WO2011077097A1 - Physiological monitoring device and method - Google Patents

Physiological monitoring device and method Download PDF

Info

Publication number
WO2011077097A1
WO2011077097A1 PCT/GB2010/002327 GB2010002327W WO2011077097A1 WO 2011077097 A1 WO2011077097 A1 WO 2011077097A1 GB 2010002327 W GB2010002327 W GB 2010002327W WO 2011077097 A1 WO2011077097 A1 WO 2011077097A1
Authority
WO
WIPO (PCT)
Prior art keywords
signals
motion
subject
respiration
physiological
Prior art date
Application number
PCT/GB2010/002327
Other languages
French (fr)
Inventor
John Mccune Anderson
Jonathan David Francey
Original Assignee
Intelesens Limited
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Intelesens Limited filed Critical Intelesens Limited
Priority to GB201212695A priority Critical patent/GB2488743B/en
Publication of WO2011077097A1 publication Critical patent/WO2011077097A1/en

Links

Classifications

    • 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
    • A61B5/103—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
    • 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]
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01P—MEASURING LINEAR OR ANGULAR SPEED, ACCELERATION, DECELERATION, OR SHOCK; INDICATING PRESENCE, ABSENCE, OR DIRECTION, OF MOVEMENT
    • G01P15/00—Measuring acceleration; Measuring deceleration; Measuring shock, i.e. sudden change of acceleration
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B2562/00—Details of sensors; Constructional details of sensor housings or probes; Accessories for sensors
    • A61B2562/02—Details of sensors specially adapted for in-vivo measurements
    • A61B2562/0219—Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches

