080101160A1 (en) | 2006-11-01 | 2008-05-01 | Rodney Besson | Med Alert Watch |
US8449469B2 (en) | 2006-11-10 | 2013-05-28 | Sotera Wireless, Inc. | Two-part patch sensor for monitoring vital signs |
US20080221404A1 (en) | 2006-11-13 | 2008-09-11 | Shun-Wun Tso | Multifunction health apparatus |
US7586418B2 (en) | 2006-11-17 | 2009-09-08 | General Electric Company | Multifunctional personal emergency response system |
US8668651B2 (en) | 2006-12-05 | 2014-03-11 | Covidien Lp | ECG lead set and ECG adapter system |
DE102006057709B4 (en) | 2006-12-07 | 2015-04-02 | Dräger Medical GmbH | Apparatus and method for determining a respiratory rate |
US8157730B2 (en) | 2006-12-19 | 2012-04-17 | Valencell, Inc. | Physiological and environmental monitoring systems and methods |
US20090262074A1 (en) | 2007-01-05 | 2009-10-22 | Invensense Inc. | Controlling and accessing content using motion processing on mobile devices |
US20080171927A1 (en) | 2007-01-11 | 2008-07-17 | Health & Life Co., Ltd. | Physiological detector with a waterproof structure |
US8391786B2 (en) | 2007-01-25 | 2013-03-05 | Stephen Hodges | Motion triggered data transfer |
US20080194918A1 (en) | 2007-02-09 | 2008-08-14 | Kulik Robert S | Vital signs monitor with patient entertainment console |
JP4818162B2 (en) | 2007-02-27 | 2011-11-16 | パラマウントベッド株式会社 | Movable floor board type bed device |
US20080221399A1 (en) | 2007-03-05 | 2008-09-11 | Triage Wireless, Inc. | Monitor for measuring vital signs and rendering video images |
US7698101B2 (en) | 2007-03-07 | 2010-04-13 | Apple Inc. | Smart garment |
US7678060B1 (en) * | 2007-03-08 | 2010-03-16 | Millen Ernest W | Method of monitoring a state of health, and a wellness/emotional state monitor implementing the method |
US7541939B2 (en) | 2007-03-15 | 2009-06-02 | Apple Inc. | Mounted shock sensor |
GB0705033D0 (en) | 2007-03-15 | 2007-04-25 | Imp Innovations Ltd | Heart rate measurement |
US8047998B2 (en) | 2007-04-17 | 2011-11-01 | General Electric Company | Non-invasive blood pressure determination method |
WO2008135985A1 (en) | 2007-05-02 | 2008-11-13 | Earlysense Ltd | Monitoring, predicting and treating clinical episodes |
US8231614B2 (en) | 2007-05-11 | 2012-07-31 | Tyco Healthcare Group Lp | Temperature monitoring return electrode |
US8750971B2 (en) | 2007-05-24 | 2014-06-10 | Bao Tran | Wireless stroke monitoring |
US8602997B2 (en) | 2007-06-12 | 2013-12-10 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US8419649B2 (en) | 2007-06-12 | 2013-04-16 | Sotera Wireless, Inc. | Vital sign monitor for measuring blood pressure using optical, electrical and pressure waveforms |
US7530956B2 (en) | 2007-06-15 | 2009-05-12 | Cardiac Pacemakers, Inc. | Daytime/nighttime respiration rate monitoring |
US7698941B2 (en) | 2007-06-20 | 2010-04-20 | Headway Technologies, Inc. | Sensing unit and method of making same |
US7628071B2 (en) | 2007-06-20 | 2009-12-08 | Headway Techologies, Inc. | Sensing unit and method of making same |
US20080319327A1 (en) | 2007-06-25 | 2008-12-25 | Triage Wireless, Inc. | Body-worn sensor featuring a low-power processor and multi-sensor array for measuring blood pressure |
JP5060186B2 (en) | 2007-07-05 | 2012-10-31 | 株式会社東芝 | Pulse wave processing apparatus and method |
WO2009009761A1 (en) | 2007-07-11 | 2009-01-15 | Triage Wireless, Inc. | Device for determining respiratory rate and other vital signs |
US7825794B2 (en) | 2007-08-10 | 2010-11-02 | Integrity Tracking, Llc | Alzheimer's patient tracking system |
US8221290B2 (en) | 2007-08-17 | 2012-07-17 | Adidas International Marketing B.V. | Sports electronic training system with electronic gaming features, and applications thereof |
US20090054752A1 (en) | 2007-08-22 | 2009-02-26 | Motorola, Inc. | Method and apparatus for photoplethysmographic sensing |
US20090069642A1 (en) | 2007-09-11 | 2009-03-12 | Aid Networks, Llc | Wearable Wireless Electronic Patient Data Communications and Physiological Monitoring Device |
US8535522B2 (en) | 2009-02-12 | 2013-09-17 | Fresenius Medical Care Holdings, Inc. | System and method for detection of disconnection in an extracorporeal blood circuit |
US20090076397A1 (en) | 2007-09-14 | 2009-03-19 | Corventis, Inc. | Adherent Emergency Patient Monitor |
US8249686B2 (en) | 2007-09-14 | 2012-08-21 | Corventis, Inc. | Adherent device for sleep disordered breathing |
EP2194847A1 (en) | 2007-09-14 | 2010-06-16 | Corventis, Inc. | Adherent device with multiple physiological sensors |
JP2009072417A (en) | 2007-09-21 | 2009-04-09 | Toshiba Corp | Biological information processor and processing method |
US20090112072A1 (en) | 2007-10-26 | 2009-04-30 | Triage Wireless, Inc. | System that displays both vital sign information and entertainment content on a common video monitor |
US7983757B2 (en) | 2007-10-26 | 2011-07-19 | Medtronic, Inc. | Medical device configuration based on sensed brain signals |
US8082160B2 (en) | 2007-10-26 | 2011-12-20 | Hill-Rom Services, Inc. | System and method for collection and communication of data from multiple patient care devices |
US20090118626A1 (en) | 2007-11-01 | 2009-05-07 | Transoma Medical, Inc. | Calculating Respiration Parameters Using Impedance Plethysmography |
US20090124863A1 (en) | 2007-11-08 | 2009-05-14 | General Electric Company | Method and system for recording patient-status |
