USRE38492E1 - Signal processing apparatus and method - Google Patents

Signal processing apparatus and method Download PDF

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USRE38492E1
USRE38492E1 US10095586 US9558602A USRE38492E1 US RE38492 E1 USRE38492 E1 US RE38492E1 US 10095586 US10095586 US 10095586 US 9558602 A US9558602 A US 9558602A US RE38492 E1 USRE38492 E1 US RE38492E1
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signal
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noise
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Mohamed Kheir Diab
Massi E. Kiani
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JP Morgan Chase Bank
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Masimo Corp
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06KRECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
    • G06K9/00Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
    • G06K9/00496Recognising patterns in signals and combinations thereof
    • G06K9/00503Preprocessing, e.g. filtering
    • G06K9/0051Denoising
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/1455Measuring 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
    • A61B5/14551Measuring 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
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7203Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
    • A61B5/7207Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts
    • A61B5/7214Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal of noise induced by motion artifacts using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/72Signal processing specially adapted for physiological signals or for diagnostic purposes
    • A61B5/7228Signal modulation applied to the input signal sent to patient or subject; demodulation to recover the physiological signal
    • HELECTRICITY
    • H04ELECTRIC COMMUNICATION TECHNIQUE
    • H04BTRANSMISSION
    • H04B1/00Details of transmission systems, not covered by a single one of groups H04B3/00 - H04B13/00; Details of transmission systems not characterised by the medium used for transmission
    • H04B1/06Receivers
    • H04B1/10Means associated with receiver for limiting or suppressing noise or interference induced by transmission
    • H04B1/12Neutralising, balancing, or compensation arrangements
    • H04B1/123Neutralising, balancing, or compensation arrangements using adaptive balancing or compensation means

Abstract

A signal processor which acquires a first signal, including a first desired signal portion and a first undesired signal portion, and a second signal, including a second desired signal portion and a second undesired signal portion, wherein the first and second desired signal portions are correlated. The signals may be acquired by propagating energy through a medium and measuring an attenuated signal after transmission or reflection. Alternatively, the signals may be acquired by measuring energy generated by the medium. A processor generates a noise reference signal which is a combination of only the undesired signal portions and is correlated to both the first and second undesired signal portions. The noise reference signal is then used to remove the undesired portion of each of the first and second measured signals via an adaptive noise canceler, preferably of the joint process estimator type. The processor may be employed in conjunction with an adaptive noise canceler in physiological monitors wherein the know properties of energy attenuation through a medium are used to determine physiological characteristics of the medium. Many physiological conditions, such as the pulse of a patient or the concentration of a constituent in a medium, can be determined from the desired portion of the signal after undesired signal portions, such as those caused by erratic motion, are removed.

Description

The present application is a continuation of application Ser. No. 08/479,918, now U.S. Pat. No. 5,769,785, filed Jun. 7, 1995, which is a continuation of application Ser. No. 08/249,690, now U.S. Pat. No. 5,482,036, filed May 26, 1994, which is a continuation of application Ser. No. 07/666,060 filed Mar. 7, 1991, now abandoned.

FIELD OF THE INVENTION

The present invention relates to the field of signal processing. More specifically, the present invention relates to the processing of measured signals to remove undesired portions when little is known about the undesired signal portion.

BACKGROUND OF THE INVENTION

Signal processors are typically employed to remove undesired portions from a composite measured signal including a desired signal portion and an undesired signal portion. If the undesired signal portion occupies a different frequency spectrum than the desired signal, then conventional filtering techniques such as low pass, band pass, and high pass filtering could be used to separate the desired portion from the total signal. Fixed single or multiple notch filters could also be employed if the undesired signal portion(s) exist at a fixed frequency(s).

However, it is often the case that an overlap in frequency spectrum between the desired and undesired signal portions does exist and the statistical properties of both signal portions change with time. In such cases, conventional filtering techniques are totally ineffective in extracting the desired signal. If, however, a description of the undesired portion can be made available, adaptive noise canceling can be employed to remove the undesired portion of the signal leaving the desired portion available for measurement. Adaptive noise cancelers dynamically change their transfer function to adapt to and remove the undesired signal portions of a composite signal. Adaptive noise cancelers require a noise reference signal which is correlated to the undesired signal portion. The noise reference signal is not necessarily a representation of the undesired signal portion, but has a frequency spectrum which is similar to that of the undesired signal. In many cases, it requires considerable ingenuity to determine a noise reference signal since nothing is a priori known about the undesired signal portion.

One area where composite measured signals comprise a desired signal portion and an undesired signal portion about which no information can easily be determined is physiological monitoring. Physiological monitoring apparatuses generally measure signals derived from a physiological system, such as the human body. Measurements which are typically taken with physiological monitoring systems include electron cardiographs, blood pressure, blood gas saturation (such as oxygen saturation), capnographs, heart rate, respiration rate, and depth of anesthesia, for example. Other types of measurements include those which measure the pressure and quantity of a substance within the body such as breathalizer testing, drug testing, cholesterol testing, glucose testing, arterial carbon dioxide testing, protein testing, and carbon monoxide testing, for example. The source of the undesired signal portion in these measurements is often due to motion of the patient, both external and internal (muscle movement, for example), during the measurement process.

Knowledge of physiological systems, such as the amount of oxygen in a patient's blood, can be critical, for example during surgery. Data can be determined by a lengthy invasive procedure of extracting and testing matter, such as blood, from a patient, or by more expedient, non-invasive measures. Many types of non-invasive measurements can be made by using the known properties of energy attenuation as a selected form of energy passes through a medium.

Energy is caused to be incident on a medium either derived from or contained within a patient and the amplitude of transmitted or reflected energy is then measured. The amount of attenuation of the incident energy caused by the medium is strongly dependent on the thickness and composition of the medium through which the energy must pass as well as the specific form of energy selected. Information about a physiological system can be derived from data taken from the attenuated signal of the incident energy transmitted through the medium if the noise can be removed. However, non-invasive measurements often do not afford the opportunity to selectively observe the interference causing the undesired signal portion, making it difficult to remove.

These undesired signal portions often originate from both AC and DC sources. The first undesired portion is an easily removed DC component caused by transmission of the energy through differing media which are of relatively constant thickness within the body, such as bone, tissue, skin, blood, etc. Second, is an erratic AC component caused when differing media being measured are perturbed and thus, change in thickness while the measurement is being made. Since most materials in and derived from the body are easily compressed, the thickness of such matter changes if the patient moves during a non-invasive physiological measurement. Patient movement can cause the properties of energy attenuation to vary erratically. Traditional signal filtering techniques are frequently totally ineffective and grossly deficient in removing these motion induced effects from a signal. The erratic or unpredictable nature of motion induced undesired signal components is the major obstacle in removing them. Thus, presently available physiological monitors generally become totally inoperative during time periods when the patient moves.

A blood gas monitor is one example of a physiological monitoring system which is based upon the measurement of energy attenuated by biological tissues or substances. Blood gas monitors transmit light into the tissue and measure the attenuation of the light as a function of time. The output signal of a blood gas monitor which is sensitive to the arterial blood flow contains a component which is a waveform representative of the patient's arterial pulse. This type of signal, which contains a component related to the patient's pulse, is called a plethysmographic wave, and is shown in FIG. 1 as curve Y. Plethysmographic waveforms are used in blood pressure or blood gas saturation measurements, for example. As the heart beats the amount of blood in the arteries increases and decreases, causing increases and decreases in energy attenuation, illustrated by the cyclic wave Y in FIG. 1.

Typically, a digit such as a finger, an ear lobe, or other portion of the body where blood flows close to the skin, is employed as the medium through which light energy is transmitted for blood gas attenuation measurements. The finger comprises skin, fat, bone, muscle, etc., shown schematically in FIG. 2, each of which attenuates energy incident on the finger in a generally predictable and constant manner. However, when fleshy portions of the finger are compressed erratically, for example by motion of the finger, energy attenuation becomes erratic.

An example of a more realistic measured waveform S is shown in FIG. 3, illustrating the effect of motion. The desired portion of the signal Y is the waveform representative of the pulse, corresponding to the sawtooth-like pattern wave in FIG. 1. The large, motion-induced excursions in signal amplitude hide the desired signal Y. It is easy to see how even small variations in amplitude make it difficult to distinguish the desired signal Y in the presence of a noise component n.

A specific example of a blood gas monitoring apparatus is a pulse oximeter which measures the saturation of oxygen in the blood. The pumping of the heart forces freshly oxygenated blood into the arteries causing greater energy attenuation. The saturation of oxygenated blood may be determined from the depth of the valleys relative to the peaks of two plethysmographic waveforms measured at separate wavelengths. However, motion induced undesired signal portions, or motion artifacts, must be removed from the measured signal for the oximeter to continue the measurement during periods when the patient moves.

SUMMARY OF THE INVENTION

The present invention is a signal processor which acquires a first signal and a second signal that is correlated to the first signal. The first signal comprises a first desired signal portion and a first undesired signal portion. The second signal comprises a second desired signal portion and a second undesired signal portion. The signals may be acquired by propagating energy through a medium and measuring an attenuated signal after transmission or reflection. Alternatively, the signal may be acquired by measuring energy generated by the medium.

The first and second measured signals are processed to generate a noise reference signal which does not contain the desired signal portions from either of the first or second measured signals. The remaining undesired signal portions from the first and second measured signals are combined to form a noise reference signal. This noise reference signal is correlated to the undesired signal portion of each of the first and second measured signals.

The noise reference signal is then used to remove the undesired portion of each of the first and second measured signals via an adaptive noise canceler. An adaptive noise canceler can be described by analogy to a dynamic multiple notch filter which dynamically changes its transfer function in response to the noise reference signal and the measured signals to remove frequencies from the measured signals that are also present in the noise reference signal. Thus, a typical adaptive noise canceler receives the signal from which it is desired to remove noise and a noise reference signal. The output of the adaptive noise canceler is a good approximation to the desired signal with the noise removed.

Physiological monitors can often advantageously employ signal processors of the present invention. Often in physiological measurements a first signal comprising a first desired portion and a first undesired portion and a second signal comprising a second desired portion and a second undesired portion are acquired. The signals may be acquired by propagating energy through a patient's body (or a material which is derived from the body, such as breath, blood, or tissue, for example) and measuring an attenuated signal after transmission or reflection. Alternatively, the signal may be acquired by measuring energy generated by a patient's body, such as in electrocardiography. The signals are processed via the signal processor of the present invention to acquire a noise reference signal which is input to an adaptive noise canceler.

One physiological monitoring apparatus which can advantageously incorporate the features of the present invention is a monitoring system which determines a signal which is representative of the arterial pulse, called a plethysmographic wave. This signal can be used in blood pressure calculations, blood gas saturation measurements, etc. A specific example of such a use is in pulse oximetry which determines the saturation of oxygen in the blood. In this configuration, the desired portion of the signal is the arterial blood contribution to attenuation of energy as it passes through a portion of the body where blood flows close to the skin. The pumping of the heart causes blood flow to increase and decrease in the arteries in a periodic fashion, causing periodic attenuation wherein the periodic waveform is the plethysmographic waveform representative of the pulse.