Definitions

  • the invention relates to a physiological monitoring device and method, for monitoring physiological signals of a subject in conjunction with monitoring motion of the subject.
  • ECG signals may be monitored to determine if an arrhythmia of the subject's heart has occurred. This involves analysis of the ECG signals, and signal to noise ratio is an issue particularly in the mobile environment. If the signal to noise ratio is low, then the analysis of the ECG signals may lead to detection of false arrhythmias.
  • respiration signals may be monitored to determine if the respiration rate of the subject is within an acceptable range. This involves analysis of the respiration signals, and signal to noise ratio is again an issue particularly when the subject is mobile. If the signal to noise ratio is low, then the analysis of the respiration signals may lead to detection of false respiration rates.
  • Various sources of noise in ECG and respiration signafs are known, for example noise caused by motion of the subject. To enhance analysis of ECG and respiration signals it is desirable to monitor sources of noise, e.g. subject movement.
  • a physiological monitoring device adapted to be worn by a subject, comprising
  • one or more electrodes adapted to be placed on the subject and to detect physiological signals from the subject
  • a physiological signal detector which receives the physiological signals from the electrodes
  • a motion detector which detects motion of the subject
  • the motion detector may comprise an accelerometer.
  • the accelerometer may be a single- axis accelerometer.
  • the accelerometer may be a multi-axis accelerometer.
  • the accelerometer may be a three-axis accelerometer which measures the acceleration of movement of the subject along three orthogonal axes.
  • the accelerometer may be configured to detect only accelerations, and therefore subject movement, within a predetermined range.
  • the accelerometer may provide information concerning its, and therefore the subject's, orientation e.g. upright, prone.
  • the microprocessor may comprise embedded motion signal analysis software which it uses to analyse motion signals received from the motion detector.
  • the microprocessor may analyse acceleration signals from the accelerometer.
  • the motion detector comprises a multi- axis accelerometer the microprocessor may analyse acceleration signals along each axis.
  • the microprocessor may analyse the acceleration signals along each axis separately. For each axis, the microprocessor may analyse the acceleration signals along the axis by comparing a measured acceleration signal with a previously-measured acceleration signal to determine a change in acceleration along the axis. For each axis, the microprocessor may analyse the acceleration signals along the axis by comparing a measured acceleration signal with an immediatly-preceding, previously-measured acceleration signal to determine a change in acceleration along the axis.
  • the motion threshold of the subject may be determined as an acceleration change threshold for each axis.
  • the motion threshold of the subject may be determined as an acceleration change threshold of typically O.lg for a sagittal axis, typically O.lg for a transverse axis and typically 0.6g for a frontal axis.
  • the microprocessor may sum the acceleration signals along each axis and analyse the summed acceleration signals.
  • the microprocessor may analyse the summed acceleration signals by comparing a summed acceleration signal with a previous summed acceleration signal to determine a change in summed acceleration.
  • the motion threshold of the subject may be determined as a summed acceleration change threshold.
  • the motion threshold of the subject may be determined as a summed acceleration change threshold above lg that is considered acceptable for physiological signal detection and reporting.
  • the motion threshold may be determined from a study of motion and physiological signal detection in one or more normal subjects.
  • Physiological signals and motion signals may be collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement.
  • the activities may comprise, for example, resting in various positions e.g. sitting, standing, lying on back, front, left and right sides, walking on the level and on stairs, running at various rates, cycling, getting dressed, cooking, housework.
  • the physiological signals and motion signals are analysed to determine the motion threshold, i.e. a threshold level for the motion signals at or above which it is decided that noise in the physiological signals due to motion of the subject is unacceptable.
  • the physiological signals detected from the subject may comprise ECG signals detected from the subject's heart.
  • An ECG motion threshold may be determined from a study of motion and arrhythmia detection in one or more normal subjects i.e. with normal heart function and therefore no arrhythmias.
  • ECG signals and acceleration signals may be collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement.
  • the ECG signals and motion signals are analysed to determine the ECG motion threshold, i.e. a threshold level for the motion signals at or above which it is decided that noise in the ECG signals due to motion of the subject is too great to be confident that analysis of the ECG signals will detect a real arrhythmia.
  • An ECG motion threshold may be determined by analysis of the ECG signals to measure the level of noise in these signals, and setting a value of the ECG motion threshold where the noise level of the ECG signals is considered to be unacceptable.
  • the microprocessor may comprise embedded ECG analysis software which analyses the ECG signals.
  • the microprocessor may analyse the ECG signals to monitor physiological parameters such as the subject's heart beat rate and ECG features such as P wave shape, QRS width.
  • the microprocessor may use monitoring of these parameters to determine if the subject experiences a heart arrhythmia.
  • the microprocessor may record data concerning an arrhythmia such as the time that it occurs, the duration of the arrhythmia and the shape of the arrhythmia and can output data concerning the arrhythmia.
  • the microprocessor may receive motion signals and correlate these with the analysis of the ECG signals. At points of time when the subject's motion is equal to or greater than the ECG motion threshold, analysis of arrhythmia in the ECG signals may be disregarded, e.g. the ECG analysis software may cease to output arrhythmia data.
  • the physiological signals detected from the subject may comprise respiration signals detected from the subject.
  • a respiration motion threshold may be determined from a study of motion and respiration in one or more subjects having normal respiration. Respiration signals and acceleration signals may be collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement. For the or each subject and each activity, the respiration signals and motion signals are analysed to determine the respiration motion threshold, i.e. a threshold level for the motion signals at or above which it is decided that noise in the respiration signals due to motion of the subject is too great to be confident that analysis of the respiration signals will result in a true measure of the subject's respiration.
  • the respiration motion threshold i.e. a threshold level for the motion signals at or above which it is decided that noise in the respiration signals due to motion of the subject is too great to be confident that analysis of the respiration signals will result in a true measure of the subject's respiration.
  • a respiration motion threshold may be determined by analysis of the respiration signals to measure the level of noise in these signals, and setting a value of the respiration motion threshold where the noise level of the respiration signals is considered to be unacceptable.
  • the microprocessor may comprise embedded respiration analysis software which analyses the respiration signals.
  • the microprocessor may analyse the respiration signals to monitor physiological parameters such as the subject's respiration rate.
  • the microprocessor may use monitoring of these parameters to determine if the subject experiences abnormal respiration.
  • the microprocessor may record data concerning abnormal respiration such as the time that it occurs, the duration, and can output data concerning the abnormal respiration.
  • the microprocessor may receive motion signals and correlate these with the analysis of the respiration signals. At points of time when the subject's motion is equal to or greater than the respiration motion threshold, analysis of the respiration signals may be disregarded, e.g. the respiration analysis software may cease to output respiration data.
  • the microprocessor may comprise an analogue to digital converter (ADC).
  • ADC analogue to digital converter
  • the microprocessor may receive analogue physiological signals and use the ADC to convert these to digital physiological signals for analysis.
  • the electrodes may be any known electrodes which, when placed on a subject, are able to detect ECG signals from the subject's heart.
  • An ECG signal comprising a difference between the voltage signal between first and second electrodes may be fed to the ECG signal detector.
  • An ECG signal comprising a difference between the voltage signal between first and third electrodes may be fed to the ECG signal detector.
  • the electrodes may be any known electrodes which, when placed on a subject, are able to detect respiration signals from the subject.
  • the electrodes may measure fluctuations in impedance signals from the subject's lungs during breathing.
  • the impedance signals may be used in the assessment of the respiration of the subject.
  • the physiological monitoring device may further comprise a temperature sensor for measuring the temperature of the subject's skin.
  • a method of monitoring physiological signals of a subject in conjunction with monitoring motion of the subject comprising
  • ⁇ using a microprocessor to receive the physiological signals and motion signals and correlate the motion signals with analysis of the physiological signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded.
  • a physiological monitoring device adapted to be worn by a subject, comprising
  • one or more electrodes adapted to be placed on the subject and to detect ECG signals from the subject's heart
  • an ECG signal detector which receives the ECG signals from the electrodes, a motion detector which detects motion of the subject,
  • a microprocessor which receives ECG signals from the ECG signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the ECG signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of arrhythmia in the ECG signals is disregarded.
  • a method of monitoring ECG signals of a subject in conjunction with monitoring motion of the subject comprising
  • a microprocessor to receive the ECG signals and motion signals and correlate the motion signals with analysis of the ECG signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of arrhythmia in the ECG signals is disregarded.
  • a physiological monitoring device adapted to be worn by a subject, comprising
  • one or more electrodes adapted to be placed on the subject and to detect respiration signals from the subject
  • respiration signal detector which receives the respiration signals from the electrodes
  • a motion detector which detects motion of the subject
  • a microprocessor which receives respiration signals from the respiration signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the respiration signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the respiration signals is disregarded.
  • a method of monitoring respiration signals of a subject in conjunction with monitoring motion of the subject comprising
  • a microprocessor to receive the respiration signals and motion signals and correlate the motion signals with analysis of the respiration signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the respiration signals is disregarded.
  • Figure 1 is a schematic representation of a physiological monitoring device according to the first aspect of the invention.
  • Figure 2 is a flowchart representing the method of the second aspect of the invention.
  • the physiological monitoring device 1 comprises three electrodes 3a, 3b, 3c for measuring ECG signals, two electrodes 3d, 3e for measuring respiration signals, a temperature sensor 5, an ECG signal detector 7, a respiration detector 9, a motion sensor 11 a microprocessor 13 and a transceiver 19.
  • the electrodes 3a to 3e and the temperature sensor 5 are housed in a flexible patch (not shown) which is attached to the subject by an adhesive layer.
  • the flexible patch is made of a biocompatible material.
  • the ECG signal detector 7, the respiration detector 9, the motion sensor 11 and the microprocessor 13 are contained within a device (not shown) which removably attaches to the flexible patch by clips. Five clips are provided on the device which attach to corresponding clips on the flexible patch.