US8442608B2 (en) | 2007-12-28 | 2013-05-14 | Covidien Lp | System and method for estimating physiological parameters by deconvolving artifacts |
US7684954B2 (en) | 2007-12-31 | 2010-03-23 | Intel Corporation | Apparatus and method for classification of physical orientation |
DE102008003978A1 (en) | 2008-01-11 | 2009-08-13 | Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. | Pressure gauges, sphygmomanometer, method for determining pressure values, method for calibrating a pressure gauge and computer program |
US8152745B2 (en) | 2008-02-25 | 2012-04-10 | Shriners Hospitals For Children | Activity monitoring |
EP2096566B1 (en) | 2008-02-29 | 2018-04-04 | Fresenius Medical Care Holdings, Inc. | Instructional media system for dialysis machine |
US20100130811A1 (en) | 2008-04-24 | 2010-05-27 | Searete Llc | Computational system and method for memory modification |
FR2930421A1 (en) | 2008-04-28 | 2009-10-30 | Univ Sud Toulon Var Etablissem | DEVICE FOR ACQUIRING AND PROCESSING PHYSIOLOGICAL DATA OF AN ANIMAL OR A HUMAN DURING PHYSICAL ACTIVITY |
US20090295541A1 (en) | 2008-05-27 | 2009-12-03 | Intellidot Corporation | Directional rfid reader |
US20090306485A1 (en) | 2008-06-03 | 2009-12-10 | Jonathan Arnold Bell | Wearable Electronic System |
US8165840B2 (en) | 2008-06-12 | 2012-04-24 | Cardiac Pacemakers, Inc. | Posture sensor automatic calibration |
US20090322513A1 (en) | 2008-06-27 | 2009-12-31 | Franklin Dun-Jen Hwang | Medical emergency alert system and method |
US8532932B2 (en) | 2008-06-30 | 2013-09-10 | Nellcor Puritan Bennett Ireland | Consistent signal selection by signal segment selection techniques |
US8688225B2 (en) | 2008-07-11 | 2014-04-01 | Medtronic, Inc. | Posture state detection using selectable system control parameters |
US9545215B2 (en) | 2008-07-31 | 2017-01-17 | Medtronic, Inc. | Apparatus and method for detecting cardiac events |
US20110152632A1 (en) | 2008-08-06 | 2011-06-23 | E-Vitae Pte. Ltd. | Universal Body Sensor Network |
WO2010020914A1 (en) | 2008-08-19 | 2010-02-25 | Koninklijke Philips Electronics N.V. | Monitoring the blood pressure of a patient |
WO2010025144A1 (en) | 2008-08-29 | 2010-03-04 | Corventis, Inc. | Method and apparatus for acute cardiac monitoring |
US20100081946A1 (en) * | 2008-09-26 | 2010-04-01 | Qualcomm Incorporated | Method and apparatus for non-invasive cuff-less blood pressure estimation using pulse arrival time and heart rate with adaptive calibration |
US20100125188A1 (en) | 2008-11-18 | 2010-05-20 | Nonin Medical, Inc. | Motion correlated pulse oximetry |
US20120123232A1 (en) | 2008-12-16 | 2012-05-17 | Kayvan Najarian | Method and apparatus for determining heart rate variability using wavelet transformation |
TWI425934B (en) | 2008-12-23 | 2014-02-11 | Ind Tech Res Inst | Biosignal measurement modules and methods |
WO2010098912A2 (en) | 2009-02-25 | 2010-09-02 | Valencell, Inc. | Light-guiding devices and monitoring devices incorporating same |
US20100222649A1 (en) | 2009-03-02 | 2010-09-02 | American Well Systems | Remote medical servicing |
US9596989B2 (en) | 2009-03-12 | 2017-03-21 | Raytheon Company | Networked symbiotic edge user infrastructure |
US8152694B2 (en) | 2009-03-16 | 2012-04-10 | Robert Bosch Gmbh | Activity monitoring device and method |
US8313439B2 (en) | 2009-03-20 | 2012-11-20 | Massachusetts Institute Of Technology | Calibration of pulse transit time measurements to arterial blood pressure using external arterial pressure applied along the pulse transit path |
US8175720B2 (en) | 2009-04-30 | 2012-05-08 | Medtronic, Inc. | Posture-responsive therapy control based on patient input |
US8180440B2 (en) | 2009-05-20 | 2012-05-15 | Sotera Wireless, Inc. | Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds |
US11589754B2 (en) | 2009-05-20 | 2023-02-28 | Sotera Wireless, Inc. | Blood pressure-monitoring system with alarm/alert system that accounts for patient motion |
US9204857B2 (en) | 2009-06-05 | 2015-12-08 | General Electric Company | System and method for monitoring hemodynamic state |
US10085657B2 (en) | 2009-06-17 | 2018-10-02 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US20100331640A1 (en) | 2009-06-26 | 2010-12-30 | Nellcor Puritan Bennett Llc | Use of photodetector array to improve efficiency and accuracy of an optical medical sensor |
US20110066043A1 (en) | 2009-09-14 | 2011-03-17 | Matt Banet | System for measuring vital signs during hemodialysis |
US8239010B2 (en) | 2009-09-14 | 2012-08-07 | Sotera Wireless, Inc. | System for measuring vital signs during hemodialysis |
US8545417B2 (en) | 2009-09-14 | 2013-10-01 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US8321004B2 (en) | 2009-09-15 | 2012-11-27 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8364250B2 (en) | 2009-09-15 | 2013-01-29 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8527038B2 (en) | 2009-09-15 | 2013-09-03 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10806351B2 (en) | 2009-09-15 | 2020-10-20 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US20110066044A1 (en) | 2009-09-15 | 2011-03-17 | Jim Moon | Body-worn vital sign monitor |
SG10201405704QA (en) | 2009-09-15 | 2014-10-30 | Sotera Wireless Inc | Body-worn vital sign monitor |
US10420476B2 (en) | 2009-09-15 | 2019-09-24 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US9106275B2 (en) | 2009-09-24 | 2015-08-11 | Blackberry Limited | Accelerometer tap detection to initiate NFC communication |