A physiological monitor particularly adapted to pulse oximetry oxygen saturation measurement comprises two light emitting diodes (LED's) which emit light at different wavelengths to produce first and second signals. A detector registers the attenuation of the two different energy signals after each passes through an absorptive media, for example a digit such as a finger, or an earlobe. The attenuated signals generally comprise both desired and undesired signal portions. A static filtering system, such as a band pass filter, removes a portion of the undesired signal which is static, or constant, or outside of a known bandwidth of interest, leaving an erratic or random undesired signal portion, often caused by motion and often difficult to remove, along with the desired signal portion.

Next, a processor of the present invention removes the desired signal portions from the measured signals yielding a noise reference signal which is a combination of the remaining undesired signal portions. The noise reference signal is correlated to both of the undesired signal portions. The noise reference signal and at least one of the measured signals are input to an adaptive noise canceler which removes the random or erratic portion of the undesired signal. This yields a good approximation to the desired plethysmographic signal as measured at one of the measured signal wavelengths. As is known in the art, quantitative measurements of the amount of oxygenated blood in the body can be determined from the plethysmographic signal in a variety of ways.

One aspect of the present invention is a signal processor comprising a detector for receiving a first signal which travels along a first propagation path and a second signal which travels along a second propagation path wherein a portion of the first and second propagation paths are located in a propagation medium. The first signal has a first desired signal portion and a first undesired signal portion and the second signal has a second desired signal portion and a second undesired signal portion. The first and second undesired signal portions are a result of a perturbation of the propagation medium. This aspect of the invention additionally comprises a reference processor having an input for receiving the first and second signals. The processor is adapted to combine the first and second signals to generate a reference signal having a primary component which is a function of the first and said second undesired signal portions.

The above described aspect of the present invention may further comprise an adaptive signal processor for receiving the reference signal and the first signal and for deriving therefrom an output signal having a primary component which is a function of the first desired signal portion of the first signal. Alternatively, the above described aspect of the present invention may further comprise an adaptive signal processor for receiving the reference signal and the second signal and for deriving therefrom an output signal having a primary component which is a function of the second desired signal portion of the second signal. The adaptive signal processor may comprise an adaptive noise canceler. The adaptive noise canceler may be comprise a joint process estimator having a least-squares-lattice predictor and a regression filter.

The detector in the aspect of the signal processor of the present invention described above may further comprise a sensor for sensing a physiological function. The sensor may comprise a light sensitive device. Additionally, the present invention may further comprising a pulse oximeter for measuring oxygen saturation in a living organism.

Another aspect of the present invention is a physiological monitoring apparatus comprising a detector for receiving a first physiological measurement signal which travels along a first propagation path and a second physiological measurement signal which travels along a second propagation path. A portion of the first and second propagation paths is located in a propagation medium. The first signal has a first desired signal portion and a first undesired signal portion and the second signal has a second desired signal portion and a second undesired signal portion. The physiological monitoring apparatus further comprises a reference processor having an input for receiving the first and second signals. The processor is adapted to combine the first and second signals to generate a reference signal having a primary component which is a function of the first and the second undesired signal portions.

The physiological monitoring apparatus may further comprise an adaptive signal processor for receiving the reference signal and the first signal for deriving therefrom an output signal having a primary component which is a function of the first desired signal portion of the first signal. Alternatively, the physiological monitoring apparatus may further comprise an adaptive signal processor for receiving the reference signal and the second signal and for deriving therefrom an output signal having a primary component which is a function of the second desired signal portion of the second signal. The physiological monitoring apparatus may further comprise a pulse oximeter.

A further aspect of the present invention is an apparatus for measuring a blood constituent comprising an energy source for directing a plurality of predetermined wavelengths of electromagnetic energy upon a specimen and a detector for receiving the plurality of predetermined wavelengths of electromagnetic energy from the specimen. The detector produces electrical signals corresponding to the predetermined wavelengths in response to the electromagnetic energy. At least two of the electrical signals each has a desired signal portion and an undesired signal portion. Additionally, the apparatus comprises a reference processor having an input for receiving the electrical signals. The processor is configured to combine said electrical signals to generate a reference signal having a primary component which is derived from the undesired signal portions.

This aspect of the present invention may further comprise an adaptive signal processor for receiving the reference signal and one of the two electrical signals and for deriving therefrom an output signal having a primary component which is a function of the desired signal portion of the electrical signal. This may be accomplished by use of an adaptive noise canceler in the adaptive signal processor which may employ a joint process estimator having a least-squares-lattice predictor and a regression filter.

Yet another aspect of the present invention is a blood gas monitor for non-invasively measuring a blood constituent in a body comprising a light source for directing at least two predetermined wavelengths of light upon a body and a detector for receiving the light from the body. The detector, in response to the light from the body, produces at least two electrical signals corresponding to the at least two predetermined wavelengths of light. The at least two electrical signals each has a desired signal portion and an undesired signal portion. The blood oximeter further comprises a reference processor having an input for receiving the at least two electrical signals. The processor is adapted to combine the at least two electrical signals to generate a reference signal with a primary component which is derived from the undesired signal portions. The blood oximeter may further comprise an adaptive signal processor for receiving the reference signal and the two electrical signals and for deriving therefrom at least two output signals which are substantially equal, respectively, to the desired signal portions of the electrical signals.

The present invention also includes a method of determining a noise reference signal from a first signal comprising a first desired signal portion and a first noise portion and a second signal comprising a second desired signal portion and a second noise portion. The method comprises the steps of selecting a signal coefficient which is proportional to a ratio of predetermined attributes of the first desired signal portion and predetermined attributes of the second desired signal portion. The first signal and the second signal coefficient are input into a signal multiplier wherein the first signal is multiplied by the signal coefficient thereby generating a first intermediate signal. The second signal and the first intermediate signal are input into a signal subtractor wherein the first intermediate signal is subtracted from the second signal. This generates a noise reference signal having a primary component which is derived from the first and second noise signal portions. The first and second signals in this method may be derived from light energy transmitted through an absorbing medium.

The present invention further embodies a physiological monitoring apparatus comprising means for acquiring a first signal comprising a first desired signal portion and a first undesired signal portion and a second signal comprising a second desired signal portion and a second undesired signal portion. The physiological monitoring apparatus of the present invention also comprises means for determining from the first and second signals a noise reference signal. Additionally, the monitoring apparatus comprises an adaptive noise canceler having a noise reference input for receiving the noise reference signal and a signal input for receiving the first signal wherein the adaptive noise canceler, in real or near real time, generates an output signal which approximates the first desired signal portion. The adaptive noise canceler may further comprise a joint process estimator.

A further aspect of the present invention is an apparatus for processing an amplitude modulated signal having a signal amplitude complicating feature, the apparatus comprising an energy source for directing electromagnetic energy upon a specimen. Additionally, the apparatus comprises a detector for acquiring a first amplitude modulated signal and a second amplitude modulated signal. Each of the first and second signals has a component containing information about the attenuation of electromagnetic energy by the specimen and a signal amplitude complicating feature. The apparatus includes a reference processor for receiving the first and second amplitude modulated signals and deriving therefrom a noise reference signal which is correlated with the signal amplitude complicating feature. Further, the apparatus incorporates an adaptive noise canceler having a signal input for receiving the first amplitude modulated signal, a noise reference input for receiving the noise reference signal, wherein the adaptive noise canceler produces an output signal having a primary component which is derived from the component containing information about the attenuation of electromagnetic energy by the specimen.

Still another aspect of the present invention is an apparatus for extracting a plethysmographic waveform from an amplitude modulated signal having a signal amplitude complicating feature, the apparatus comprising a light source for transmitting light into an organism and a detector for monitoring light from the organism. The detector produces a first light attenuation signal and a second light attenuation signal, wherein each of the first and second light attenuation signals has a component which is representative of a plethysmographic waveform and a component which is representative of the signal amplitude complicating feature. The apparatus also includes a reference processor for receiving the first and second light attenuation signals and deriving therefrom a noise reference signal. The noise reference signal and the signal amplitude complicating feature each has a frequency spectrum. The frequency spectrum of the noise reference signal is correlated with the frequency spectrum of the signal amplitude complicating feature. Additionally incorporated into this embodiment of the present invention is an adaptive noise canceler having a signal input for receiving the first attenuation signal and a noise reference input for receiving the noise reference signal. The adaptive noise canceler produces an output signal having a primary component which is derived from the component which is representative of a plethysmographic waveform.

The present invention also comprises a method of removing a motion artifact signal from a signal derived from a physiological measurement wherein a first signal having a physiological measurement component and a motion artifact component and a second signal having a physiological measurement component and a motion artifact component are acquired. From the first and second signals a motion artifact noise reference signal which is a primary function of the first and second signals motion artifact components is derived. This method of removing a motion artifact signal from a signal derived from a physiological measurement may also comprise the step of inputting the motion artifact noise reference signal into an adaptive noise canceler to produce an output signal which is a primary function of the first signal physiological measurement component.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an ideal plethysmographic waveform.

FIG. 2 schematically illustrates the cross-sectional structure of a typical finger.

FIG. 3 illustrates a plethysmographic waveform which includes a motion-induced undesired erratic signal portion.

FIG. 4 illustrates a schematic diagram of a physiological monitor incorporating a processor of the present invention and an adaptive noise canceler.

FIG. 4a illustrates the transfer function of a multiple notch filter.

FIG. 5 illustrates an example of an adaptive noise canceler which could be employed in a physiological monitor which also incorporates the processor of the present invention.

FIG. 6a illustrates a schematic absorbing material comprising N constituents within an absorbing material.

FIG. 6b illustrates another schematic absorbing material comprising N constituents within an absorbing material.

FIG. 7 is a schematic model of a joint process estimator comprising a least-squares lattice predictor and a regression filter.

FIG. 8 is a flowchart representing a subroutine capable of implementing a joint process estimator as modeled in FIG. 7.

FIG. 9 is a schematic model of a joint process estimator with a least-squares lattice predictor and two regression filters.

FIG. 10 is an example of a physiological monitor incorporating a processor of the present invention and an adaptive noise canceler within a microprocessor. This physiological monitor is specifically designed to measure a plethysmographic waveform and perform pulse oximetry measurements.

FIG. 11 is a graph of oxygenated and deoxygenated absorption coefficients vs. wavelength.

FIG. 12 is a graph of the ratio of the absorption coefficients of deoxygenated hemoglobin divided by oxygenated hemoglobin vs. wavelength.

FIG. 13 is an expanded view of a portion of FIG. 11 marked by a circle labelled 13.

FIG. 14 illustrates a signal measured at a first red wavelength λa=λred1=650 nm for use in a processor of the present invention employing the ratiometric method for determining the noise reference signal n′(t) and for use in a joint processor estimator. The measured signal comprises a desired portion Yλa(t) and an undesired portion nλa(t).