  • the device further contains a battery (not shown) for providing power to the other components contained in the device.
  • the device comprises charger connections (not shown) for connection of the device battery to a charger.
  • the device further comprises a battery power monitoring device (not shown). When the battery power approaches a preset level, a warning light provided on the device (not shown) is lit to indicate to the subject that the battery of the device should be charged.
  • the electrodes 3a, 3b, 3c may comprise any known electrode which, when placed on a subject, are able to detect ECG signals from the subject's heart.
  • the electrodes may, for example, be placed on the subject's torso or on the subject's chest.
  • the first electrode 3a is closest to the subject's left leg
  • a second electrode 3b is closest to the subject's right arm
  • a third electrode 3c is closest to the subject's left arm. It will be appreciated, however, that other electrode positions may be used.
  • Each of the electrodes 3a, 3b, 3c is connected by a lead and clips to the ECG signal detector 7.
  • An ECG signal comprising a difference in voltage between the signal of electrode 3b and the signal of electrode 3a is fed to the ECG signal detector 7.
  • ECG signal comprising a difference in voltage between the signal of electrode 3c and the signal of electrode 3a is fed to the ECG signal detector 7. These two signals are used in the assessment of the ECG physiological parameters of the subject. A signal is also sent to the ECG signal detector 7 from electrode 3a, this signal is used as a reference signal.
  • the physiological monitoring device comprises three electrodes for ECG measurement, but it will be appreciated that other embodiments may comprise more or less electrodes.
  • the electrodes 3d, 3e may comprise any known electrode which, when placed on a subject, are able to detect respiration signals from the subject.
  • the electrodes may, for example, be placed on the subject's torso or on the subject's chest.
  • a known sinusoida l signal at a specific frequency (57kHz) is injected into the electrodes 3d and 3e, and impedance changes caused by fluctuations of the volume of air in the subject's lungs are measured by the electrodes.
  • the impedance signals are fed by a lead to the respiration signal detector 9.
  • the temperature sensor 5 comprises a thermistor. This measures resistance signals of the subject's skin and passes these signals via a lead to the microprocessor 13 where they are used as an indication of the temperature of the subject's skin.
  • the ECG signal detector 7 comprises analogue hardware and is able to receive continuous signals from the electrodes 3a, 3b, 3c.
  • the ECG signal detector 7 comprises a noise filter 15, having a passband of approximately 0.5 Hz to 25 Hz.
  • the noise filter 15 attempts to filter noise from the signals received from the electrodes caused by, for example, mobile phone interference or movement by the subject.
  • the efficacy of the noise filter 15 is limited as it has to have a certain passband to achieve adequate detection of the ECG signals. The size of the passband, however, allows some motion artefacts to be detected which results in noise in the ECG signals.
  • the ECG signal detector 7 is connected to the microprocessor 13 and sends filtered ECG signals to the microprocessor 13.
  • the respiration signal detector 9 also comprises analogue hardware and receives impedance signals from the electrodes 3d and 3e. Measurement of the impedance signals is highly susceptible to motion artefacts, and the respiration signal detector 9 comprises a noise filter. The noise filter attempts to filter noise due to motion from the impedance signals received from the electrodes.
  • the respiration signal detector 9 is connected to the microprocessor 13 and sends filtered respiration signals to the microprocessor 13.
  • the motion detector 11 of the physiological monitoring device 1 comprises a three-axis accelerometer for measurement of three axis motion and orientation of the subject.
  • the accelerometer comprises a digital accelerometer micro electro mechanical system and measures the acceleration of motion of the subject along three orthogonal axes.
  • the accelerometer may be configured to detect only accelerations, and therefore subject movement, within a pre-determined range.
  • the accelerometer is connected to the microprocessor 13. Signals comprising the acceleration along each axis are sent to the microprocessor 13.
  • the microprocessor 13 comprises an analogue to digital converter (ADC) 17. This receives the filtered ECG signals from the ECG signal detector 5 and the filtered respiration signals from the respiration signal detector 7 and samples these signals, e.g. at a rate of 360 samples per second to create digital ECG signals and at a rate of 120 samples per second to create digital respiration signals.
  • ADC analogue to digital converter
  • the microprocessor 13 comprises embedded acceleration analysis software which it uses to analyse the three acceleration signals from the motion detector 11.
  • the acceleration analysis software analyses the acceleration signals received from each axis of the accelerometer separately. For each axis, the acceleration analysis software analyses the acceleration signals along the axis by comparing a measured acceleration signal with a previously-measured acceleration signal to determine a change in acceleration along the axis.
  • the acceleration analysis software sets a motion threshold of the subject as an acceleration change threshold for each axis, for example O.lg for a sagittal axis, O.lg for a transverse axis and 0.6g for a frontal axis.
  • the microprocessor 13 comprises embedded ECG analysis software and uses this to analyse the ECG signals received from the ECG signal detector 7.
  • the ECG analysis software periodically, e.g. every 15 minutes, outputs details of the subject's heart rate to the transceiver 19.
  • the ECG analysis software is remotely configurable to allow a clinician to set the periodic reporting frequency appropriate for the subject.
  • the respiration analysis software is also remotely configurable to allow a clinician to set a heart rate limit which is deemed acceptable for the subject.
  • the ECG analysis software analyses the ECG signals to monitor the subject's heart beat rate. If this exceeds the set heart rate limit, the microprocessor 13 outputs a heart rate alarm and data concerning the heart rate to the transceiver 19.
  • the ECG analysis software further analyses the ECG signals to monitor ECG features such as P wave shape, QRS width.
  • the ECG analysis software periodically outputs such details of the subject's ECG signals to the transceiver 19.
  • the ECG analysis software uses monitoring of these ECG parameters to determine if the subject experiences a heart arrhythmia, for example any of asystole, ventricular fibrillation, ventricular tachycardia, bradycardia, tachycardia, atrial fibrillation, supra-ventricular fibrillation.
  • ECG arrhythmia detection and analysis is based on the Pan-Tompkins method and uses analysis of the slope, amplitude and width of QRS complexes to detect an arrhythmia.
  • the arrhythmia detection and analysis algorithm used includes a series of steps which include lowpass and highpass filtering, derivative, squaring, and integration functions, and adaptive thresholding and search procedures. If an arrhythmia occurs the ECG analysis software records data concerning the arrhythmia such as the time that it occurs, the duration of the arrhythmia and the shape of the arrhythmia and outputs an arrhythmia alarm and data concerning the arrhythmia to the transceiver 19. During analysis of the ECG signals, the embedded ECG analysis software receives the measured change of acceleration for each axis of the accelerometer from the acceleration analysis software and correlates this with the analysis of the ECG signals.
  • the ECG analysis software ceases to output information, particularly arrhythmia data.
  • the microprocessor 13 comprises embedded respiration analysis software and uses this to analyse the respiration signals received from the respiration signal detector 9.
  • the respiration analysis software analyses the impedance signals from the detector 9 to calculate an average respiration rate over a set period of time, e.g. 30 seconds.
  • the respiration analysis software periodically, e.g. every 15 minutes, outputs details of the subject's respiration rate to the transceiver 19.
  • the respiration analysis software is remotely configurable to allow a clinician to set the periodic reporting frequency appropriate for the subject.
  • the respiration analysis software is also remotely configurable to allow a clinician to set a respiration rate limit which is deemed acceptable for the subject.
  • the respiration analysis software analyses the respiration signals to monitor the subject's respiration rate.
  • the microprocessor 13 If this exceeds the set respiration rate limit, the microprocessor 13 outputs a respiration rate alarm and data concerning the respiration rate to the transceiver 19.
  • the embedded respiration analysis software receives the measured change of acceleration for each axis of the accelerometer from the acceleration analysis software and correlates this with the analysis of the respiration signals. At points of time when the measured change of acceleration signal along an axis is equal to or greater than the acceleration change threshold (i.e. motion threshold) for that axis, and for a certain time (e.g. 30 seconds) thereafter, the respiration analysis software ceases to output data.
  • the acceleration change threshold i.e. motion threshold
  • the transceiver 19 outputs data from the physiological monitoring device 1 by a wired or wireless (e.g. WiFi) communication link.
  • the transceiver 19 is able to detect if it is out of range of wireless coverage and generate an alarm for the subject.
  • the physiological monitoring device 1 may comprise a visual and/or audio warning device to indicate various alarms to the subject or a third party (e.g. a nurse), e.g. that electrodes/leads have become disconnected, the power level of the battery is low, that the transceiver is out of wireless range, that abnormal heart or respiration rate has occurred, that an arrhythmia has occurred.
  • the transceiver 19 is also be used to receive commands from the subject's clinician via the server.
  • the output of the physiological monitoring device 1 is received, for example, by a server (not shown) used by a medical practitioner e.g. a clinician monitoring the condition of the subject.
  • the server provides access to the physiological monitoring device 1 by clinicians and other medical staff.
  • Configuration data for the physiological monitoring device such as heart and respiration limits, is stored in the server and input to the physiological monitoring device 1 from the server.
  • the server receives information from the physiological monitoring device 1, stores this data and displays this information for use by a clinician.
  • the server will display various alarms to the clinician, e.g. that electrodes/leads have become disconnected, the power level of the battery is low, that the transceiver is out of wireless range, that abnormal heart or respiration rate has occurred, that an arrhythmia has occurred.
  • the server may also collate the received information and provide trends in physiological data of the subject to the clinician.
  • the server may also perform further analysis of the subject physiological data.
  • the server may comprise a web browser for the input and output of data.
  • components of the device are connected to each other by leads. It will be appreciated, however, that the one or more of the components can be connected to one or more other components by means of a wireless connection.
  • the motion threshold may be determined by a number of methods, as described above.
  • the motion threshold may not be pre-set but may be specifically determined for the subject using the device by any of the methods detailed above.
  • physiological monitoring devices may further comprise components for monitoring other physiological parameters such as subject blood oxygen levels.
  • the method of monitoring physiological signals of a subject in conjunction with monitoring motion of the subject comprises using one or more electrodes to detect physiological signals from the subject, using a motion detector to detect motion of the subject, and using a microprocessor to receive the physiological signals and motion signals from the motion detector, and correlate the motion signals with analysis of the physiological signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded.
  • the motion t reshold for use in the method may be determined by any of the methods detailed above.