US8781393B2 (en) | 2009-09-30 | 2014-07-15 | Ebay Inc. | Network updates of time and location |
US8583453B2 (en) | 2009-10-20 | 2013-11-12 | Universal Research Solutions, Llc | Generation and data management of a medical study using instruments in an integrated media and medical system |
US20110178375A1 (en) | 2010-01-19 | 2011-07-21 | Avery Dennison Corporation | Remote physiological monitoring |
US20110224556A1 (en) | 2010-03-10 | 2011-09-15 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US9339209B2 (en) | 2010-04-19 | 2016-05-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8888700B2 (en) | 2010-04-19 | 2014-11-18 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9173594B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8747330B2 (en) | 2010-04-19 | 2014-06-10 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8979765B2 (en) | 2010-04-19 | 2015-03-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
EP2560550B1 (en) | 2010-04-19 | 2017-12-06 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US20110275907A1 (en) | 2010-05-07 | 2011-11-10 | Salvatore Richard Inciardi | Electronic Health Journal |
-
2009
- 2009-09-14 US US12/559,392 patent/US10085657B2/en active Active
- 2009-09-14 US US12/559,413 patent/US8554297B2/en active Active
- 2009-09-14 US US12/559,403 patent/US20100324388A1/en not_active Abandoned
- 2009-09-14 US US12/559,386 patent/US9775529B2/en active Active
- 2009-09-14 US US12/559,379 patent/US9596999B2/en active Active
- 2009-09-14 US US12/559,408 patent/US8437824B2/en active Active
-
2010
- 2010-06-17 SG SG10201404148WA patent/SG10201404148WA/en unknown
- 2010-06-17 SG SG2011093507A patent/SG176892A1/en unknown
- 2010-06-17 WO PCT/US2010/039000 patent/WO2010148205A1/en active Application Filing
- 2010-06-17 EP EP10790195.1A patent/EP2442709B1/en active Active
-
2013
- 2013-11-27 US US14/092,173 patent/US10085658B2/en active Active
-
2017
- 2017-10-02 US US15/723,039 patent/US10813562B2/en active Active
-
2018
- 2018-10-01 US US16/148,948 patent/US11103148B2/en active Active
- 2018-10-01 US US16/148,874 patent/US11134857B2/en active Active
-
2020
- 2020-10-26 US US17/080,725 patent/US11638533B2/en active Active
-
2021
- 2021-08-31 US US17/463,373 patent/US20210401304A1/en active Pending
Patent Citations (100)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US4086916A (en) * | 1975-09-19 | 1978-05-02 | Joseph J. Cayre | Cardiac monitor wristwatch |
US4263918A (en) * | 1977-03-21 | 1981-04-28 | Biomega Corporation | Methods of and apparatus for the measurement of blood pressure |
US4270547A (en) * | 1978-10-03 | 1981-06-02 | University Patents, Inc. | Vital signs monitoring system |
US4305400A (en) * | 1979-10-15 | 1981-12-15 | Squibb Vitatek Inc. | Respiration monitoring method and apparatus including cardio-vascular artifact detection |
US4582068A (en) * | 1981-12-21 | 1986-04-15 | American Home Products Corporation | Systems and methods for processing physiological signals |
US4722351A (en) * | 1981-12-21 | 1988-02-02 | American Home Products Corporation | Systems and methods for processing physiological signals |
US4653498A (en) * | 1982-09-13 | 1987-03-31 | Nellcor Incorporated | Pulse oximeter monitor |
US4653498B1 (en) * | 1982-09-13 | 1989-04-18 | ||
US5224928A (en) * | 1983-08-18 | 1993-07-06 | Drug Delivery Systems Inc. | Mounting system for transdermal drug applicator |
US4710164A (en) * | 1984-05-01 | 1987-12-01 | Henry Ford Hospital | Automated hemodialysis control based upon patient blood pressure and heart rate |
US4577639A (en) * | 1984-11-08 | 1986-03-25 | Spacelabs, Inc. | Apparatus and method for automatic lead selection in electrocardiography |
US4802486A (en) * | 1985-04-01 | 1989-02-07 | Nellcor Incorporated | Method and apparatus for detecting optical pulses |
US4807638A (en) * | 1987-10-21 | 1989-02-28 | Bomed Medical Manufacturing, Ltd. | Noninvasive continuous mean arterial blood prssure monitor |
US4905697A (en) * | 1989-02-14 | 1990-03-06 | Cook Pacemaker Corporation | Temperature-controlled cardiac pacemaker responsive to body motion |
US5025791A (en) * | 1989-06-28 | 1991-06-25 | Colin Electronics Co., Ltd. | Pulse oximeter with physical motion sensor |
US5339818A (en) * | 1989-09-20 | 1994-08-23 | University Of Utah Research Foundation | Method for determining blood pressure utilizing a neural network |
US5190038A (en) * | 1989-11-01 | 1993-03-02 | Novametrix Medical Systems, Inc. | Pulse oximeter with improved accuracy and response time |
US5448991A (en) * | 1989-11-01 | 1995-09-12 | Polson; Michael J. R. | Method of measuring the oxygen saturation in pulsating blood flow |
US5820550A (en) * | 1989-11-01 | 1998-10-13 | Novametrix Medical Systems Inc. | Pulse oximeter with improved accuracy and response time |
US5316008A (en) * | 1990-04-06 | 1994-05-31 | Casio Computer Co., Ltd. | Measurement of electrocardiographic wave and sphygmus |
US5465082A (en) * | 1990-07-27 | 1995-11-07 | Executone Information Systems, Inc. | Apparatus for automating routine communication in a facility |
US5140990A (en) * | 1990-09-06 | 1992-08-25 | Spacelabs, Inc. | Method of measuring blood pressure with a photoplethysmograph |
US7509154B2 (en) * | 1991-03-07 | 2009-03-24 | Masimo Corporation | Signal processing apparatus |