FIG. 15 illustrates a signal measured at a second red wavelength λb=λred2=685 nm for use in a processor of the present invention employing the ratiometric method for determining the noise reference signal n′(t). The measured signal comprises a desired portion Yλb(t) and an undesired portion nλb(t).

FIG. 16 illustrates a signal measured at an infrared wavelength λc=λIR=940 nm for use in a joint process estimator. The measured signal comprises a desired portion Yλc(t) and an undesired portion nλc(t).

FIG. 17 illustrates the noise reference signal n′(t) determined by a processor of the present invention using the ratiometric method.

FIG. 18 illustrates a good approximation Y′λa(t) to the desired portion Yλa(t) of the signal Sλa(t) measured at λa=λred1=650 nm estimated with a noise reference signal n′(t) determined by the ratiometric method.

FIG. 19 illustrates a good approximation Y′λc(t) to the desired portion Yλc(t) of the signal Sλc(t) measured at λc=λIR=940 nm estimated with a noise reference signal n′(t) determined by the ratiometric method.

FIG. 20 illustrates a signal measured at a red wavelength λa=λred=660 nm for use in a processor of the present invention employing the constant saturation method for determining the noise reference signal n′(t) and for use in a joint process estimator. The measured signal comprises a desired portion Yλa(t) and an undesired portion nλa(t).

FIG. 21 illustrates a signal measured at an infrared wavelength λb=λIR=940 nm for use in a processor of the present invention employing the constant saturation method for determining the noise reference signal n′(t) and for use in a joint process estimator. The measured signal comprises a desired portion Yλb(t) and an undesired portion nλb(t).

FIG. 22 illustrates the noise reference signal n′(t) determined by a processor of the present invention using the constant saturation method.

FIG. 23 illustrates a good approximation Y′λa(t) to the desired portion Yλa(t) of the signal Sλa(t) measured at λa=λred=660 nm estimated with a noise reference signal n′(t) determined by the constant saturation method.

FIG. 24 illustrates a good approximation Y′λb(t) to the desired portion Yλb(t) of the signal Sλb(t) measured at λb=λIR=940 nm estimated with a noise reference signal n′(t) determined by the constant saturation method.

DETAILED DESCRIPTION OF THE INVENTION

The present invention is a processor which determines a noise reference signal n′(t) for use in an adaptive noise canceler. An adaptive noise canceler estimates a good approximation Y′(t) to a desired signal Y(t) from a composite signal S(t)=Y(t)+n(t) which, in addition to the desired portion Y(t) comprises an undesired portion n(t). The undesired portion n(t) may contain one or more of a constant portion, a predictable portion, an erratic portion, a random portion, etc. The approximation to the desired signal Y′(t) is derived by removing as many of the undesired portions n(t) from the composite signal S(t) as possible. The constant portion and predictable portion are easily removed with traditional filtering techniques, such as simple subtraction, low pass, band pass, and high pass filtering. The erratic portion is more difficult to remove due to its unpredictable nature. If something is known about the erratic signal, even statistically, it could be removed from the measured signal via traditional filtering techniques. However, it is often the case that no information is known about the erratic portion of the noise. In this case, traditional filtering techniques are usually insufficient. Often no information about the erratic portion of the measured signal is known. Thus, an adaptive noise canceler is utilized in the present invention to remove the erratic portion.

Generally, an adaptive noise canceler has two signal inputs and one output. One of the inputs is the noise reference signal n′(t) which is correlated to the erratic undesired signal positions n(t) present in the composite signal S(t). The other input is for the composite signal S(t). Ideally, the output of the adaptive noise canceler Y′(t) corresponds to the desired signal portion Y(t) only. Often, the most difficult task in the application of adaptive noise cancelers is determining the noise reference signal n′(t) which is correlated to the erratic undesired portion n(t) of the measured signal S(t) since, as discussed above, unpredictable signal portions are usually quite difficult to isolate from the measured signal S(t). In the signal processor of the present invention, a noise reference signal n′(t) is determined from two composite signals measured simultaneously, or nearly simultaneously, at two different wavelengths, λa and λb. The signal processor of the present invention can be advantageously used in a monitoring device, such a monitor being well suited for physiological monitoring.

A block diagram of a generic monitor incorporating a signal processor, or reference processor, according to the present invention and an adaptive noise canceler is shown in FIG. 4. Two measured signals, Sλa(t) and Sλb(t), are acquired by a detector 20. One skilled in the art will realize that for some physiological measurements, more than one detector may be advantageous. Each signal is conditioned by a signal conditioner 22a and 22b. Conditioning includes, but is not limited to, such procedures as filtering the signals to remove constant portions and amplifying the signals for each of manipulation. The signals are then converted to digital data by an analog-to-digital converter 24a and 24b. The first measured signal Sλa(t) comprises a first desired signal portion, labelled herein Yαa(t), and a first undesired signal portion, labelled herein nλa(t). The second measured signal Sλb(t) is at least partially correlated to the first measured signal Sλa(t) and comprises a second desired signal portion, labelled herein Yλb(t), and a second undesired signal portion, labelled herein nλb(t). Typically the first and second undesired signal portions, nλa(t) and nλb(t), are uncorrelated and/or erratic with respect to the desired signal portions Yλa(t) and Yλb(t). The undesired signal portions nλa(t) and nλb(t) are often caused by motion of a patient. The signals Sλa(t) and Sλb(t) are input to a reference processor 26. The reference processor 26 multiplies the second measured signal Sλb(t) by a factor ω and then subtracts the second measured signals Sλb(t) from the first measured signal Sλa(t). The factor ω is determined to cause the desired signal portions Yλa(t) and Yλb(t) to cancel when the two signals Sλa(t) and Sλb(t) are subtracted. Thus, the output of the reference processor 26 is a noise reference signal n′(t)=nλa(t)−ωnλb(t) which is correlated to both of the erratic undesired signal portions nλa(t) and nλb(t). The noise reference signal n′(t) is input, along with one of the measured signals Sλa(t), to an adaptive noise canceler 27 which uses the noise reference signal n′(t) to remove the undesired signal portion nλa(t) or nλb(t) from the measured signal Sλa(t). It will be understood that Sλb(t) could have been input to the adaptive noise canceler 27 along with the noise reference signal n′(t) instead of Sλa(t). The output of the adaptive noise canceler 27 is a good approximation Y′λa(t) to the desired signal Yλa(t). The approximation Y′λa(t) is displayed on the display 28.

An adaptive noise canceler 30, an example of which is shown in block diagram in FIG. 5, is employed to remove the erratic, undesired signal portions nλa(t) and nλb(t) from the measured signals Sλa(t) and Sλb(t). The adaptive noise canceler 30 in FIG. 5 has as one input a sample of the noise reference signal n′(t) which is correlated to the undesired signal portions nλa(t) and nλb(t). The noise reference signal n′(t) is determined from the two measured signals Sλa(t) and Sλb(t) by the processor 26 of the present invention as described herein. A second input to the adaptive noise canceler is a sample of either the first or second measured signal Sλa(t)=Yλa(t)+nλa(t) or Sλb(t)=Yλb(t)+nλb(t).

The adaptive noise canceler 30 functions to remove frequencies common to both the noise reference signal n′(t) and the measured signal Sλa(t) or Sλb(t). Since the noise reference signal n′(t) is correlated to the erratic undesired signal portions nλa(t) and nλb(t), the noise reference signal n′(t) is also erratic. The adaptive noise canceler acts in a manner which may be analogized to a dynamic multiple notch filter based on the spectral distribution of the noise reference signal n′(t).

Referring to FIG. 4a, the transfer function of a multiple notch filter is shown. The notches, or dips in the amplitude of the transfer function, indicate frequencies which are attenuated or removed when a composite measured signal passes through the notch filter. The output of the notch filter is the composite signal having frequencies at which a notch was present removed. In the analogy to an adaptive noise canceler, the frequencies at which notches are present change continuously based upon the inputs to the adaptive noise canceler.

The adaptive noise canceler 30 shown in FIG. 5 produces an output signal, labelled herein Y′λa(t) or Y′λb(t), which is fed back to an internal processor 32 within the adaptive noise canceler 30. The internal processor 32 automatically adjusts its own transfer function according to a predetermined algorithm such that the output of the internal processor 32, labelled b(t), closely resembles the undesired signal portion nλa(t) or nλb(t). The output b(t) of the internal processor 32 is subtracted from the measured signal, Sλa(t) or Sλb(t), yielding a signal Y′λa(t)≈Sλa(t)+nλb(t)−bλb(t) or Y′λb(t)≈Sλb(t)+nλb(t)−bλb(t). The internal processor optimizes Y′λaor Y′λb(t) such that Y′λa(t) or Y′λb(t) is approximately equal to the desired signal Yλa(t) or Yλb(t), respectively.

One algorithm which may be used for the adjustment of the transfer function of the internal processor 32 is a least-squares algorithm, as described in Chapter 6 and Chapter 12 of the book Adaptive Signal Processing by Bernard Widrow and Samuel Stearns, published by Prentice Hall, copyright 1985. This entire book, including Chapters 6 and 12, is hereby incorporated herein by reference.

Adaptive processors 30 have been successfully applied to a number of problems including antenna sidelobe canceling, pattern recognition, the elimination of periodic interference in general, and the elimination of echoes on long distance telephone transmission lines. However, considerable ingenuity is often required to find a suitable noise reference signal n′(t) for a given application since the random or erratic portions nλa(t) or nλb(t) cannot easily be separated from the measured signal Sλa(t) or Sλb(t). If the actual undesired signal portion nλa(t) or nλb(t) were a priori available, techniques such as adaptive noise canceling would not be necessary. The unique determination of a suitable noise reference signal n′(t) from measurements taken by a monitor incorporating a reference processor of the present invention is one aspect of the present invention.

Generalized Determination of Noise Reference Signal

An explanation which describes how the noise reference signal n′(t) may be determined as follows. A first signal is measured at, for example, a wavelength λa, by a detector yielding a signal Sλa(t).

Sλa(t)=Yλa(t)+nλa(t);   (1)

Yλa(t) is the desired signal and nλa(t) is the noise component.

A similar measurement is taken simultaneously, or nearly simultaneously, at a different wavelength, λb, yielding:

Sλb(t)=Yλb(t)+nλb.   (2)

Note that as long as the measurements, Sλa(t) and Sλb(t), are taken substantially simultaneously, the undesired signal components, nλa(t) and nλb(t), will be correlated because any random or erratic functions will affect each measurement in nearly the same fashion.

To obtain the noise reference signal n′(t), the measured signals Sλa(t) and Sλb(t) are transformed to eliminate the desired signal components. One way of doing this is to find a proportionality constant, ω1, between the desired signals Yλa(t) and Yλb(t) such that:

Yλa(t)=ω1Yλb(t).   (3)

This proportionality relationship can be satisfied in many measurements, including but not limited to absorption measurements and physiological measurements. Additionally, in most measurements, the proportionality constant ω1 can be determined such that:

nλa(t)≠ω1nλb(t).   (4)

Multiplying equation (2) by ω1 and then subtracting equation (2) from equation (1) results in a single equation wherein the desired signal terms Yλa(t) and Sλb(t) cancel, leaving:

n′(t)=Sλa(t)−ω1Sλb(t)=nλa(t)−ω1nλb(t);   (5)

a non-zero signal which is correlated to each undesired signal portion nλa(t) and nλb(t) and can be used as the noise reference signal n′(t) in an adaptive noise canceler.