Landscapes

  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Medical Informatics (AREA)
  • Animal Behavior & Ethology (AREA)
  • Veterinary Medicine (AREA)
  • Biophysics (AREA)
  • Pathology (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Heart & Thoracic Surgery (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Surgery (AREA)
  • Signal Processing (AREA)
  • Physiology (AREA)
  • Cardiology (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Psychiatry (AREA)
  • Oral & Maxillofacial Surgery (AREA)
  • General Physics & Mathematics (AREA)
  • Dentistry (AREA)
  • Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)

Abstract

A physiological monitoring device (1) adapted to be worn by a subject, comprising one or more electrodes (3a, 3b, 3c, 3d, 3e) adapted to be placed on the subject and to detect physiological signals from the subject, a physiological signal detector (7, 9) which receives the physiological signals from the electrodes, a motion detector (H] which detects motion of the subject, a microprocessor (13) which receives physiological signals from the physiological signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the physiological signals, such that at any point of time when motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded.

Description

Physiological Monitoring Device and Method
The invention relates to a physiological monitoring device and method, for monitoring physiological signals of a subject in conjunction with monitoring motion of the subject.
Various physiological monitoring devices which are wearable by a subject and which monitor physiological signals are known, for example these may monitor ECG and/or respiration signals of the subject. ECG signals may be monitored to determine if an arrhythmia of the subject's heart has occurred. This involves analysis of the ECG signals, and signal to noise ratio is an issue particularly in the mobile environment. If the signal to noise ratio is low, then the analysis of the ECG signals may lead to detection of false arrhythmias. Similarly, respiration signals may be monitored to determine if the respiration rate of the subject is within an acceptable range. This involves analysis of the respiration signals, and signal to noise ratio is again an issue particularly when the subject is mobile. If the signal to noise ratio is low, then the analysis of the respiration signals may lead to detection of false respiration rates. Various sources of noise in ECG and respiration signafs are known, for example noise caused by motion of the subject. To enhance analysis of ECG and respiration signals it is desirable to monitor sources of noise, e.g. subject movement.
According to a first aspect of the invention there is provided a physiological monitoring device adapted to be worn by a subject, comprising
one or more electrodes adapted to be placed on the subject and to detect physiological signals from the subject,
a physiological signal detector which receives the physiological signals from the electrodes,
a motion detector which detects motion of the subject,
a microprocessor which receives physiological signals from the physiological signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the physiological signals, such that at any point of time when motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded. The motion detector may comprise an accelerometer. The accelerometer may be a single- axis accelerometer. The accelerometer may be a multi-axis accelerometer. The accelerometer may be a three-axis accelerometer which measures the acceleration of movement of the subject along three orthogonal axes. The accelerometer may be configured to detect only accelerations, and therefore subject movement, within a predetermined range. The accelerometer may provide information concerning its, and therefore the subject's, orientation e.g. upright, prone.
The microprocessor may comprise embedded motion signal analysis software which it uses to analyse motion signals received from the motion detector.
When the motion detector comprises an accelerometer the microprocessor may analyse acceleration signals from the accelerometer. When the motion detector comprises a multi- axis accelerometer the microprocessor may analyse acceleration signals along each axis.
The microprocessor may analyse the acceleration signals along each axis separately. For each axis, the microprocessor may analyse the acceleration signals along the axis by comparing a measured acceleration signal with a previously-measured acceleration signal to determine a change in acceleration along the axis. For each axis, the microprocessor may analyse the acceleration signals along the axis by comparing a measured acceleration signal with an immediatly-preceding, previously-measured acceleration signal to determine a change in acceleration along the axis. The motion threshold of the subject may be determined as an acceleration change threshold for each axis. The motion threshold of the subject may be determined as an acceleration change threshold of typically O.lg for a sagittal axis, typically O.lg for a transverse axis and typically 0.6g for a frontal axis.
The microprocessor may sum the acceleration signals along each axis and analyse the summed acceleration signals. The microprocessor may analyse the summed acceleration signals by comparing a summed acceleration signal with a previous summed acceleration signal to determine a change in summed acceleration. The motion threshold of the subject may be determined as a summed acceleration change threshold. The motion threshold of the subject may be determined as a summed acceleration change threshold above lg that is considered acceptable for physiological signal detection and reporting.
The motion threshold may be determined from a study of motion and physiological signal detection in one or more normal subjects. Physiological signals and motion signals may be collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement. The activities may comprise, for example, resting in various positions e.g. sitting, standing, lying on back, front, left and right sides, walking on the level and on stairs, running at various rates, cycling, getting dressed, cooking, housework. For the or each subject and each activity, the physiological signals and motion signals are analysed to determine the motion threshold, i.e. a threshold level for the motion signals at or above which it is decided that noise in the physiological signals due to motion of the subject is unacceptable.
The physiological signals detected from the subject may comprise ECG signals detected from the subject's heart.
An ECG motion threshold may be determined from a study of motion and arrhythmia detection in one or more normal subjects i.e. with normal heart function and therefore no arrhythmias. ECG signals and acceleration signals may be collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement. For the or each subject and each activity, the ECG signals and motion signals are analysed to determine the ECG motion threshold, i.e. a threshold level for the motion signals at or above which it is decided that noise in the ECG signals due to motion of the subject is too great to be confident that analysis of the ECG signals will detect a real arrhythmia.
An ECG motion threshold may be determined by analysis of the ECG signals to measure the level of noise in these signals, and setting a value of the ECG motion threshold where the noise level of the ECG signals is considered to be unacceptable.
The microprocessor may comprise embedded ECG analysis software which analyses the ECG signals. The microprocessor may analyse the ECG signals to monitor physiological parameters such as the subject's heart beat rate and ECG features such as P wave shape, QRS width. The microprocessor may use monitoring of these parameters to determine if the subject experiences a heart arrhythmia. The microprocessor may record data concerning an arrhythmia such as the time that it occurs, the duration of the arrhythmia and the shape of the arrhythmia and can output data concerning the arrhythmia. The microprocessor may receive motion signals and correlate these with the analysis of the ECG signals. At points of time when the subject's motion is equal to or greater than the ECG motion threshold, analysis of arrhythmia in the ECG signals may be disregarded, e.g. the ECG analysis software may cease to output arrhythmia data.
The physiological signals detected from the subject may comprise respiration signals detected from the subject.
A respiration motion threshold may be determined from a study of motion and respiration in one or more subjects having normal respiration. Respiration signals and acceleration signals may be collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement. For the or each subject and each activity, the respiration signals and motion signals are analysed to determine the respiration motion threshold, i.e. a threshold level for the motion signals at or above which it is decided that noise in the respiration signals due to motion of the subject is too great to be confident that analysis of the respiration signals will result in a true measure of the subject's respiration.
A respiration motion threshold may be determined by analysis of the respiration signals to measure the level of noise in these signals, and setting a value of the respiration motion threshold where the noise level of the respiration signals is considered to be unacceptable.