US5482036A (en) * | 1991-03-07 | 1996-01-09 | Masimo Corporation | Signal processing apparatus and method |
US5685299A (en) * | 1991-03-07 | 1997-11-11 | Masimo Corporation | Signal processing apparatus |
US5490505A (en) * | 1991-03-07 | 1996-02-13 | Masimo Corporation | Signal processing apparatus |
US7383070B2 (en) * | 1991-03-07 | 2008-06-03 | Masimo Corporation | Signal processing apparatus |
US6206830B1 (en) * | 1991-03-07 | 2001-03-27 | Masimo Corporation | Signal processing apparatus and method |
US5769785A (en) * | 1991-03-07 | 1998-06-23 | Masimo Corporation | Signal processing apparatus and method |
US5632272A (en) * | 1991-03-07 | 1997-05-27 | Masimo Corporation | Signal processing apparatus |
US6541756B2 (en) * | 1991-03-21 | 2003-04-01 | Masimo Corporation | Shielded optical probe having an electrical connector |
US5197489A (en) * | 1991-06-17 | 1993-03-30 | Precision Control Design, Inc. | Activity monitoring apparatus with configurable filters |
US5247931A (en) * | 1991-09-16 | 1993-09-28 | Mine Safety Appliances Company | Diagnostic sensor clasp utilizing a slot, pivot and spring hinge mechanism |
US5289824A (en) * | 1991-12-26 | 1994-03-01 | Instromedix, Inc. | Wrist-worn ECG monitor |
US5515858A (en) * | 1992-02-28 | 1996-05-14 | Myllymaeki; Matti | Wrist-held monitoring device for physical condition |
US5899855A (en) * | 1992-11-17 | 1999-05-04 | Health Hero Network, Inc. | Modular microprocessor-based health monitoring system |
US5485838A (en) * | 1992-12-07 | 1996-01-23 | Nihon Kohden Corporation | Non-invasive blood pressure measurement device |
US5913827A (en) * | 1993-03-19 | 1999-06-22 | Gorman; Peter Gregory | Personal monitor and method for monitoring a biomedical condition in the presence of interference |
US5517988A (en) * | 1993-12-11 | 1996-05-21 | Hewlett-Packard Company | Method for detecting an irregular state in a non-invasive pulse oximeter system |
US5575284A (en) * | 1994-04-01 | 1996-11-19 | University Of South Florida | Portable pulse oximeter |
US6011985A (en) * | 1994-04-01 | 2000-01-04 | University Of South Florida | Medical diagnostic instrument using light-to-frequency converter |
US5649543A (en) * | 1994-06-06 | 1997-07-22 | Nihon Kohden Corporation | Pulse-wave propagation time basis blood pressure monitor |
US5549650A (en) * | 1994-06-13 | 1996-08-27 | Pacesetter, Inc. | System and method for providing hemodynamically optimal pacing therapy |
US5848373A (en) * | 1994-06-24 | 1998-12-08 | Delorme Publishing Company | Computer aided map location system |
US5524637A (en) * | 1994-06-29 | 1996-06-11 | Erickson; Jon W. | Interactive system for measuring physiological exertion |
US5709205A (en) * | 1994-08-23 | 1998-01-20 | Hewlett-Packard Company | Pulsoximetry sensor |
US5873834A (en) * | 1994-11-15 | 1999-02-23 | Omron Corporation | Blood pressure detecting device |
US5680870A (en) * | 1995-01-04 | 1997-10-28 | Johnson & Johnson Medical, Inc. | Oscillometric blood pressure monitor which acquires blood pressure signals from composite arterial pulse signal |
US5593431A (en) * | 1995-03-30 | 1997-01-14 | Medtronic, Inc. | Medical service employing multiple DC accelerometers for patient activity and posture sensing and method |
US5645060A (en) * | 1995-06-14 | 1997-07-08 | Nellcor Puritan Bennett Incorporated | Method and apparatus for removing artifact and noise from pulse oximetry |
US5766131A (en) * | 1995-08-04 | 1998-06-16 | Seiko Epson Corporation | Pulse-wave measuring apparatus |
US5743856A (en) * | 1995-11-06 | 1998-04-28 | Colin Corporation | Apparatus for measuring pulse-wave propagation velocity |
US5588427A (en) * | 1995-11-20 | 1996-12-31 | Spacelabs Medical, Inc. | Enhancement of physiological signals using fractal analysis |
US7373191B2 (en) * | 1996-03-05 | 2008-05-13 | Nellcor Puritan Bennett Inc. | Shunt barrier in pulse oximeter sensor |
US5853370A (en) * | 1996-09-13 | 1998-12-29 | Non-Invasive Technology, Inc. | Optical system and method for non-invasive imaging of biological tissue |
US20050070773A1 (en) * | 1996-10-10 | 2005-03-31 | Nellcor Puritan Bennett Incorporated | Motion compatible sensor for non-invasive optical blood analysis |
US5865755A (en) * | 1996-10-11 | 1999-02-02 | Dxtek, Inc. | Method and apparatus for non-invasive, cuffless, continuous blood pressure determination |
US5857975A (en) * | 1996-10-11 | 1999-01-12 | Dxtek, Inc. | Method and apparatus for non-invasive, cuffless continuous blood pressure determination |
US5800349A (en) * | 1996-10-15 | 1998-09-01 | Nonin Medical, Inc. | Offset pulse oximeter sensor |
US5964720A (en) * | 1996-11-29 | 1999-10-12 | Adaptivity Devices Ltd. | Method and system for monitoring the physiological condition of a patient |
US5876353A (en) * | 1997-01-31 | 1999-03-02 | Medtronic, Inc. | Impedance monitor for discerning edema through evaluation of respiratory rate |
US6159147A (en) * | 1997-02-28 | 2000-12-12 | Qrs Diagnostics, Llc | Personal computer card for collection of real-time biological data |
US5895359A (en) * | 1997-06-06 | 1999-04-20 | Southwest Research Institute | System and method for correcting a living subject's measured blood pressure |
US5865756A (en) * | 1997-06-06 | 1999-02-02 | Southwest Research Institute | System and method for identifying and correcting abnormal oscillometric pulse waves |