Example of Determination of Noise Reference Signal in an Absorptive System

Adaptive noise canceling is particularly useful in a large number of measurements generally described as absorption measurements. An example of an absorption type monitor which can advantageously employ adaptive noise canceling based upon a noise reference signal n′(t) determined by a processor of the present invention is one which determines the concentration of an energy absorbing constituent within an absorbing material when the material is subject to perturbation. Such perturbations can be caused by forces about which information is desired, or alternatively, by random or erratic forces such as a mechanical force on the material. Random or erratic interference, such as motion, generates undesired noise components in the measured signal. These undesired components can be removed by the adaptive noise canceler if a suitable noise reference signal n′(t) is known.

A schematic N constituent absorbing material comprising a container 42 having N different absorbing constituents, labelled A1, A2, A3, . . . AN, is shown schematically in FIG. 6a. The constituents A3 through AN in FIG. 6a are arranged in a generally orderly, layered fashion within the container 42. An example of a particular type of absorptive system is one in which light energy passes through the container 42 and is absorbed according to the generalized Beer-Lambert Law of light absorption. For light of wavelength λa, this attenuation may be approximated by:

I=Ioe−Σ N i=0εi,λa c pei   (6)

Initially transforming the signal by taking the natural log of both sides and manipulating terms, the signal is transformed such that the signal components are combined by addition rather than multiplication, i.e.:

Sλa=ln(Io/I)=ΣN i=0εi,λacixi   (7)

where Io is the incident light energy intensity; I is the transmitted light energy intensity: εi,λa is the absorption coefficient of the ith constituent at the wavelength λa; xi(t) is the optical path length of ith layer, i.e., the thickness of material of the ith layer through which optical energy passes; and ci(t) is the concentration of the ith constituent in the volume associated with the thickness xi(t). The absorption coefficients ε1 through εN are known values which are constant at each wavelength. Most concentrations c1(t) through cN(t) are typically unknown, as are most of the optical path lengths xi(t) of each layer. The total optical path length is the sum of each of the individual optical path lengths xi(t) of each layer.

When the material is not subject to any forces which cause perturbation in the thicknesses of the layers, the optical path length of each layer, xi(t), is generally constant. This results in generally constant attenuation of the optical energy and thus, a generally constant offset in the measured signal. Typically, this portion of the signal is of little interest since knowledge about a force which perturbs the material is usually desired. Any signal portion outside of a known bandwidth of interest, including the constant undesired signal portion resulting from the generally constant absorption of the constituents when not subject to perturbation, should be removed. This is easily accomplished by traditional band pass filtering techniques. However, when the material is subject to forces, each layer of constituents may be affected by the perturbation differently than each other layer xi(t) may result in excursions in the measured signal which represent desired information. Other perturbations of the optical path length of each layer xi(t) cause undesired excursions which mask desired information in the measured signal. Undesired signal components associated with undesired excursions must also be removed to obtain desired information from the measured signal.

The adaptive noise canceler removes from the composite signal, measured after being transmitted through or reflected from the absorbing material, the undesired signal components cause by forces which perturb the material differently from the forces which perturbed the material to cause the desired signal component. For the purposes of illustration, it will be assumed that the portion of the measured signal which is deemed the desired signal Yλa(t) is the attenuation term ε5c5x5(t) associated with a constituent of interest, namely A5, and that the layer of constituent A5 is affected by perturbations differently than each of the layers of other constituents A1 through A4 and A6 through AN. An example of such a situation is when layer A5 is subject to forces about which information is desired and, additionally, the entire material is subject to forces which affect each of the layers. In this case, since the total force affecting the layer of constituents A5 is different than the total forces affecting each of the other layers and information is desired about the forces and resultant perturbation of the layer of constituents A5, attenuation terms due to constituents A1 through A4 and A6 through AN make up the undesired signal nλa(t). Even if the additional forces which affect the entire material cause the same perturbation in each layer, including the layer of A5, the total forces on the layer of constituent A5 cause it to have different total perturbation than each of the other layers of constituents A1 through A4 and A6 through AN.

It is often the case that the total perturbation affecting the layers associated with the undesired signal components is caused by random or erratic forces. This causes the thickness of layers to change erratically and the optical path length of each layer, xi(t), to change erratically, thereby producing a random or erratic undesired signal component nλa(t). However, regardless of whether or not the undesired signal portion nλa(t) is erratic, the undesired signal component nλa(t) can be removed via an adaptive noise canceler having as one input a noise reference signal n′(t) determined by a processor of the present invention as long as the perturbation on layers other than the layer of constituents A5 is different than the perturbation on the layer of constituent A5. The adaptive noise canceler yields a good approximation to the desire signal Y′λa(t). From this approximation, the concentration of the constituent of the interest, c5(t) can often be determined since in some physiological measurements, the thickness of the desired signal component, x5(t) in this example, is known or can be determined.

The adaptive noise canceler utilizes a sample of a noise reference signal n′(t) determined from two substantially simultaneously measured signals Sλa(t) and Sλb(t). Sλa(t) is determined as above in equation (7). Sλb(t) is determined similarly at a different wavelength λb. To find the noise reference signal n′(t), attenuated transmitted energy is measured at the two different wavelengths λa and λb and transformed via logarithmic conversion. The signals Sλa(t) and Sλb(t) can then be written (logarithm converted) as: S λ a ( t ) = ε 5 λ a c 5 x 5 ( t ) + [ i = 1 4 ε i λ a c i x i ( t ) + k = 6 N ε k λ a c k x k ( t ) ] ( 8 ) = ε 5 λ a c 5 x 5 ( t ) + n λ a ( t ) ( 9 ) S λ b ( t ) = ε 5 λ b c 5 x 5 ( t ) + [ i = 1 4 ε i λ b c i x i ( t ) + k = 6 N ε k λ b c k x k ( t ) ] ( 10 ) = ε 5 λ b c 5 x 5 ( t ) + n λb ( t ) ( 11 )

Figure USRE038492-20040406-M00001

A further transformation of the signals is the proportionality relationship defining ω2, similarly to equation (3), which allows determination of a noise reference signal n′(t), is:

ε5,λa2ε5,λb;   (12)

where

nλa≠ω2nλb.   (13)

It is often the case that the both equations (12) and (13) can be simultaneously satisfied. Multiplying equation (11) by ω2 and subtracting the result from equation (9) yields a non-zero noise reference signal which is a linear sum of undesired signal components. n ( t ) = S λ a ( t ) - ω 2 S λ b ( t ) = n λ a ( t ) - ω 2 n λ b ( t ) . ( 14 ) = i = 1 4 ε i λ a c i x i ( t ) + k = 6 N ε k λ a c k x k ( t ) - i = 1 4 ω 2 ε i λ b c i x i ( t ) - k = 6 N ω 2 ε k λ b c k x k ( t ) ( 15 ) = i = 1 4 c i x i ( t ) [ ε i λ a - ω 2 ε i λ b ] + k = 6 N c k x k ( t ) [ ε k λ a - ω 2 ε k λ b ] ( 16 )

Figure USRE038492-20040406-M00002

A sample of this noise reference signal n′(t), and a sample of either measured signal Sλa(t) or Sλb(t), are input to an adaptive noise canceler, one model of which is shown in FIG. 5 and a preferred model of which is discussed herein under the heading PREFERRED ADAPTIVE NOISE CANCELER USING A JOINT PROCESS ESTIMATOR IMPLEMENTATION. The adaptive noise canceler removes the undesired portion of the measured signal nλa(t) or nλb(t), yielding a good approximation to the desired portion of signal Y′λa(t)≈ε5,λac5x5(t). The concentration c5(t) may then be determined from the approximation to the desired signal Y′λa(t) (t) or Y′λb(t) according to:

c5(t)≈Y′λa(t)/ε5,λax5(t)≈Y′λb(t)/ε5,λbx5(t).   (17)

As discussed previously, the absorption coefficients are constant at each wavelength λa and λb and the thickness of the desired signal component, x5(t) in this example, is often known or can be determined as a function of time, thereby allowing calculation of the concentration c5(t) of constituent A5.

Determination of Concentration or Saturation in a Volume Containing More Than One Constituent

Referring to FIG. 6b, another material having N different constituents arranged in layers is shown. In this material, two constituents A5 and A6 are found within one layer having thickness x5,6(t)=x5(t)+x6(t), located generally randomly within the layer. This is analogous to combining the layers of constituents A5 and A6 in FIG. 6a. A combination of layers, such as the combination of layers of constituents A5 and A6, is feasible when the two layers are under the same total forces which result in the same perturbation of the optical path lengths x5(t) and x6(t) of the layers.

Often it is desirable to find the concentration or the saturation, i.e., a percent concentration, of one constituent within a given thickness which contains more than one constituent and is subject to unique forces. A determination of the concentration or the saturation of a constituent within a given volume may be made with any number of constituents in the volume subject to the same total forces and therefore under the same perturbation. To determine the saturation of one constituent in a volume comprising many constituents, as many measured signals as there are constituents which absorb incident light energy are necessary. It will be understood that constituents which do not absorb light energy are not consequential in the determination of saturation. To determine the concentration, as many signals as there are constituents which absorb incident light energy are necessary as well as information about the sum of concentrations.

It is often the case that a thickness under unique motion contains only two constituents. For example, it may be desirable to know the concentration or saturation of A5 within a given volume which contains A5 and A6. In this case, the desired signals Yλa(t) and Yλb(t) comprise terms related to both A5 and A6 so that a determination of the concentration or saturation of A5 or A6 in the volume may be made. A determination of saturation is discussed herein. It will be understood that the concentration of A5 in volume containing both A5 and A6 could also be determined if it is known that A5+A6=1, i.e., that there are no constituents in the volume which do not absorb incident light energy at the particular measurement wavelengths chosen. The measured signals Sλa(t) and Sλb(t) can be written (logarithm converted) as:

Sλa(t)=ε5,λac5x5,6(t)+ε6,λac6x5,6(t)+nλa(t)   (18)

 =Yλa(t)+nλa(t);   (19)

Sλb(t)=ε5,λbc5x5,6(t)+ε6,λbc6x5,6(t)+nλb(t)   (20)

 =Yλb(t)+nλb(t).   (21)

Any signal portions outside of a known bandwidth of interest, including the constant undesired signal portion resulting from the generally constant absorption of the constituents when not under perturbation, should be removed to determine an approximation to the desired signal. This is easily accomplished by traditional band pass filtering techniques. As in the previous example, it is often the case that the total perturbation affecting the layers associated with the undesired signal components is caused by random or erratic forces, causing the thickness of each layer, or the optical path length of each layer, xi(t), to change erratically, producing a random or erratic undesired signal component nλa(t). Regardless of whether or not the undesired signal portion nλa(t) is erratic, the undesired signal component nλa(t) can be removed via an adaptive noise canceler having as one input a noise reference signal n′(t) determined by a processor of the present invention as long as the perturbation in layers other than the layer of constituents A5 and A6 is different than the perturbation in the layer of constituents A5 and A6. The erratic undesired signal components nλa(t) and nλb(t) may advantageously be removed from equations (18) and (20), or alternatively equations (19) and (21), by an adaptive noise canceler. The adaptive noise canceler, again, requires a sample of a noise reference signal n′(t).