The microprocessor may comprise embedded respiration analysis software which analyses the respiration signals. The microprocessor may analyse the respiration signals to monitor physiological parameters such as the subject's respiration rate. The microprocessor may use monitoring of these parameters to determine if the subject experiences abnormal respiration. The microprocessor may record data concerning abnormal respiration such as the time that it occurs, the duration, and can output data concerning the abnormal respiration. The microprocessor may receive motion signals and correlate these with the analysis of the respiration signals. At points of time when the subject's motion is equal to or greater than the respiration motion threshold, analysis of the respiration signals may be disregarded, e.g. the respiration analysis software may cease to output respiration data.
The microprocessor may comprise an analogue to digital converter (ADC). The microprocessor may receive analogue physiological signals and use the ADC to convert these to digital physiological signals for analysis.
The electrodes may be any known electrodes which, when placed on a subject, are able to detect ECG signals from the subject's heart. An ECG signal comprising a difference between the voltage signal between first and second electrodes may be fed to the ECG signal detector. An ECG signal comprising a difference between the voltage signal between first and third electrodes may be fed to the ECG signal detector. These two signals may be used in the assessment of physiological parameters of the subject.
The electrodes may be any known electrodes which, when placed on a subject, are able to detect respiration signals from the subject. The electrodes may measure fluctuations in impedance signals from the subject's lungs during breathing. The impedance signals may be used in the assessment of the respiration of the subject.
The physiological monitoring device may further comprise a temperature sensor for measuring the temperature of the subject's skin.
According to a second aspect of the invention there is provided a method of monitoring physiological signals of a subject in conjunction with monitoring motion of the subject comprising
using one or more electrodes to detect physiological signals from the subject, using a motion detector to detect motion of the subject,
using a microprocessor to receive the physiological signals and motion signals and correlate the motion signals with analysis of the physiological signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded. β
According to a third aspect of the invention there is provided a physiological monitoring device adapted to be worn by a subject, comprising
one or more electrodes adapted to be placed on the subject and to detect ECG signals from the subject's heart,
an ECG signal detector which receives the ECG signals from the electrodes, a motion detector which detects motion of the subject,
a microprocessor which receives ECG signals from the ECG signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the ECG signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of arrhythmia in the ECG signals is disregarded.
According to a fourth aspect of the invention there is provided a method of monitoring ECG signals of a subject in conjunction with monitoring motion of the subject comprising
using one or more electrodes to detect ECG signals from the subject's heart, using a motion detector to detect motion of the subject,
using a microprocessor to receive the ECG signals and motion signals and correlate the motion signals with analysis of the ECG signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of arrhythmia in the ECG signals is disregarded.
In this way, detection of 'false' arrhythmias may be reduced.
According to a fifth aspect of the invention there is provided a physiological monitoring device adapted to be worn by a subject, comprising
one or more electrodes adapted to be placed on the subject and to detect respiration signals from the subject,
a respiration signal detector which receives the respiration signals from the electrodes,
a motion detector which detects motion of the subject,
a microprocessor which receives respiration signals from the respiration signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the respiration signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the respiration signals is disregarded.
According to a sixth aspect of the invention there is provided a method of monitoring respiration signals of a subject in conjunction with monitoring motion of the subject comprising
using one or more electrodes to detect respiration signals from the subject, using a motion detector to detect motion of the subject,
using a microprocessor to receive the respiration signals and motion signals and correlate the motion signals with analysis of the respiration signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the respiration signals is disregarded.
Embodiments of the invention will now be described by way of example only, with reference to the accompanying drawings, in which
Figure 1 is a schematic representation of a physiological monitoring device according to the first aspect of the invention, and
Figure 2 is a flowchart representing the method of the second aspect of the invention.
Referring to Figure 1, the physiological monitoring device 1 comprises three electrodes 3a, 3b, 3c for measuring ECG signals, two electrodes 3d, 3e for measuring respiration signals, a temperature sensor 5, an ECG signal detector 7, a respiration detector 9, a motion sensor 11 a microprocessor 13 and a transceiver 19.
In this embodiment, the electrodes 3a to 3e and the temperature sensor 5 are housed in a flexible patch (not shown) which is attached to the subject by an adhesive layer. The flexible patch is made of a biocompatible material. The ECG signal detector 7, the respiration detector 9, the motion sensor 11 and the microprocessor 13 are contained within a device (not shown) which removably attaches to the flexible patch by clips. Five clips are provided on the device which attach to corresponding clips on the flexible patch. a
When connected, three of the clips each provide a signal from one of electrodes 3a, 3b, 3c in the flexible patch to the ECG signal detector 7 in the device, and the remaining two clips each provide a signal from one of electrodes 3d, 3e in the flexible patch to the respiration signal detector 9 in the device. It will be appreciated that other arrangements of the components of the physiological monitoring device may be employed. The device further contains a battery (not shown) for providing power to the other components contained in the device. The device comprises charger connections (not shown) for connection of the device battery to a charger. The device further comprises a battery power monitoring device (not shown). When the battery power approaches a preset level, a warning light provided on the device (not shown) is lit to indicate to the subject that the battery of the device should be charged.
The electrodes 3a, 3b, 3c may comprise any known electrode which, when placed on a subject, are able to detect ECG signals from the subject's heart. The electrodes may, for example, be placed on the subject's torso or on the subject's chest. The first electrode 3a is closest to the subject's left leg, a second electrode 3b is closest to the subject's right arm and a third electrode 3c is closest to the subject's left arm. It will be appreciated, however, that other electrode positions may be used. Each of the electrodes 3a, 3b, 3c is connected by a lead and clips to the ECG signal detector 7. An ECG signal comprising a difference in voltage between the signal of electrode 3b and the signal of electrode 3a is fed to the ECG signal detector 7. An ECG signal comprising a difference in voltage between the signal of electrode 3c and the signal of electrode 3a is fed to the ECG signal detector 7. These two signals are used in the assessment of the ECG physiological parameters of the subject. A signal is also sent to the ECG signal detector 7 from electrode 3a, this signal is used as a reference signal. In this embodiment of the invention the physiological monitoring device comprises three electrodes for ECG measurement, but it will be appreciated that other embodiments may comprise more or less electrodes.
The electrodes 3d, 3e may comprise any known electrode which, when placed on a subject, are able to detect respiration signals from the subject. The electrodes may, for example, be placed on the subject's torso or on the subject's chest. A known sinusoida l signal at a specific frequency (57kHz) is injected into the electrodes 3d and 3e, and impedance changes caused by fluctuations of the volume of air in the subject's lungs are measured by the electrodes. The impedance signals are fed by a lead to the respiration signal detector 9.
The temperature sensor 5 comprises a thermistor. This measures resistance signals of the subject's skin and passes these signals via a lead to the microprocessor 13 where they are used as an indication of the temperature of the subject's skin.
The ECG signal detector 7 comprises analogue hardware and is able to receive continuous signals from the electrodes 3a, 3b, 3c. In this embodiment the ECG signal detector 7 comprises a noise filter 15, having a passband of approximately 0.5 Hz to 25 Hz. The noise filter 15 attempts to filter noise from the signals received from the electrodes caused by, for example, mobile phone interference or movement by the subject. The efficacy of the noise filter 15 is limited as it has to have a certain passband to achieve adequate detection of the ECG signals. The size of the passband, however, allows some motion artefacts to be detected which results in noise in the ECG signals. The ECG signal detector 7 is connected to the microprocessor 13 and sends filtered ECG signals to the microprocessor 13.