US6684090B2 (en) * | 1999-01-07 | 2004-01-27 | Masimo Corporation | Pulse oximetry data confidence indicator |
US7530949B2 (en) * | 1999-01-25 | 2009-05-12 | Masimo Corporation | Dual-mode pulse oximeter |
US7048687B1 (en) * | 1999-04-14 | 2006-05-23 | Ob Scientific, Inc. | Limited use medical probe |
US7186966B2 (en) * | 1999-08-26 | 2007-03-06 | Masimo Corporation | Amount of use tracking device and method for medical product |
US6388240B2 (en) * | 1999-08-26 | 2002-05-14 | Masimo Corporation | Shielded optical probe and method having a longevity indication |
US20020032386A1 (en) * | 2000-04-17 | 2002-03-14 | Sackner Marvin A. | Systems and methods for ambulatory monitoring of physiological signs |
US6480729B2 (en) * | 2000-04-28 | 2002-11-12 | Alexander K. Mills | Method for determining blood constituents |
US20040054821A1 (en) * | 2000-08-22 | 2004-03-18 | Warren Christopher E. | Multifunctional network interface node |
US6811538B2 (en) * | 2000-12-29 | 2004-11-02 | Ares Medical, Inc. | Sleep apnea risk evaluation |
US6595929B2 (en) * | 2001-03-30 | 2003-07-22 | Bodymedia, Inc. | System for monitoring health, wellness and fitness having a method and apparatus for improved measurement of heat flow |
US6850787B2 (en) * | 2001-06-29 | 2005-02-01 | Masimo Laboratories, Inc. | Signal component processor |
US7355512B1 (en) * | 2002-01-24 | 2008-04-08 | Masimo Corporation | Parallel alarm processor |
US20060074321A1 (en) * | 2002-08-27 | 2006-04-06 | Kenji Kouchi | Vital sign display and its method |
US20060142648A1 (en) * | 2003-01-07 | 2006-06-29 | Triage Data Networks | Wireless, internet-based, medical diagnostic system |
US7225007B2 (en) * | 2003-01-24 | 2007-05-29 | Masimo Corporation | Optical sensor including disposable and reusable elements |
US20080214963A1 (en) * | 2003-07-02 | 2008-09-04 | Commissariat A L'energie Atomique | Method for measuring movements of a person wearing a portable detector |
US20050059870A1 (en) * | 2003-08-25 | 2005-03-17 | Aceti John Gregory | Processing methods and apparatus for monitoring physiological parameters using physiological characteristics present within an auditory canal |
US20090131759A1 (en) * | 2003-11-04 | 2009-05-21 | Nathaniel Sims | Life sign detection and health state assessment system |
US7400919B2 (en) * | 2004-02-25 | 2008-07-15 | Nellcor Puritan Bennett Llc | Oximeter ambient light cancellation |
US20050234317A1 (en) * | 2004-03-19 | 2005-10-20 | Kiani Massi E | Low power and personal pulse oximetry systems |
US7477143B2 (en) * | 2004-07-23 | 2009-01-13 | Innovalarm Corporation | Enhanced personal monitoring and alarm response method and system |
US20060047215A1 (en) * | 2004-09-01 | 2006-03-02 | Welch Allyn, Inc. | Combined sensor assembly |
US20060173257A1 (en) * | 2005-01-31 | 2006-08-03 | Konica Minolta Sensing, Inc. | Sleep evaluation method, sleep evaluation system, operation program for sleep evaluation system, pulse oximeter, and sleep support system |
US7377794B2 (en) * | 2005-03-01 | 2008-05-27 | Masimo Corporation | Multiple wavelength sensor interconnect |
US20060252999A1 (en) * | 2005-05-03 | 2006-11-09 | Devaul Richard W | Method and system for wearable vital signs and physiology, activity, and environmental monitoring |
US20060265246A1 (en) * | 2005-05-10 | 2006-11-23 | Cardinal Health 303, Inc. | Medication safety system featuring a multiplexed RFID interrogator panel |
US7725147B2 (en) * | 2005-09-29 | 2010-05-25 | Nellcor Puritan Bennett Llc | System and method for removing artifacts from waveforms |
US7184809B1 (en) * | 2005-11-08 | 2007-02-27 | Woolsthorpe Technologies, Llc | Pulse amplitude indexing method and apparatus |
US7215987B1 (en) * | 2005-11-08 | 2007-05-08 | Woolsthorpe Technologies | Method and apparatus for processing signals reflecting physiological characteristics |
US20070185393A1 (en) * | 2006-02-03 | 2007-08-09 | Triage Wireless, Inc. | System for measuring vital signs using an optical module featuring a green light source |
US20070265880A1 (en) * | 2006-05-06 | 2007-11-15 | Irody Inc | Apparatus and method for obtaining an identification of drugs for enhanced safety |
US20070260487A1 (en) * | 2006-05-06 | 2007-11-08 | Irody Inc | System and method for real time management of a drug regimen |
US20070265533A1 (en) * | 2006-05-12 | 2007-11-15 | Bao Tran | Cuffless blood pressure monitoring appliance |
US7373912B2 (en) * | 2006-06-07 | 2008-05-20 | Ford Global Technologies, Llc | Oil level indicating system for internal combustion engine |
US7983933B2 (en) * | 2006-12-06 | 2011-07-19 | Microsoft Corporation | Patient monitoring via image capture |
US20100210930A1 (en) * | 2009-02-13 | 2010-08-19 | Saylor Stephen D | Physiological Blood Gas Detection Apparatus and Method |
Cited By (120)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US8602997B2 (en) | 2007-06-12 | 2013-12-10 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US9215986B2 (en) | 2007-06-12 | 2015-12-22 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US9161700B2 (en) | 2007-06-12 | 2015-10-20 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US9668656B2 (en) | 2007-06-12 | 2017-06-06 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US20140276145A1 (en) * | 2007-06-12 | 2014-09-18 | Sotera Wireless, Inc. | BODY-WORN SYSTEM FOR MEASURING CONTINUOUS NON-INVASIVE BLOOD PRESSURE (cNIBP) |