Determination of Noise Reference Signal for Saturation Measurement

Two methods which may be used by a processor of the present invention to determine the noise reference signal n′(t) are a ratiometric method and a constant saturation method. The preferred embodiment of a physiological monitor incorporating a processor of the present invention utilizes the ratiometric method wherein the two wavelengths λa and λb, at which the signals Sλa(t) and Sλb(t) are measured, are specifically chosen such that a relationship between the absorption coefficients ε5,λa, ε5,λb, ε6,λa and ε6,λb exists, i.e.:

ε5,λa6,λa5,λb6,λb   (22)

The measured signals Sλa(t) and Sλb(t) can be factored and written as:

Sλa(t)=ε6,λa[(ε5,λa6,λa)c5x(t)+c6x(t)]+nλa(t)   (23)

Sλb(t)=ε6,λb[(ε5,λb6,λb)c5x(t)+c6x(t)]+nλb(t).   (24)

The wavelengths λa and λb, chosen to satisfy equation (22), cause the terms within the square brackets to be equal, thereby causing the desired signal portions Y′λa(t) and Y′λb(t) to be linearly dependent. Then, a proportionality constant ωr3 which causes the desired signal portions Y′λa(t) and Y′λb(t) to be equal and allows determination of a non-zero noise reference signal n′(t) is:

ε6,λar3ε6,λb;   (25)

where

nλa≠ωr3nλb.   (b 26)

It is often the case that both equations (25) and (26) can be simultaneously satisfied. Additionally, since absorption coefficients of each constituent are constant with respect to wavelength, the proportionality constant ωr3 can be easily determined. Furthermore, absorption coefficients of other constituents A1 through A4 and A7 through AN are generally unequal to the absorption coefficients of A5 and A6. Thus, the undesired noise components nλa and nλb are generally not made linearly dependent by the relationships of equations (22) and (25).

Multiplying equation (24) by ωr3 and subtracting the resulting equation from equation (23), a non-zero noise reference signal is determined by:

 n′(t)=Sλa(t)−ωr3Sλb(t)=nλa(t)−ωr3nλb(t).   (27)

An alternative method for determining the noise reference signal from the measured signals Sλa(t) and Sλb(t) using a processor of the present invention is the constant saturation approach. In this approach, it is assumed that the saturation of A5 in the volume containing A5 and A6 remains relatively constant, i.e.: Saturation ( A 5 ( t ) ) = c 5 ( t ) / [ c 5 ( t ) + c 6 ( t ) ] ( 28 ) = { 1 + [ c 6 ( t ) / c 5 ( t ) ] } - 1 ( 29 )

Figure USRE038492-20040406-M00003

is substantially constant over many samples of the measured signals Sλa and Sλb. This assumption is accurate over many samples since saturation generally changes relatively slowly in physiological systems.

The constant saturation assumption is equivalent to assuming that:

c5(t)/c6(t)=constant   (30)

since the only other term in equation (29) is a constant, namely the numeral 1.

Using this assumption, the proportionality constant ωs3(t) which allows determination of the noise reference signal n′(t) is: ω s3 ( t ) = ε 5 λ a c 5 x 5 , 6 ( t ) + ε 6 λ a c 6 x 5 , 6 ( t ) ε 5 λ b c 5 x 5 , 6 ( t ) + ε 6 λ b c 6 x 5 , 6 ( t ) ( 31 ) = Y λ a ( t ) / Y λ b ( t ) ( 32 ) = ε 5 λ a c 5 + ε 6 λ a c 6 ε 5 λ b c 5 + ε 6 λ b c 6 ( 33 ) = ε 5 λ a ( c 5 ( t ) c 6 ( t ) ) + ε 6 λ a ε 5 λ b ( c 5 ( t ) c 6 ( t ) ) + ε 6 λ b ( 34 ) Y λ a ( t ) / Y λ b ( t ) = constant where ( 35 ) n λ a ( t ) ω s3 ( t ) n λ b ( t ) . ( 36 )

Figure USRE038492-20040406-M00004

It is often the case that both equations (35) and (36) can be simultaneously satisfied to determine the proportionality constant ωs3(t). Additionally, the absorption coefficients at each wavelength ε5,λa, ε6,λa, ε5,λb, and ε6λb are constant and the central assumption of the constant saturation method is that c5(t)/c6(t) is constant over many sample periods. Thus, a new proportionality constant ωa3(t) may be determined every few samples from new approximations to the desired signal as output from the adaptive noise canceler. Thus, the approximations to the desired signals Y′λa(t) and Y′λb(t), found by the adaptive noise canceler for substantially immediately preceding set of samples of the measured signals Sλa(t) and Sλb(t) are used in a processor of the present invention for calculating the proportionality constant, ωs3(t), for the next set of samples of the measured signals Sλa(t) and Sλb(t).

Multiplying equation (20) by ωs3(t) and subtracting the resulting equation from equation (18) yields a non-zero noise reference signal:

n′(t)=Sλa(t)−ωs3(t)Sλb(t)=nλa(t)−ωs3(t)nλb(t).   (37)

It will be understood that equation (21) could be multiplied by ωs3(t) and the resulting equation could be subtracted from equation (19) to yield the same noise reference signal n′(t) as given in equation (37).

When using the constant saturation method, it is necessary for the patient to remain motionless for a short period of time such that an accurate initial saturation value can be determined by known methods other than adaptive noise canceling on which all other calculations will be based. With no erratic, motion-induced undesired signal portions, a physiological monitor can very quickly produce an initial value of the saturation of A5 in the volume containing A5 and A6. An example of a saturation calculation is given in the article “SPECTROPHOTOMETRIC DETERMINATION OF OXYGEN SATURATION OF BLOOD INDEPENDENT OF THE PRESENT OF INDOCYANINE GREEN” by G. A. Mook, et al., wherein determination of oxygen saturation in arterial blood is discussed. Another article discussing the calculation of oxygen saturation is “PULSE OXIMETRY: PHYSICAL PRINCIPLES, TECHNICAL REALIZATION AND PRESENT LIMITATIONS” by Michael R. Neuman. Then, with values for Y′λa(t) and Y′λb(t) determined, an adaptive noise canceler may be utilized with a noise reference signal n′(t) determined by the constant saturation method.

Preferred Adaptive Noise Canceler Using a Joint Process Estimator Implementation

Once the noise reference signal n′(t) is determined by the processor of the present invention using either the above described ratiometric or constant saturation methods, the adaptive noise canceler can be implemented in either hardware or software.

The least mean squares (LMS) implementation of the internal processor 32 described above in conjunction with the adaptive noise canceler of FIG. 5 is relatively easy to implement, but lacks the speed of adaptation desirable for most physiological monitoring applications of the present invention. Thus, a faster approach for adaptive noise canceling, called a least-squares lattice joint process estimator model, is preferably used. A joint process estimator 60 is shown diagrammatically in FIG. 7 and is described in detail in Chapter 9 of Adaptive Filter Theory by Simon Haykin, published by Prentice-Hall, copyright 1986. This entire book, including Chapter 9, is hereby incorporated herein by reference. The function of the joint process estimator is to remove the undesired signal portions nλa(t) or nλb(t) from the measured signals Sλa(t) or Sλb(t), yielding a signal Y′λa(t) or Y′λb(t) which is a good approximation to the desired signal Yλa(t) or Yλb(t). Thus, the joint process estimator estimates the value of the desired signal Yλa(t) or Yλb(t). The inputs to the joint process estimator 60 are the noise reference signal n′(t) and the composite measured signal Sλa(t) or Sλb(t). The output is a good approximation to the signal Sλa(t) or Sλb(t) with the noise removed, i.e. a good approximation to Yλa(t) or Yλb(t).

The joint process estimator 60 utilizes, in conjunction, a least square lattice predictor 70 and a regression filter 80. The noise reference signal n′(t) is input to the least square lattice predictor 70 while the measured signal Sλa(t) or Sλb(t) is input to the regression filter 80. For simplicity in the following description, Sλa(t) will be the measured signal from which the desired portion Yλa(t) will be estimated by the joint process estimator 60. However, it will be noted that Sλb(t) could equally well be input to the regression filter 80 and the desired portion Yλb(t) of this signal could equally well be estimated.

The joint process estimator 60 removes all frequencies that are present in both the noise reference signal n′(t) and the measured signal Sλa(t). The undesired signal portion nλa(t) usually comprises frequencies unrelated to those of the desired signal portion Yλa(t). It is highly improbable that the undesired signal portion nλa(t) would be of exactly the same spectral content as the desired signal portion Yλa(t). However, in the unlikely event that the spectral content of Sλa(t) and n′(t) are similar, this approach will not yield accurate results. Functionally, the joint process estimator 60 compares input signal n′(t), which is correlated to the undesired signal portion nλa(t), and input signal Sλa(t) and removes all frequencies which are identical. Thus, the joint process estimator 60 acts as a dynamic multiple notch filter to remove those frequencies in the undesired signal component nλa(t) as they change erratically with the motion of the patient. This yields a signal having substantially the same spectral content as the desired signal Yλa(t). The output of the joint process estimator 60 has substantially the same spectral content and amplitude as the desired signal Yλa(t). Thus, the output Y′λa(t) of the joint process estimator 60 is a very good approximation to the desired signal Yλa(t).

The joint process estimator 60 can be divided into stages, beginning with a zero-stage and terminating in an mth-stage, as shown in FIG. 7. Each stage, except for the zero-stage, is identical to every other stage. The zero-stage is an input stage for the joint process estimator 60. The first stage through the mth-stage work on the signal produced in the immediately previous stage, i.e., the (m−1)th-stage, such that a good approximation to the desired signal Y′λa(t) is produced as output from the mth-stage.

The least-squares lattice predictor 70 comprises registers 90 and 92, summing elements 100 and 102, and delay elements 110. The registers 90 and 92 contain multiplicative values of a forward reflection coefficient Γb,m(t) and a backward reflection coefficient Γb,m(t) which multiply the noise reference signal n′(t) and signals derived from the noise reference signal n′(t). Each stage of the least-squares lattice predictor outputs a forward prediction error fm(t) and a backward prediction error bm(t). The subscript m is indicative of the stage.