The respiration signal detector 9 also comprises analogue hardware and receives impedance signals from the electrodes 3d and 3e. Measurement of the impedance signals is highly susceptible to motion artefacts, and the respiration signal detector 9 comprises a noise filter. The noise filter attempts to filter noise due to motion from the impedance signals received from the electrodes. The respiration signal detector 9 is connected to the microprocessor 13 and sends filtered respiration signals to the microprocessor 13.
The motion detector 11 of the physiological monitoring device 1 comprises a three-axis accelerometer for measurement of three axis motion and orientation of the subject. The accelerometer comprises a digital accelerometer micro electro mechanical system and measures the acceleration of motion of the subject along three orthogonal axes. The accelerometer may be configured to detect only accelerations, and therefore subject movement, within a pre-determined range. The accelerometer is connected to the microprocessor 13. Signals comprising the acceleration along each axis are sent to the microprocessor 13. The microprocessor 13 comprises an analogue to digital converter (ADC) 17. This receives the filtered ECG signals from the ECG signal detector 5 and the filtered respiration signals from the respiration signal detector 7 and samples these signals, e.g. at a rate of 360 samples per second to create digital ECG signals and at a rate of 120 samples per second to create digital respiration signals.
The microprocessor 13 comprises embedded acceleration analysis software which it uses to analyse the three acceleration signals from the motion detector 11. The acceleration analysis software analyses the acceleration signals received from each axis of the accelerometer separately. For each axis, the acceleration analysis software analyses the acceleration signals along the axis by comparing a measured acceleration signal with a previously-measured acceleration signal to determine a change in acceleration along the axis. The acceleration analysis software sets a motion threshold of the subject as an acceleration change threshold for each axis, for example O.lg for a sagittal axis, O.lg for a transverse axis and 0.6g for a frontal axis.
The microprocessor 13 comprises embedded ECG analysis software and uses this to analyse the ECG signals received from the ECG signal detector 7. The ECG analysis software periodically, e.g. every 15 minutes, outputs details of the subject's heart rate to the transceiver 19. The ECG analysis software is remotely configurable to allow a clinician to set the periodic reporting frequency appropriate for the subject. The respiration analysis software is also remotely configurable to allow a clinician to set a heart rate limit which is deemed acceptable for the subject. The ECG analysis software analyses the ECG signals to monitor the subject's heart beat rate. If this exceeds the set heart rate limit, the microprocessor 13 outputs a heart rate alarm and data concerning the heart rate to the transceiver 19. The ECG analysis software further analyses the ECG signals to monitor ECG features such as P wave shape, QRS width. The ECG analysis software periodically outputs such details of the subject's ECG signals to the transceiver 19. The ECG analysis software uses monitoring of these ECG parameters to determine if the subject experiences a heart arrhythmia, for example any of asystole, ventricular fibrillation, ventricular tachycardia, bradycardia, tachycardia, atrial fibrillation, supra-ventricular fibrillation. ECG arrhythmia detection and analysis is based on the Pan-Tompkins method and uses analysis of the slope, amplitude and width of QRS complexes to detect an arrhythmia. The arrhythmia detection and analysis algorithm used includes a series of steps which include lowpass and highpass filtering, derivative, squaring, and integration functions, and adaptive thresholding and search procedures. If an arrhythmia occurs the ECG analysis software records data concerning the arrhythmia such as the time that it occurs, the duration of the arrhythmia and the shape of the arrhythmia and outputs an arrhythmia alarm and data concerning the arrhythmia to the transceiver 19. During analysis of the ECG signals, the embedded ECG analysis software receives the measured change of acceleration for each axis of the accelerometer from the acceleration analysis software and correlates this with the analysis of the ECG signals. At points of time when the measured change of acceleration signal along an axis is equal to or greater than the acceleration change threshold (motion threshold) for that axis, and for a certain time (e.g. 30 seconds) thereafter, the ECG analysis software ceases to output information, particularly arrhythmia data.
The microprocessor 13 comprises embedded respiration analysis software and uses this to analyse the respiration signals received from the respiration signal detector 9. The respiration analysis software analyses the impedance signals from the detector 9 to calculate an average respiration rate over a set period of time, e.g. 30 seconds. The respiration analysis software periodically, e.g. every 15 minutes, outputs details of the subject's respiration rate to the transceiver 19. The respiration analysis software is remotely configurable to allow a clinician to set the periodic reporting frequency appropriate for the subject. The respiration analysis software is also remotely configurable to allow a clinician to set a respiration rate limit which is deemed acceptable for the subject. The respiration analysis software analyses the respiration signals to monitor the subject's respiration rate. If this exceeds the set respiration rate limit, the microprocessor 13 outputs a respiration rate alarm and data concerning the respiration rate to the transceiver 19. During analysis of the respiration signals, the embedded respiration analysis software receives the measured change of acceleration for each axis of the accelerometer from the acceleration analysis software and correlates this with the analysis of the respiration signals. At points of time when the measured change of acceleration signal along an axis is equal to or greater than the acceleration change threshold (i.e. motion threshold) for that axis, and for a certain time (e.g. 30 seconds) thereafter, the respiration analysis software ceases to output data.
The transceiver 19 outputs data from the physiological monitoring device 1 by a wired or wireless (e.g. WiFi) communication link. The transceiver 19 is able to detect if it is out of range of wireless coverage and generate an alarm for the subject.
The physiological monitoring device 1 may comprise a visual and/or audio warning device to indicate various alarms to the subject or a third party (e.g. a nurse), e.g. that electrodes/leads have become disconnected, the power level of the battery is low, that the transceiver is out of wireless range, that abnormal heart or respiration rate has occurred, that an arrhythmia has occurred. The transceiver 19 is also be used to receive commands from the subject's clinician via the server.
The output of the physiological monitoring device 1 is received, for example, by a server (not shown) used by a medical practitioner e.g. a clinician monitoring the condition of the subject. The server provides access to the physiological monitoring device 1 by clinicians and other medical staff. Configuration data for the physiological monitoring device, such as heart and respiration limits, is stored in the server and input to the physiological monitoring device 1 from the server. The server receives information from the physiological monitoring device 1, stores this data and displays this information for use by a clinician. The server will display various alarms to the clinician, e.g. that electrodes/leads have become disconnected, the power level of the battery is low, that the transceiver is out of wireless range, that abnormal heart or respiration rate has occurred, that an arrhythmia has occurred. The server may also collate the received information and provide trends in physiological data of the subject to the clinician. The server may also perform further analysis of the subject physiological data. The server may comprise a web browser for the input and output of data.
In the embodiment of the invention described above, components of the device are connected to each other by leads. It will be appreciated, however, that the one or more of the components can be connected to one or more other components by means of a wireless connection. The motion threshold may be determined by a number of methods, as described above.
In an alternative embodiment of the physiological device of the invention, the motion threshold may not be pre-set but may be specifically determined for the subject using the device by any of the methods detailed above.
It will be appreciated that physiological monitoring devices according to the invention may further comprise components for monitoring other physiological parameters such as subject blood oxygen levels.
Referring to Figure 2, the method of monitoring physiological signals of a subject in conjunction with monitoring motion of the subject comprises using one or more electrodes to detect physiological signals from the subject, using a motion detector to detect motion of the subject, and using a microprocessor to receive the physiological signals and motion signals from the motion detector, and correlate the motion signals with analysis of the physiological signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded.
The motion t reshold for use in the method may be determined by any of the methods detailed above.