US8808188B2 (en) | 2007-06-12 | 2014-08-19 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US11607152B2 (en) | 2007-06-12 | 2023-03-21 | Sotera Wireless, Inc. | Optical sensors for use in vital sign monitoring |
US8740802B2 (en) | 2007-06-12 | 2014-06-03 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US11330988B2 (en) | 2007-06-12 | 2022-05-17 | Sotera Wireless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US10765326B2 (en) * | 2007-06-12 | 2020-09-08 | Sotera Wirless, Inc. | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) |
US8909330B2 (en) | 2009-05-20 | 2014-12-09 | Sotera Wireless, Inc. | Body-worn device and associated system for alarms/alerts based on vital signs and motion |
US8956293B2 (en) | 2009-05-20 | 2015-02-17 | Sotera Wireless, Inc. | Graphical ‘mapping system’ for continuously monitoring a patient's vital signs, motion, and location |
US11918321B2 (en) | 2009-05-20 | 2024-03-05 | Sotera Wireless, Inc. | Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds |
US8672854B2 (en) | 2009-05-20 | 2014-03-18 | Sotera Wireless, Inc. | System for calibrating a PTT-based blood pressure measurement using arm height |
US8594776B2 (en) | 2009-05-20 | 2013-11-26 | Sotera Wireless, Inc. | Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds |
US11589754B2 (en) | 2009-05-20 | 2023-02-28 | Sotera Wireless, Inc. | Blood pressure-monitoring system with alarm/alert system that accounts for patient motion |
US9492092B2 (en) | 2009-05-20 | 2016-11-15 | Sotera Wireless, Inc. | Method for continuously monitoring a patient using a body-worn device and associated system for alarms/alerts |
US8738118B2 (en) | 2009-05-20 | 2014-05-27 | Sotera Wireless, Inc. | Cable system for generating signals for detecting motion and measuring vital signs |
US10555676B2 (en) | 2009-05-20 | 2020-02-11 | Sotera Wireless, Inc. | Method for generating alarms/alerts based on a patient's posture and vital signs |
US11896350B2 (en) | 2009-05-20 | 2024-02-13 | Sotera Wireless, Inc. | Cable system for generating signals for detecting motion and measuring vital signs |
US8475370B2 (en) | 2009-05-20 | 2013-07-02 | Sotera Wireless, Inc. | Method for measuring patient motion, activity level, and posture along with PTT-based blood pressure |
US10987004B2 (en) | 2009-05-20 | 2021-04-27 | Sotera Wireless, Inc. | Alarm system that processes both motion and vital signs using specific heuristic rules and thresholds |
US10973414B2 (en) | 2009-05-20 | 2021-04-13 | Sotera Wireless, Inc. | Vital sign monitoring system featuring 3 accelerometers |
US8956294B2 (en) | 2009-05-20 | 2015-02-17 | Sotera Wireless, Inc. | Body-worn system for continuously monitoring a patients BP, HR, SpO2, RR, temperature, and motion; also describes specific monitors for apnea, ASY, VTAC, VFIB, and ‘bed sore’ index |
US9775529B2 (en) | 2009-06-17 | 2017-10-03 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US11134857B2 (en) | 2009-06-17 | 2021-10-05 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US11638533B2 (en) | 2009-06-17 | 2023-05-02 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US9596999B2 (en) | 2009-06-17 | 2017-03-21 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US8554297B2 (en) | 2009-06-17 | 2013-10-08 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US8437824B2 (en) | 2009-06-17 | 2013-05-07 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US10085657B2 (en) | 2009-06-17 | 2018-10-02 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US11103148B2 (en) | 2009-06-17 | 2021-08-31 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US10813562B2 (en) | 2009-06-17 | 2020-10-27 | Sotera Wireless, Inc. | Body-worn pulse oximeter |
US11963736B2 (en) | 2009-07-20 | 2024-04-23 | Masimo Corporation | Wireless patient monitoring system |
US11253169B2 (en) | 2009-09-14 | 2022-02-22 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US10595746B2 (en) | 2009-09-14 | 2020-03-24 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US10123722B2 (en) | 2009-09-14 | 2018-11-13 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US8740807B2 (en) | 2009-09-14 | 2014-06-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US8622922B2 (en) | 2009-09-14 | 2014-01-07 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US8545417B2 (en) | 2009-09-14 | 2013-10-01 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiration rate |
US10420476B2 (en) | 2009-09-15 | 2019-09-24 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8364250B2 (en) | 2009-09-15 | 2013-01-29 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10806351B2 (en) | 2009-09-15 | 2020-10-20 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8321004B2 (en) | 2009-09-15 | 2012-11-27 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8527038B2 (en) | 2009-09-15 | 2013-09-03 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US11963746B2 (en) | 2009-09-15 | 2024-04-23 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10278645B2 (en) | 2010-03-10 | 2019-05-07 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8591411B2 (en) | 2010-03-10 | 2013-11-26 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US10213159B2 (en) | 2010-03-10 | 2019-02-26 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8727977B2 (en) | 2010-03-10 | 2014-05-20 | Sotera Wireless, Inc. | Body-worn vital sign monitor |