For each set of samples, i.e. one sample of the noise reference signal n′(t) derived substantially simultaneously with one sample of the measured signal Sλa(t), the sample of the nose reference signal n′(t) is input to the lead-squares lattice predictor 70. The zero-stage forward predictor error f0(t) and the zero-stage backward prediction error b0(t) are set equal to the noise reference signal n′(t). The backward prediction error b0(t) is delayed by one sample period by the delay element 110 in the first stage of the least-squares lattice predictor 70. Thus, the immediately previous value of the noise reference signal n′(t) is used in calculations involving the first-stage delay element 110. The zero-stage forward prediction error is added to the negative of the delayed zero-stage backward prediction error b0(t−1) multiplied by the forward reflection coefficient value Γf,1(t) register 90 value, to produce a first-stage forward prediction error f1(t). Additionally, the zero-stage forward prediction error f0(t) is multiplied by the backward reflection coefficient value Γb,1 (t) register 92 value and added to the delayed zero-stage backward prediction error b0(t−1) to produce a first-stage backward prediction error b1(t). In each subsequent stage, m, of the least square lattice predictor 70, the previous forward and backward prediction error values, fm-1(t) and bm-1(t−1), the backward prediction error being delayed by one sample period, are used to produce values of the forward and backward prediction errors for the present stage, fm(t) and bm(t).

The backward prediction error bm(t) is fed to the concurrent stage, m, of the regression filter 80. There it is input to a register 96, which contains a multiplicative regression coefficient value κm,λa(t). For example, in the zero-stage of the regression filter 80, the zero-stage backward prediction error b0(t) is multiplied by the zero-stage regression coefficient κ0,λa(t) register 96 value and subtracted from the measured value of the signal Sλa(t) at a summing element 106 to produce a first stage estimation error signal e1,λa(t). The first-stage estimation error signal e1,λa(t) is a first approximation to the desired signal. This first-stage estimation error signal e1,λa(t) is input to the first-stage of the regression filter 80. The first-stage backward prediction error b1(t), multiplied by the first-stage regression coefficient κ1,λa(t) register 96 value is subtracted from the first-stage estimation error signal e1,λa(t) to produce the second-stage estimation error e2,λa(t). The second-stage estimation error signal e2,λa(t) is a second, somewhat better approximation to the desired signal Yλa(t).

The same processes are repeated in the least-squares lattice predictor 70 and the regression filter 80 for each stage until a good approximation to the desired signal Y′λa(t)=em,λa(t) is determined. Each of the signals discussed above, including the forward prediction error fm(t), the backward prediction error bm(t), the estimation error signal em,λa(t), is necessary to calculate the forward reflection coefficient Γf,m(t), the backward reflection coefficient Γb,m(t), and the regression coefficient κm,λa(t) register 90, 92, and 96 values in each stage, m. In addition to the forward prediction error fm(t), the backward prediction error bm(t), and the estimation error em,λa(t) signals, a number of intermediate variables, not shown in FIG. 7 but based on the values labelled in FIG. 7, are required to calculate the forward reflection coefficient Γfm,(t), the backward reflection coefficient Γb,m(t), and the regression coefficient κm,λa(t) register 90, 92, and 96 values.

Intermediate variables include a weighted sum of the forward prediction error squares Fm(t), a weighted sum of the backward prediction error squares βm(t), a scaler parameter Δm(t), a conversion factor γm(t), and another scaler parameter ρm,λa(t). The weighted sum of the forward prediction errors Fm(t) is defined as: F m ( t ) = i = 1 t λ t - i f m ( i ) 2 ; ( 38 )

Figure USRE038492-20040406-M00005

where λ without a wavelength identifier, a or b, is a constant multiplicative value unrelated to wavelength and is typically less than or equal to one, i.e., λ≦1. The weighted sum of the backward prediction errors βm(t) is defined as: β m ( t ) = i = 1 t λ t - i b m ( i ) 2 ( 39 )

Figure USRE038492-20040406-M00006

where, again, λ without a wavelength identifier, a or b, is a constant multiplicative value unrelated to wavelength and is typically less than or equal to one, i.e., λ≦1. These weighted sum intermediate error signals can be manipulated such that they are more easily solved for, as described in Chapter 9, §9.3, and defined hereinafter in equations (53) and (54).

Description of the Joint Process Estimator

The operation of the joint process estimator 60 is as follows. When the joint process estimator 60 is turned on, the initial values of intermediate variable and signal including the parameter Δm-1(t), the weighted sum of the forward prediction error signals Fm-1(t), the weighted sum of the backward prediction error signals βm-1(t), the parameter ρm,λa(t), and the zero-stage estimation error e0,λa(t) are initialized, some to zero and some to a small positive number δ:

Δm-1(0)=0;   (40)

Fm-1(0)=δ;   (41)

βm-1(0)=δ;   (42)

ρm,λa(0)=0;   (43)

e0,λa(t)=Sλa(t) for t≧0.   (44)

After initialization, a simultaneous sample of the measured signal Sλa(t) and the noise reference signal n′(t) are input to the joint process estimator 60, as shown in FIG. 7. The forward and backward prediction error signals f0t and b0t, and intermediate variables including the weighted sums of the forward and backward error signals ƒ0t and β0t, and the conversion factor τO(t) are calculated for the zero-stage according to:

f0(t)=b0(t)=n′(t)   (45)

F0(t)=β0(t)=λF0(t−1)+|n′(t)|2   (46)

γ0(t−1)=1   (47)

where, again, λ without a wavelength identifier, a or b, is a constant multiplicative value unrelated to wavelength.

Forward reflection coefficient Γf,m(t), backward reflection coefficient Γb,m(t), and regression coefficient κm,λa(t) register 90, 92 and 96 values in each stage thereafter are set according to the output of the previous stage. The forward reflection coefficient Γf,1(t), backward reflection coefficient Γb,1(t), and regression coefficient κ1,λa(t) register 90, 92 and 96 values in the first stage are thus set according to algorithm using values in the zero-stage of the joint process estimator 60. In each stage, m≧1, intermediate values and register values including the parameter Δm-1(t); the forward reflection coefficient Γf,m(t) register 90 value; the backward reflection coefficient Γb,m(t) register 92 value; the forward and backward error signals fm(t) and bm(t); the weighted sum of squared forward prediction errors Ff,m(t), as manipulated in § 9.3 of the Haykin book; the weighted sum of squared backward prediction errors βb,m(t), as manipulated in § 9.3 of the Haykin book; the conversion factor γm(t); the parameter ρm,λa(t); the regression coefficient κm,λa(t) register 96 value; and the estimation error em+1,λa(t) value are set according to:

Δm-1(t)=λΔm-1(t−1)+{bm-1(t−1)f*m-1(t−1)}  (48)

Γf,m(t)=−{Δm-1(t)/βm-1(t−1}  (49)

Γb,m(t)=−{Δ*m-1(t)/Fm-1(t−1)}  (50)

fm(t)=fm-1(t)+Γ*f,m(t)bm-1(t−1)   (51)

bm(t)=bm-1(t−1)+Γ*b,m(t)fm-1(t)   (52)

Fm(t)=Fm-1(t)−{|Δm-1(t)|2m-1(t−1)}  (53)

βm(t)=βm-1(t−1)−{|Δm-1(t)|2/Fm-1(t)}  (54)

γm(t−1)=γm-1(t−1)−{|bm-1(t−1)(|2m-1(t−1)}  (55)

ρm,λa(t)=λρm,λa(t−1)+{bm(t)e*m,λa(t)/γm(t)}  (56)

κm,λa(t)={ρm,λa(t)/βm(t)}  (57)

em+1,λa(t)=en,λa(t)−κ*m(t)bm(t)   (58)

where a (*) denotes a complex conjugate.

These equations cause the error signals fm(t), bm(t), em,λa(t) to be squared or to be multiplied by one another, in effect squaring the errors, and creating new intermediate error values, such as Δm-1(t). The error signals and the intermediate error values are recursively tied together, as shown in the above equations (48) through (58). They interact to minimize the error signals in the next stage.

After a good approximation to the desired signal Y′λa(t) has been determined by the joint process estimator 60, a next set of samples, including a sample of the measured signal Sλa(t) and a sample of the noise reference signal n′(t), are input to the joint process estimator 60. The re-initialization process does not re-occur, such that the forward and backward reflection coefficient Γr,m(t) and Γb,m(t) register 90, 92 values and the regression coefficient κm,λa(t) register 96 value reflect the multiplicative values required to estimate the desired portion Yλa(t) of the sample of Sλa(t) input previously. Thus, information from previous samples is used to estimate the desired signal portion of a present set of samples in each stage.

Flowchart of Joint Process Estimator

In a signal processor, such as a physiological monitor, incorporating a reference processor of the present invention to determine a noise reference signal n′(t) for input to an adaptive noise canceler, a joint process estimator 60 type adaptive noise canceler is generally implemented via a software program having an interactive loop. One iteration of the loop is analogous to a single stage of the joint process estimator as shown in FIG. 7. Thus, if a loop is iterated m times, it is equivalent to an m stage joint process estimator 60.

A flow chart of a subroutine to estimate the desired signal portion Yλa(t) of a sample of a measured signal, Sλa(t) is shown in FIG. 8. The flow chart describes how the action of a reference processor for determining the noise reference signal and the joint process estimator 60 would be implemented in software.

A one-time only initialization is performed when the physiological monitor is turned on, as indicated by an “INITIALIZE NOISE CANCELER” box 120. The initialization sets all registers 90, 92, and 96 and delay element variables 110 to the values described above in equations (40) through (44).

Next, a set of simultaneous samples of the measured signals Sλa(t) and Sλb(t) is input to the subroutine represented by the flowchart in FIG. 8. Then a time update of each of the delay element program variable occurs, as indicated in a “TIME UPDATE OF [Z−1] ELEMENTS” box 130, wherein the value stored in each of the delay element variables 110 is set to the value at the input of the delay element variables 110. Thus, the zero-stage backward prediction error b0(t) is stored in the first-stage delay element variable, the first-stage backward prediction error b1(t) is stored in the second-stage delay element variable, and so on.

Then, using the set of measured signal samples Sλs(t) and Sλb(t), the noise reference signal is calculated according to the ratiometric or the constant saturation method described above. This is indicated by a “CALCULATE NOISE REFERENCE (n′(t)) FOR TWO MEASURED SIGNAL SAMPLES” box 140. The ratiometric method is generally preferred since no assumptions about constant saturation values need be made.

A zero-stage order update is performed next as indicated in a “ZERO-STAGE UPDATE” box 150. The zero-stage backward prediction error b0(t), and the zero-stage forward prediction error f0(t) are set equal to the value of the noise reference signal n′(t). Additionally, the weighted sum of the forward prediction errors Fm(t) and the weighted sum of backward prediction errors βm(t) are set equal to the value defined in equation (46).

Next, a loop counter, m, is initialized as indicated in a “m=0” box 160. A maximum value of m, defining the total number of stages to be used by the subroutine corresponding to the flowchart in FIG. 8, is also defined. Typically, the loop is constructed such that it stops iterating once a criterion for convergence upon a best approximation to the desired signal has been met by the joint process estimator 60. Additionally, a maximum number of loop iterations may be chosen at which the loop stops iteration. In a preferred embodiment of a physiological monitor of the present invention, a maximum number of iterations, m=60 to m=80, is advantageously chosen.