Claims

1. A physiological monitoring device adapted to be worn by a subject, comprising
one or more electrodes adapted to be placed on the subject and to detect physiological signals from the subject,
a physiological signal detector which receives the physiological signals from the electrodes,
a motion detector which detects motion of the subject,
a microprocessor which receives physiological signals from the physiological signal detector and motion signals from the motion detector, and correlates the motion signals with analysis of the physiological signals, such that at any point of time when motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded.
2. A device according to claim 1 in which the motion detector comprises a three-axis accelerometer which measures the acceleration of movement of the subject along three orthogonal axes and the microprocessor analyses the acceleration signals along each axis separately.
3. A device according to claim 2 in which the microprocessor analyses the acceleration signals along each axis by comparing a measured acceleration signal with a previously- measured acceleration signal to determine a change in acceleration along the axis and the motion threshold of the subject is determined as an acceleration change threshold for each axis.
4. A device according to claim 3 in which the microprocessor analyses the acceleration signals along each axis by comparing a measured acceleration signal with an immediatly- preceding, previously-measured acceleration signal to determine a change in acceleration along the axis and the motion threshold of the subject is determined as an acceleration change threshold for each axis.
5. A device according to any preceding claim in which the motion threshold is determined from a study of motion and physiological signal detection in one or more normal subjects.
6. A device according to claim 5 in which physiological signals and motion signals are collected for the or each subject as the subject undertakes various activities, each with a different type/level of movement.
7. A device according to any preceding claim in which the physiological signals detected from the subject comprise ECG signals detected from the subject's heart.
8. A device according to claim 7 in which an ECG motion threshold is determined from a study of motion and arrhythmia detection in one or more normal subjects i.e. with normal heart function and therefore no arrhythmias.
9. A device according to claim 7 in which an ECG motion threshold is determined by analysis of the ECG signals to measure the level of noise in these signals, and setting a value of the ECG motion threshold where the noise level of the ECG signals is considered to be unacceptable.
10. A device accordin to claim 8 or claim 9 in which the microprocessor receives motion signals and correlates these with the analysis of the ECG signals and at points of time when the subject's motion is equal to or greater than the ECG motion threshold, analysis of arrhythmia in the ECG signals is disregarded.
11. A device according to any preceding claim in which the physiological signals detected from the subject comprise respiration signals detected from the subject.
12. A device according to claim 11 in which a respiration motion threshold is determined from a study of motion and respiration in one or more subjects having normal respiration.
13. A device according to claim 11 in which a respiration motion threshold is determined by analysis of the respiration signals to measure the level of noise in these signals, and setting a value of the respiration motion threshold where the noise level of the respiration signals is considered to be unacceptable.
14. A device according to claim 12 or claim 13 in which the microprocessor receives motion signals and correlates these with the analysis of the respiration signals and at points of time when the subject's motion is equal to or greater than the respiration motion threshold, analysis of the respiration signals is disregarded.
15. A method of monitoring physiological signals of a subject in conjunction with monitoring motion of the subject comprising
using one or more electrodes to detect physiological signals from the subject, using a motion detector to detect motion of the subject,
using a microprocessor to receive the physiological signals and motion signals and correlate the motion signals with analysis of the physiological signals, such that at any point of time when the motion of the subject is equal to or greater than a motion threshold, analysis of the physiological signals is disregarded.
PCT/GB2010/002327 2009-12-24 2010-12-23 Physiological monitoring device and method WO2011077097A1 (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
GB201212695A GB2488743B (en) 2009-12-24 2010-12-23 Physiological monitoring device and method

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
GBGB0922622.6A GB0922622D0 (en) 2009-12-24 2009-12-24 Physiological monitoring device and method
GB0922622.6 2009-12-24

Publications (1)

Publication Number Publication Date
WO2011077097A1 true WO2011077097A1 (en) 2011-06-30

Family

ID=41716956

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/GB2010/002327 WO2011077097A1 (en) 2009-12-24 2010-12-23 Physiological monitoring device and method

Country Status (2)

Country Link
GB (2) GB0922622D0 (en)
WO (1) WO2011077097A1 (en)

Cited By (11)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2479255A (en) * 2010-03-31 2011-10-05 Gen Electric System and method of performing electrocardiography with motion detection
US9173670B2 (en) 2013-04-08 2015-11-03 Irhythm Technologies, Inc. Skin abrader
US9241649B2 (en) 2010-05-12 2016-01-26 Irhythm Technologies, Inc. Device features and design elements for long-term adhesion
EP2992817A1 (en) * 2014-09-03 2016-03-09 Samsung Electronics Co., Ltd. Electronic device and method for measuring vital signal
WO2016145438A1 (en) * 2015-03-12 2016-09-15 Medicomp, Inc. Earbud electrocardiogram monitor and associated systems and methods
US9597004B2 (en) 2014-10-31 2017-03-21 Irhythm Technologies, Inc. Wearable monitor
US10271754B2 (en) 2013-01-24 2019-04-30 Irhythm Technologies, Inc. Physiological monitoring device
US11083371B1 (en) 2020-02-12 2021-08-10 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network
US11246523B1 (en) 2020-08-06 2022-02-15 Irhythm Technologies, Inc. Wearable device with conductive traces and insulator
US11350865B2 (en) 2020-08-06 2022-06-07 Irhythm Technologies, Inc. Wearable device with bridge portion
US11998342B2 (en) 2022-11-10 2024-06-04 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050240087A1 (en) * 2003-11-18 2005-10-27 Vivometrics Inc. Method and system for processing data from ambulatory physiological monitoring
US20070030587A1 (en) * 2005-08-05 2007-02-08 Hitachi Metals Ltd. Fall detection device
US20090221937A1 (en) * 2008-02-25 2009-09-03 Shriners Hospitals For Children Activity Monitoring

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070156190A1 (en) * 2005-12-30 2007-07-05 Can Cinbis Subcutaneous ICD with motion artifact noise suppression
US8849425B2 (en) * 2011-05-03 2014-09-30 Biotronik Se & Co. Kg Implantable apparatus for detection of external noise using motion sensor signal

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20050240087A1 (en) * 2003-11-18 2005-10-27 Vivometrics Inc. Method and system for processing data from ambulatory physiological monitoring
US20070030587A1 (en) * 2005-08-05 2007-02-08 Hitachi Metals Ltd. Fall detection device
US20090221937A1 (en) * 2008-02-25 2009-09-03 Shriners Hospitals For Children Activity Monitoring