US8979765B2 (en) | 2010-04-19 | 2015-03-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8747330B2 (en) | 2010-04-19 | 2014-06-10 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US8888700B2 (en) | 2010-04-19 | 2014-11-18 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9339209B2 (en) | 2010-04-19 | 2016-05-17 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9173593B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9173594B2 (en) | 2010-04-19 | 2015-11-03 | Sotera Wireless, Inc. | Body-worn monitor for measuring respiratory rate |
US9585577B2 (en) | 2010-12-28 | 2017-03-07 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US9364158B2 (en) | 2010-12-28 | 2016-06-14 | Sotera Wirless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US10722130B2 (en) | 2010-12-28 | 2020-07-28 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US10722131B2 (en) | 2010-12-28 | 2020-07-28 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US10722132B2 (en) | 2010-12-28 | 2020-07-28 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US10856752B2 (en) | 2010-12-28 | 2020-12-08 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US9380952B2 (en) | 2010-12-28 | 2016-07-05 | Sotera Wireless, Inc. | Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure |
US9439574B2 (en) | 2011-02-18 | 2016-09-13 | Sotera Wireless, Inc. | Modular wrist-worn processor for patient monitoring |
US11179105B2 (en) | 2011-02-18 | 2021-11-23 | Sotera Wireless, Inc. | Modular wrist-worn processor for patient monitoring |
US10357187B2 (en) | 2011-02-18 | 2019-07-23 | Sotera Wireless, Inc. | Optical sensor for measuring physiological properties |
US10307111B2 (en) | 2012-02-09 | 2019-06-04 | Masimo Corporation | Patient position detection system |
US11083397B2 (en) | 2012-02-09 | 2021-08-10 | Masimo Corporation | Wireless patient monitoring device |
US10149616B2 (en) | 2012-02-09 | 2018-12-11 | Masimo Corporation | Wireless patient monitoring device |
US11918353B2 (en) | 2012-02-09 | 2024-03-05 | Masimo Corporation | Wireless patient monitoring device |
USD788312S1 (en) | 2012-02-09 | 2017-05-30 | Masimo Corporation | Wireless patient monitoring device |
US10188296B2 (en) | 2012-02-09 | 2019-01-29 | Masimo Corporation | Wireless patient monitoring device |
WO2013175314A2 (en) * | 2012-05-23 | 2013-11-28 | Convergent Engineering, Inc. | System and method for detecting preeclampsia |
WO2013175314A3 (en) * | 2012-05-23 | 2014-04-10 | Convergent Engineering, Inc. | System and method for detecting preeclampsia |
US9060745B2 (en) | 2012-08-22 | 2015-06-23 | Covidien Lp | System and method for detecting fluid responsiveness of a patient |
US9402573B2 (en) | 2012-08-22 | 2016-08-02 | Covidien Lp | System and method for detecting fluid responsiveness of a patient |
US8731649B2 (en) | 2012-08-30 | 2014-05-20 | Covidien Lp | Systems and methods for analyzing changes in cardiac output |
US9357937B2 (en) | 2012-09-06 | 2016-06-07 | Covidien Lp | System and method for determining stroke volume of an individual |
US20140073962A1 (en) * | 2012-09-11 | 2014-03-13 | Nellcor Puritan Bennett Llc | System and method for determining stroke volume of a patient |
US9241646B2 (en) * | 2012-09-11 | 2016-01-26 | Covidien Lp | System and method for determining stroke volume of a patient |
US11445930B2 (en) | 2012-09-11 | 2022-09-20 | Covidien Lp | System and method for determining stroke volume of a patient |
US10448851B2 (en) | 2012-09-11 | 2019-10-22 | Covidien Lp | System and method for determining stroke volume of a patient |
US11058303B2 (en) | 2012-09-14 | 2021-07-13 | Covidien Lp | System and method for determining stability of cardiac output |
US8977348B2 (en) | 2012-12-21 | 2015-03-10 | Covidien Lp | Systems and methods for determining cardiac output |
US9833192B2 (en) | 2013-03-15 | 2017-12-05 | Thought Technology Ltd. | Finger mounted physiology sensor |
US10716510B2 (en) | 2013-09-17 | 2020-07-21 | Medibotics | Smart clothing with converging/diverging bend or stretch sensors for measuring body motion or configuration |
US9582072B2 (en) | 2013-09-17 | 2017-02-28 | Medibotics Llc | Motion recognition clothing [TM] with flexible electromagnetic, light, or sonic energy pathways |
US10602965B2 (en) | 2013-09-17 | 2020-03-31 | Medibotics | Wearable deformable conductive sensors for human motion capture including trans-joint pitch, yaw, and roll |
US10321873B2 (en) | 2013-09-17 | 2019-06-18 | Medibotics Llc | Smart clothing for ambulatory human motion capture |
US10234934B2 (en) | 2013-09-17 | 2019-03-19 | Medibotics Llc | Sensor array spanning multiple radial quadrants to measure body joint movement |
US10251607B2 (en) * | 2013-11-25 | 2019-04-09 | Samsung Electronics Co., Ltd. | Method and apparatus for measuring bio signal |
US20160262702A1 (en) * | 2013-11-25 | 2016-09-15 | Samsung Electronics Co., Ltd. | Method and apparatus for measuring bio signal |
US20160324477A1 (en) * | 2015-05-08 | 2016-11-10 | Texas Instruments Incorporated | Accuracy of heart rate estimation from photoplethysmographic (ppg) signals |
US11744520B2 (en) | 2015-05-08 | 2023-09-05 | Texas Instruments Incorporated | Accuracy of heart rate estimation from photoplethysmographic (PPG) signals |
US10736575B2 (en) | 2015-05-08 | 2020-08-11 | Texas Instruments Incorporated | Accuracy of heart rate estimation from photoplethysmographic (PPG) signals |