Within the loop, the forward and backward reflection coefficient Γf,m(t) and Γb,m(t) register 90 and 92 values in the least-squares lattice filter are calculated first, as indicated by the “ORDER UPDATE MTH CELL OF LSL-LATTICE” box 170 in FIG. 8. This requires calculation of intermediate variable and signal values used in determining register 90, 92, and 96 values in the present stage, the next stage, and in the regression filter 80.

The calculation of regression filter register 96 value κm,λa(t) is performed next, indicated by the “ORDER UPDATE MTH STAGE OF REGRESSION FILTER(S)” box 180. The two order update boxes 170 and 180 are performed in sequence m times, until m has reached its predetermined maximum (in the preferred embodiment, n=60 to m=80) or a solution has been converged upon, as indicated by a YES path from a “DONE” decision box 190. mn a computer subroutine, convergence is determined by checking if the weighted sums of the forward and backward prediction errors Fm(t) and βm(t) are less than a small positive number. An output is calculated next, as indicated by a “CALCULATE OUTPUT” box 200. The output is a good approximation to the desired signal, as determined by the reference processor and joint process estimator 60 subroutine corresponding to the flow chart of FIG. 8. This is displayed (or used in a calculation in another subroutine), as indicated by a “TO DISPLAY” box 210.

A new set of samples of the two measured signals Sλa(t) and Sλb(t) is input to the processor and joint process estimator 60 adaptive noise canceler subroutine corresponding to the flowchart of FIG. 8 and the process reiterates for these samples. Note, however, that the initialization process does not re-occur. New sets of measured signal samples Sλa(t) an Sλb(t) are continuously input to the reference processor and joint process estimator 60 adaptive noise canceler subroutine. The output forms a chain of samples which is representative of a continuous wave. This waveform is a good approximation to the desired signal waveform Y′λa(t) at wavelength λa.

Calculation of Saturation from Adaptive Noise Canceler Output

Physiological monitors typically use the approximation of the desired signal Y′λa(t) to calculate another quantity, such as the saturation of one constituent in a volume containing that constituent plus one or more other constituents. Generally, such calculations require information about a desired signal at two wavelengths. In some measurements, this wavelength is λb, the wavelength used in the calculation of the noise reference signal n′(t). For example, the constant saturation method of determining the noise reference signal requires a good approximation of the desired signal portions Yλa(t) and Yλb(t) of both measured signals Sλa(t) and Sλb(t). Then, the saturation is determined from the approximations to both signals, i.e. Y′λa(t) and Y′λb(t).

In other physiological measurements, information about a signal at a third wavelength is necessary. For example, to find the saturation using the ratiometric method, signals Sλa(t) and Sλb(t) are used to find the noise reference signal n′(t). But as discussed previously, λa and λb were chosen to satisfy a proportionately relationship like that of equation (22). This proportionality relationship forces the two desired signal portions Yλa(t) and Yλb(t) to be linearly dependent. Generally, linearly dependant mathematical equations cannot be solved for the unknowns. Analogously, some desirable information cannot be derived from two linearly dependent signals. Thus, to determine the saturation using the ratiometric method, a third signal is simultaneously measured at wavelength λc. The wavelength λc is chosen such that the desired portion Yλc(t) of the measured signal Sλc(t) is not linearly dependent with the desired portions Yλa(t) and Yλb(t) of the measured signals Sλa(t) and Sλb(t). Since all measurements are taken substantially simultaneously, the noise reference signal n′(t) is correlated to the undesired signal portions nλa, nλb, and nλc of each of the measured signals Sλa(t), Sλb(t), and Sλc(t) can be used to estimate approximations to the desired signal portions Yλa(t), Yλb(t), and Yλc(t), for all three measured signals Sλa(t), Sλb(t), Sλc(t). Using the ratiometric method, estimation of the desired signal portions Yλa(t) and Yλc(t) of two measured signals Sλa(t) and Sλc(t), chosen correctly, is usually satisfactory to determine most physiological data.

A joint process estimator 60 having two regression filters 80a and 80b is shown in FIG. 9. A first regression filter 80a accepts a measured signal Sλa(t). A second regression filter 80b accepts a measured signal Sλb(t) or the ratiometric method is used to determine the noise reference signal n′(t). The first and second regression filters 80a and 80b are independent. The backward prediction error bm(t) is input to each regression filter 80a and 80b, the input for the second regression filter 80b bypassing the first regression filter 80a.

The second regression filter 80b comprises registers 98, and summing elements 108 arranged similarly to those in the first regression filter 80a. The second regression filter 80b operates via an additional intermediate variable in conjunction with those defined by equations (48) through (58), i.e.:

ρm,λb(t)=λρm,λb(t−1)+{bm(t)e*m,λb(t)/γm(t)}; or   (59)

ρm,λc(t)=λρm,λc(t−1)+{bm(t)e*m,λc(t)/γm(t)}; and   (60)

ρ0,λb(0)=0; or   (61)

ρ0,λc(0)=0.   (62)

The second regression filter 80b has an error signal value defined similar to the first regression filter error signal values, em+1,λa(t), i.e.:

em+1,λb(t)=em,λb(t)−κ*m,λb(t)bm(t); or   (63)

em+,λc(t)=em,λc(t)−κ*m,λb(t)bm(t); and   (64)

e0,λb(t)=Sλb(t) for t≧0; or   (65)

 e0,λc(t)=Sλc(t) for t≧0.   (66)

The second regression filter has a regression coefficient κm,λb(t) register 98 value defined similarly to the first regression filter error signal values, i.e.:

κm,λb(t)={ρm,λb(t)/βm(t)}; or   (67)

κm,λc(t)={ρm,λc(t)/βm(t)};   (68)

These values are used in conjunction with those intermediate variable values, signal values, register and register values defined in equations (40) through (58). These signals are calculated in an order defined by placing the additional signals immediately adjacent a similar signal for the wavelength λa.

For the ratiometric method, Sλc(t) is input to the second regression filter 80b. The output of the second regression filter 80b is then a good approximation to the desired signal Y′λc(t). For the constant saturation method, Sλb(t) is input to the second regression filter 80b. The output is then a good approximation to the desired signal Y′λb(t).

The addition of the second regression filter 80b does not substantially change the computer program subroutine represented by the flowchart of FIG. 8. Instead of an order update of the mth stage of only one regression filter, an order update of the mth stage of both regression filters 80a and 80b is performed. This is characterized by the plural designation in the “ORDER UPDATE OF mth STAGE OF REGRESSION FILTER(S)” box 180 in FIG. 8. Since the regression filters 80a and 80b operate independently, independent calculations can be performed in the reference processor and joint process estimator 60 adaptive noise canceler subroutine modeled by the flowchart of FIG. 8.

Calculation of Saturation

Once good approximations to the desired signals, Y′λa(t) and Y′λc(t) for the ratiometric method and Y′λa(t) and Y′λb(t) for the constant saturation method, have been determined by the joint process estimator 60, the saturation of A5 in a volume containing A5 and A6, for example, may be calculated according to various known methods. Mathematically, the approximations to the desired signals can be written:

Y′λa(t)≈ε5,λac5x5,6(t)+ε6,λac6x5,6(t); and   (69)

Y′λc(t)≈ε5,λcc5x5,6(t)+ε6,λcc6x5,6(t).   (70)

for the ratiometric method using wavelengths λa and λc. For the constant saturation method, the approximations to the desired signals can be written in terms of λa and λb as:

Y′λa(t)≈ε5,λac5x5,6(t)+ε6,λac6x5,6(t); and   (71)

Y′λb(t)≈ε5,λbc5x5,6(t)+ε6,λbc6x5,6(t).   (72)

This is equivalent to two equations having three unknowns, namely c5(t), c6(t) and x5,6(t). In both the ratiometric and the constant saturation cases, the saturation can be determined by acquiring approximations to the desired signal portions at two different, yet proximate times t1 and t2 over which the saturation of A5 in the volume containing A5 and A6 does not change substantially. For example, for the desired signals estimated by the ratiometric method, at times t1 and t2:

Y′λa(t1)≈ε5,λac5x5,6(t1)+ε6,λac6x5,6(t1)   (73)

Y′λc(t1)≈ε5,λcc5x5,6(t1)+ε6,λcc6x5,6(t1)   (74)

Y′λa(t2)≈ε5,λac5x5,6(t2)+ε6,λac6x5,6(t2)   (75)

Y′λc(t2)≈ε5,λcc5x5,6(t2)+ε6,λcc6x5,6(t2)   (76)

Then, difference signals may be determined which relate the signals of equation (73) through (76), i.e.:

ΔYλa≈Y′λa(t1)−Y′λa(t2)=ε5,λac5Δx+ε6,λac6Δx; and   (77)

ΔYλc≈Y′λc(t1)−Y′λc(t2)=ε5,λcc5Δx+ε6,λcc6Δx;   (78)

where Δx=x5,6(t1)−x5,6(t2) The average saturation at time t=(t1+t2)/2 is: Saturation ( t ) = c 5 ( t ) / [ c 5 ( t ) + c 6 ( t ) ] ( 79 ) = ε 6 λ a - ε 6 λ b ( Δ Y λ a Δ Y λ b ) ε 6 λ a - ε 5 λ a - ( ε 6 λ b - ε 5 λ b ) ( Δ Y λ a Δ Y λ b ) ( 80 )

Figure USRE038492-20040406-M00007

It will be understood that the Δx term drops out from the saturation calculation because of the division. Thus, knowledge of the thickness of the desired constituents is not required to calculate saturation.

Pulse Oximetry Measurements

A specific example of a physiological monitor utilizing a processor of the present invention to determine a noise reference signal n′(t) for input to an adaptive noise canceler that removes erratic motion-induced undesired signal portions is a pulse oximeter. A pulse oximeter typically causes energy to propagate through a medium where blood flows close to the surface for example, an ear lobe, or a digit such as a finger, or a forehead. An attenuated signal is measured after propagation through or reflection from the medium. The pulse oximeter estimates the saturation of oxygenated blood available to the body for use.

Freshly oxygenated blood is pumped at high pressure from the heart into the arteries for use by the body. The volume of blood in the arteries varies with the heartbeat, giving rise to a variation in absorption of energy at the rate of the heartbeat, or the pulse.

Oxygen depleted, or deoxygenated, blood is returned to the heart by the veins along with unused oxygenated blood. The volume of blood in the veins varies with the rate of breathing, which is typically much slower than the heartbeat. Thus, when there is no motion induced variation in the thickness of the veins, venous blood causes a low frequency variation in absorption of energy. When there is motion induced variation in the thickness of veins, the low frequency variation in absorption is coupled with the erratic variation in absorption due to motion artifact.