Cited By (44)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2479255B (en) * 2010-03-31 2015-03-18 Gen Electric System and method of performing electrocardiography with motion detection
GB2479255A (en) * 2010-03-31 2011-10-05 Gen Electric System and method of performing electrocardiography with motion detection
US11141091B2 (en) 2010-05-12 2021-10-12 Irhythm Technologies, Inc. Device features and design elements for long-term adhesion
US9241649B2 (en) 2010-05-12 2016-01-26 Irhythm Technologies, Inc. Device features and design elements for long-term adhesion
US10517500B2 (en) 2010-05-12 2019-12-31 Irhythm Technologies, Inc. Device features and design elements for long-term adhesion
US10405799B2 (en) 2010-05-12 2019-09-10 Irhythm Technologies, Inc. Device features and design elements for long-term adhesion
US11051738B2 (en) 2013-01-24 2021-07-06 Irhythm Technologies, Inc. Physiological monitoring device
US10555683B2 (en) 2013-01-24 2020-02-11 Irhythm Technologies, Inc. Physiological monitoring device
US11627902B2 (en) 2013-01-24 2023-04-18 Irhythm Technologies, Inc. Physiological monitoring device
US10271754B2 (en) 2013-01-24 2019-04-30 Irhythm Technologies, Inc. Physiological monitoring device
US9173670B2 (en) 2013-04-08 2015-11-03 Irhythm Technologies, Inc. Skin abrader
US9451975B2 (en) 2013-04-08 2016-09-27 Irhythm Technologies, Inc. Skin abrader
EP2992817A1 (en) * 2014-09-03 2016-03-09 Samsung Electronics Co., Ltd. Electronic device and method for measuring vital signal
US11166675B2 (en) 2014-09-03 2021-11-09 Samsung Electronics Co., Ltd Electronic device and method for measuring vital signal
US11617539B2 (en) 2014-09-03 2023-04-04 Samsung Electronics Co., Ltd Electronic device and method for measuring vital signal
CN105380597A (en) * 2014-09-03 2016-03-09 三星电子株式会社 Electronic device and method for measuring vital signal
US10299691B2 (en) 2014-10-31 2019-05-28 Irhythm Technologies, Inc. Wearable monitor with arrhythmia burden evaluation
US10813565B2 (en) 2014-10-31 2020-10-27 Irhythm Technologies, Inc. Wearable monitor
US10667712B2 (en) 2014-10-31 2020-06-02 Irhythm Technologies, Inc. Wearable monitor
US11756684B2 (en) 2014-10-31 2023-09-12 Irhythm Technologies, Inc. Wearable monitor
US11289197B1 (en) 2014-10-31 2022-03-29 Irhythm Technologies, Inc. Wearable monitor
US10098559B2 (en) 2014-10-31 2018-10-16 Irhythm Technologies, Inc. Wearable monitor with arrhythmia burden evaluation
US9955887B2 (en) 2014-10-31 2018-05-01 Irhythm Technologies, Inc. Wearable monitor
US9597004B2 (en) 2014-10-31 2017-03-21 Irhythm Technologies, Inc. Wearable monitor
US11605458B2 (en) 2014-10-31 2023-03-14 Irhythm Technologies, Inc Wearable monitor
WO2016145438A1 (en) * 2015-03-12 2016-09-15 Medicomp, Inc. Earbud electrocardiogram monitor and associated systems and methods
US11497432B2 (en) 2020-02-12 2022-11-15 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless
US11083371B1 (en) 2020-02-12 2021-08-10 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network
US11925469B2 (en) 2020-02-12 2024-03-12 Irhythm Technologies, Inc. Non-invasive cardiac monitor and methods of using recorded cardiac data to infer a physiological characteristic of a patient
US11246524B2 (en) 2020-02-12 2022-02-15 Irhythm Technologies, Inc. Non-invasive cardiac monitor and methods of using recorded cardiac data to infer a physiological characteristic of a patient
US11375941B2 (en) 2020-02-12 2022-07-05 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network
US11382555B2 (en) 2020-02-12 2022-07-12 Irhythm Technologies, Inc. Non-invasive cardiac monitor and methods of using recorded cardiac data to infer a physiological characteristic of a patient
US11253186B2 (en) 2020-02-12 2022-02-22 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network
US11253185B2 (en) 2020-02-12 2022-02-22 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network
US11504041B2 (en) 2020-08-06 2022-11-22 Irhythm Technologies, Inc. Electrical components for physiological monitoring device
US11589792B1 (en) 2020-08-06 2023-02-28 Irhythm Technologies, Inc. Wearable device with bridge portion
US11399760B2 (en) 2020-08-06 2022-08-02 Irhythm Technologies, Inc. Wearable device with conductive traces and insulator
US11350864B2 (en) 2020-08-06 2022-06-07 Irhythm Technologies, Inc. Adhesive physiological monitoring device
US11246523B1 (en) 2020-08-06 2022-02-15 Irhythm Technologies, Inc. Wearable device with conductive traces and insulator
US11337632B2 (en) 2020-08-06 2022-05-24 Irhythm Technologies, Inc. Electrical components for physiological monitoring device
US11751789B2 (en) 2020-08-06 2023-09-12 Irhythm Technologies, Inc. Wearable device with conductive traces and insulator
US11806150B2 (en) 2020-08-06 2023-11-07 Irhythm Technologies, Inc. Wearable device with bridge portion
US11350865B2 (en) 2020-08-06 2022-06-07 Irhythm Technologies, Inc. Wearable device with bridge portion
US11998342B2 (en) 2022-11-10 2024-06-04 Irhythm Technologies, Inc. Methods and systems for processing data via an executable file on a monitor to reduce the dimensionality of the data and encrypting the data being transmitted over the wireless network

Also Published As

Publication number Publication date
GB2488743A (en) 2012-09-05
GB2488743B (en) 2015-05-06
GB201212695D0 (en) 2012-08-29
GB0922622D0 (en) 2010-02-10

Similar Documents

Publication Publication Date Title
WO2011077097A1 (en) Physiological monitoring device and method
US11562825B2 (en) Methods and systems to determine multi-parameter managed alarm hierarchy during patient monitoring
CN107920766B (en) Sleep phenomenon monitoring
US9585590B2 (en) Computerized systems and methods for stability-theoretic prediction and prevention of sudden cardiac death
CN103517669B (en) The method and system of multiparameter administrative alert grade is determined during patient monitoring
JP5174348B2 (en) Method and apparatus for monitoring heart related condition parameters
US20160038061A1 (en) Method for detecting falls and a fall detector
JP5865362B2 (en) Improved system and method for detecting symptoms of hypoglycemia
US20090048540A1 (en) Wearable Health Monitoring Device and Methods for Fall Detection
CN108309263A (en) Multi-parameter monitoring data analysing method and multi-parameter monitoring system
CN108577830A (en) A kind of user oriented sign information dynamic monitor method and dynamic monitor system
EP3451901A1 (en) A method and apparatus for determining at least one of a position and an orientation of a wearable device on a subject
JP6692498B2 (en) Method and system for monitoring a user's cardiac activity
JP2005521456A (en) Sudden cardiac arrest monitoring system
CN105451648A (en) System and method for detecting worsening of heart failure based on rapid shallow breathing index
JP6450025B2 (en) Respiration estimation method and apparatus
CN105473064A (en) System and method for detecting worsening of heart failure based on tidal volume
Qiu et al. A wearable bioimpedance chest patch for IoHT-connected respiration monitoring
Mohammadi-Koushki et al. A wearable device for continuous cardiorespiratory System Monitoring
Ramachandran et al. Development of real-time ECG signal monitoring system for telemedicine application
Ishiyama et al. RRI Real-Time Measurement System Using Capacitive Coupled Electrodes: Consideration on Body Movemant
EP3282939B1 (en) Alignment of a detachable monitoring device
Kim et al. Development of ECG and BCG measuring system on moving wheelchair using CDMA network
Giorgio A wireless electronic device for the personal safety of chronically ill persons for indoor and outdoor use
RU2702605C2 (en) Calculation of central cavity perfusion

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 10809235

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

ENP Entry into the national phase

Ref document number: 1212695

Country of ref document: GB

Kind code of ref document: A

Free format text: PCT FILING DATE = 20101223

WWE Wipo information: entry into national phase

Ref document number: 1212695.9

Country of ref document: GB

122 Ep: pct application non-entry in european phase

Ref document number: 10809235

Country of ref document: EP

Kind code of ref document: A1