US10123746B2 (en) * | 2015-05-08 | 2018-11-13 | Texas Instruments Incorporated | Accuracy of heart rate estimation from photoplethysmographic (PPG) signals |
US20180146865A1 (en) * | 2015-05-29 | 2018-05-31 | Cis Forschungsinstitut Fuer Mikrosensorik Gmbh | Method and device for ascertaining a blood pressure curve |
US10362945B2 (en) * | 2015-05-29 | 2019-07-30 | Cis Forschungsinstitut Fuer Mikrosensorik Gmbh | Method and device for ascertaining a blood pressure curve |
US10448844B2 (en) | 2015-08-31 | 2019-10-22 | Masimo Corporation | Systems and methods for patient fall detection |
US11089963B2 (en) | 2015-08-31 | 2021-08-17 | Masimo Corporation | Systems and methods for patient fall detection |
US10736518B2 (en) | 2015-08-31 | 2020-08-11 | Masimo Corporation | Systems and methods to monitor repositioning of a patient |
US10226187B2 (en) | 2015-08-31 | 2019-03-12 | Masimo Corporation | Patient-worn wireless physiological sensor |
US11576582B2 (en) | 2015-08-31 | 2023-02-14 | Masimo Corporation | Patient-worn wireless physiological sensor |
US10383527B2 (en) | 2015-08-31 | 2019-08-20 | Masimo Corporation | Wireless patient monitoring systems and methods |
US11317865B2 (en) | 2015-09-28 | 2022-05-03 | Aclaris Medical, Llc | Wearable physiologic sensing apparatus |
US10779765B2 (en) | 2015-09-28 | 2020-09-22 | Aclaris Medical, Llc | Wearable physiologic sensing apparatus |
CN106361348A (en) * | 2015-11-19 | 2017-02-01 | 北京智谷睿拓技术服务有限公司 | Finger type determining method and equipment |
US11202571B2 (en) | 2016-07-07 | 2021-12-21 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US10617302B2 (en) | 2016-07-07 | 2020-04-14 | Masimo Corporation | Wearable pulse oximeter and respiration monitor |
US11666277B2 (en) | 2016-10-12 | 2023-06-06 | Samsung Electronics Co., Ltd. | Apparatus and method for estimating biometric information |
US11076777B2 (en) | 2016-10-13 | 2021-08-03 | Masimo Corporation | Systems and methods for monitoring orientation to reduce pressure ulcer formation |
US10709339B1 (en) * | 2017-07-03 | 2020-07-14 | Senstream, Inc. | Biometric wearable for continuous heart rate and blood pressure monitoring |
US10898087B2 (en) | 2017-12-08 | 2021-01-26 | Texas Instruments Incorporated | Motion detection and cancellation using ambient light |
US11896354B2 (en) | 2018-02-05 | 2024-02-13 | Samsung Electronics Co., Ltd. | Blood pressure measuring apparatus and blood pressure measuring method |
US11382519B2 (en) | 2018-02-05 | 2022-07-12 | Samsung Electronics Co., Ltd. | Blood pressure measuring apparatus and blood pressure measuring method |
US11974833B2 (en) | 2020-03-20 | 2024-05-07 | Masimo Corporation | Wearable device for noninvasive body temperature measurement |
USD980091S1 (en) | 2020-07-27 | 2023-03-07 | Masimo Corporation | Wearable temperature measurement device |
USD1022729S1 (en) | 2020-07-27 | 2024-04-16 | Masimo Corporation | Wearable temperature measurement device |
USD974193S1 (en) | 2020-07-27 | 2023-01-03 | Masimo Corporation | Wearable temperature measurement device |
USD1000975S1 (en) | 2021-09-22 | 2023-10-10 | Masimo Corporation | Wearable temperature measurement device |
Also Published As
Publication number | Publication date |
---|---|
US20140088385A1 (en) | 2014-03-27 |
US11638533B2 (en) | 2023-05-02 |
US8437824B2 (en) | 2013-05-07 |
EP2442709C0 (en) | 2024-04-24 |
US20190029534A1 (en) | 2019-01-31 |
US10813562B2 (en) | 2020-10-27 |
US20210401304A1 (en) | 2021-12-30 |
EP2442709A4 (en) | 2014-12-17 |
WO2010148205A1 (en) | 2010-12-23 |
US20180042495A1 (en) | 2018-02-15 |
US10085658B2 (en) | 2018-10-02 |
US8554297B2 (en) | 2013-10-08 |
US11103148B2 (en) | 2021-08-31 |
US20100324385A1 (en) | 2010-12-23 |
SG10201404148WA (en) | 2014-09-26 |
US10085657B2 (en) | 2018-10-02 |
EP2442709B1 (en) | 2024-04-24 |
US20210251501A1 (en) | 2021-08-19 |
US20100324386A1 (en) | 2010-12-23 |
US9775529B2 (en) | 2017-10-03 |
US9596999B2 (en) | 2017-03-21 |
US11134857B2 (en) | 2021-10-05 |
EP2442709A1 (en) | 2012-04-25 |
SG176892A1 (en) | 2012-01-30 |
US20100324387A1 (en) | 2010-12-23 |
US20100324384A1 (en) | 2010-12-23 |
US20100324389A1 (en) | 2010-12-23 |
US20190029536A1 (en) | 2019-01-31 |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11638533B2 (en) | Body-worn pulse oximeter | |
US11607152B2 (en) | Optical sensors for use in vital sign monitoring | |
US10335044B2 (en) | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) | |
US8602997B2 (en) | Body-worn system for measuring continuous non-invasive blood pressure (cNIBP) | |
US8200321B2 (en) | Method for measuring patient posture and vital signs | |
US20230225633A1 (en) | Optical sensors for use in vital sign monitoring |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
AS | Assignment |
Owner name: TRIAGE WIRELESS, INC., CALIFORNIA Free format text: ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:MOON, JIM;MCCOMBIE, DEVIN;DHILLON, MARSHAL;AND OTHERS;REEL/FRAME:023228/0885 Effective date: 20090911 |
|
AS | Assignment |
Owner name: SOTERA WIRELESS, INC., CALIFORNIA Free format text: CHANGE OF NAME;ASSIGNOR:TRIAGE WIRELESS, INC.;REEL/FRAME:024379/0367 Effective date: 20091026 |
|
STCB | Information on status: application discontinuation |
Free format text: ABANDONED -- FAILURE TO RESPOND TO AN OFFICE ACTION |