In absorption measurements using the transmission of energy through a medium, two light emitting diodes (LED's) are positioned on one side of a portion of the body where blood flows close to the surface, such as a finger, and a photodetector is positioned on the opposite side of the finger. Typically, in pulse oximetry measurements, one LED emits a visible wavelength, preferably red, and the other LED emits an infrared wavelength. However, one skilled in the art will realize that other wavelength combinations could be used.

The finger comprises skin, tissue, muscle, both arterial blood and venous blood, fat, etc., each of which absorbs light energy differently due to different absorption coefficients, different concentrations, and different thicknesses. When the patient is not moving, absorption is substantially constant except for the flow of blood. This constant attenuation can be determined and subtracted from the signal via traditional filtering techniques. When the patient moves, the absorption becomes erratic. Erratic motion induced noise typically cannot be predetermined and subtracted from the measured signal via traditional filtering techniques. Thus, determining the saturation of oxygenated arterial blood becomes more difficult.

A schematic of a physiological monitor for pulse oximetry is shown in FIG. 10. Two LED's 300 and 302, one LED 300 emitting red wavelengths and another LED 302 emitting infrared wavelengths, are placed adjacent a finger 310. A photodetector 320, which produces an electrical signal corresponding to the attenuated visible and infrared light energy signals is located opposite the LED's 300 and 302. The photodetector 320 is connected to a single channel of common processing circuitry including an amplifier 330 which is in turn connected to a band pass filter 340. The band pass filter 340 passes signal into a synchronized demodulator 350 which has a plurality of output channels. One output channel is for signals corresponding to visible wavelengths and another output channel is for signals corresponding to infrared wavelengths.

The output channels of the synchronized demodulator for signals corresponding to both the visible and infrared wavelengths are each connected to separate paths, each path comprising further processing circuitry. Each path includes a DC offset removal element 360 and 362, such as a differential amplifier, a programmable gain amplifier 370 and 372 and a low pass filter 380 and 382. The output of each low pass filter 380 and 382 is amplified in a second programmable gain amplifier 390 and 392 and then input to a multiplexer 400.

The multiplexer 400 is connected to an analog-to-digital converter 410 which is in turn connected to a microprocessor 420. Control lines between the microprocessor 420 and the multiplexer 400, the microprocessor 420 and the analog-to-digital converter 410, and the microprocessor 420 and each programmable gain amplifier 370, 372, 390, and 392 are formed. The microprocessor 420 has additional control lines, one of which leads to a display 430 and the other of which leads to an LED driver 440 situated in a feedback loop with the two LED's 300 and 302.

The LED's 300 and 302 each emits energy which is absorbed by the finger 310 and received by the photodetector 320. The photodetector 320 produces an electrical signal which corresponds to the intensity of the light energy striking the photodetector 320 surface. The amplifier 330 amplifies this electrical signal for ease of processing. The band pass filter 340 then removes unwanted high and low frequencies. The synchronized demodulator 350 separates the electrical signal into electrical signals corresponding to the red and infrared light energy components. A predetermined reference voltage, Vref, is subtracted by the DC offset removal element 360 and 362 from each of the separate signals to remove substantially constant absorption which corresponds to absorption when there is not motion induced undesired signal component. Then the first programmable gain amplifiers 370 and 372 amplify each signal for ease of manipulation. The low pass filters 380 and 382 integrate each signal to remove unwanted high frequency components and the second programmable gain amplifiers 390 and 392 amplify each signal for further ease of processing.

The multiplexer 400 acts as an analog switch between the electrical signals corresponding to the red and the infrared light energy, allowing first a signal corresponding to the red light to enter the analog-to-digital converter 410 and then a signal corresponding to the infrared light to enter the analog-to-digital converter 410. This eliminates the need for multiple analog-to-digital convertors 410. The analog-to-digital convertor 410 inputs the data into the microprocessor 420 for calculation of a noise reference signal via the processing technique of the noise reference signal via the processing technique of the present invention and removal of undesired signal portions via an adaptive noise canceler. The microprocessor 420 centrally controls the multiplexer 400, the analog-to-digital converter 410, and the first and second programmable gain amplifiers 370 and 390 for both the red and the infrared channels. Additionally, the microprocessor 420 controls the intensity of the LED's 302 and 304 through the LED driver 440 in a servo loop to keep the average intensity received at the photodetector 320 within an appropriate range. Within the microprocessor 420 a noise reference signal n′(t) is calculated via either the constant saturation method or the ratiometric method, as described above, the ratiometric method being generally preferred. This signal is used in an adaptive noise canceler of the joint process estimator type 60, described above.

The multiplexer 400 time multiplexes, or sequentially switches between, the electrical signals corresponding to the red and the infrared light energy. This allows a single channel to be used to detect and begin processing the electrical signals. For example, the red LED 300 is energized first and the attenuated signal is measured at the photodetector 320. An electrical signal corresponding to the intensity of the attenuated red light energy is passed to the common processing circuitry. The infrared LED 302 is energized next and the attenuated signal is measured at the photodetector 320. An electrical signal corresponding to the intensity of the attenuated infrared light energy is passed to the common processing circuitry. Then, the red LED 300 is energized again and the corresponding electrical signal is passed to the common processing circuitry. The sequential energization of LED's 300 and 302 occurs continuously while the pulse oximeter is operating.

The processing circuitry is divided into distinct paths after the synchronized demodulator 350 to ease time constraints generated by time multiplexing. In the preferred embodiment of the pulse oximeter shown in FIG. 10, a sample rate, or LED energization rate, of 1000 Hz is advantageously employed. Thus, electrical signals reach the synchronized demodulator 350 at a rate of 1000 Hz. Time multiplexing is not used in place of the separate paths due to settling time constraints of the low pass filters 380, 382, and 384.

In FIG. 10, a third LED 304 is shown adjacent the finger, located near the LED's 300 and 302. The third LED 304 is used to measure a third signal Sλc(t) to be used to determine saturation using the ratiometric method. The third LED 304 is time multiplexed with the red and infrared LED's 300 and 302. Thus, a third signal is input to the common processing circuitry in sequence with the signals from the red and infrared LED's 300 and 302. After passing through and being processed by the operational amplifier 330, the band pass filter 340, and the synchronized demodulator 350, the third electrical signal corresponding to light energy at wavelength λc is input to a separate path including a DC offset removal element 364, a first programmable gain amplifier 374, a low pass filter 384, and a second programmable gain amplifier 394. The third signal is then input to the multiplexer 400.

The dashed line connection for the third LED 304 indicates that this third LED 304 is incorporated into the pulse oximeter when the ratiometric method is used; it is unnecessary for the constant saturation method. When the third LED 304 is used, the multiplexer 400 acts as an analog switch between all three LED 300, 302, and 304 signals. If the third LED 304 is utilized, feedback loops between the microprocessor 420 and the first and second programmable gain amplifier 374 and 394 in the λc wavelength path are also formed.

For pulse oximeter measurements using the ratiometric method, the signals (logarithm converted) transmitted through the finger 310 at each wavelength λa, λb, and λc are:

Sλa(t)=Sλred1(t)=εHbO2,λaCA HbO2XA(t)+εHb,λaC1 HbXA(t)+εHbO2,λaCV HbO2Xv(t)+εHb,λaCV HbXV(t)+nλa(t).   (81)

Sλb(t)=Sλred2(t)=εHbO2,λbCA HbO2XA(t)+εHb,λbCA HbXA(t)+εHbO2,λbCV HbO2XV(t)+εHb,λbCV HbXV(t)+nλb(t).   (82)

Sλc(t)=SλIR(t)=εHbO2,λcCA HbO2XA(t)+εHb,λcCA HbXA(t)+εHbO2,λcCV HbO2XV(t)+εHb,λcCV HbXV+nλc(t).   (83)

In equations (81) through (83), XA(t) is the lump-sum thickness of the arterial blood in the finger; XV(t) is the lump-sum thickness of venous blood in the finger; εHbO2,λa εHbO2,λb, εHbO2,λc, εHb,λa, εHb,λb, and εHb,λc are the absorption coefficients of the oxygenated and non-oxygenated hemoglobin, at each wavelength measured; and cHbO2(t) and cHb(t) with the superscript designations A and V are the concentrations of the oxygenated and non-oxygenated arterial blood and venous blood, respectively.

For the ratiometric method, the wavelengths chosen are typically two in the visible red range, i.e., λa and λb, and one in the infrared range, i.e., λc. As described above, the measurement wavelengths λa and λb are advantageously chosen to satisfy a proportionality relationship which removes the desired signal portion Yλa(t) and Yλb(t), yielding a noise reference signal n′(t). In the preferred embodiment, the ratiometric method is used to determine the noise reference signal n′(t) by picking two wavelengths that cause the desired portions Yλa(t) and Yλb(t) of the measured signals Sλa(t) and Sλb(t) to become linearly dependent similarly to equation (22); i.e. wavelengths λa and λb which satisfy:

εHbO2,λaHb,λaHbO2,λbHb,λb   (84)

Typical wavelength values chosen are λa=650 nm and λb=685 nm. Additionally a typical wavelength value for λc is λc=940 nm. By picking wavelengths λa and λb to satisfy equation (84) the venous portion of the measured signal is also caused to become linearly dependent even though it is not a portion of the desired signal. Thus, the venous portion of the signal is removed with the desired portion. The proportionality relationship between equations (81) and (82) which allows determination of a non-zero noise reference signal n′(t), similarly to equation (25) is:

ωr4Hb,λcHb,λb; where   (85)

nλa(t)≠ωr4nλb(t).   (86)

In pulse oximetry, both equations (85) and (86) can typically be satisfied simultaneously.

FIG. 11 is a graph of the absorption coefficients of oxygenated and deoxygenated hemoglobin (εHbO2 and εHb) vs. wavelength (λ). FIG. 12 is a graph of the ratio of absorption coefficients vs. wavelength, i.e., εHbHbO2 vs. λ over the range of wavelength within circle 13 in FIG. 11. Anywhere a horizontal line touches the curve of FIG. 12 twice, as does line 400, the condition of equation (84) is satisfied. FIG. 13 shows an exploded view of the area of FIG. 11 within the circle 13. Values of εHbO2 and εHb at the wavelengths where a horizontal line touches the curve of FIG. 12 twice can then be determined from the data in FIG. 13 to solve for the proportionality relationship of equation (85).

A special case of the ratiometric method is when the absorption coefficients εHbO2 and εHb are equal at a wavelength. Arrow 410 in FIG. 11 indicates one such location, called an isobestic point. FIG. 13 shows an exploded view of the isobestic point. To use isobestic points with the ratiometric method, two wavelengths at isobestic points are determined to satisfy equation (84).

Multiplying equation (82) by ωr4 and then subtracting equation (82) from equation (81), a non-zero noise reference signal n′(t) is determined by:

n′(t)=Sλa(t)−ωr4Sλb(t)=nλa(t)−ωr4nλb.   (87)

This noise reference signal n′(t) has spectral content corresponding to the erratic, motion-induced noise. When it is input to an adaptive noise canceler, with either signals Sλa(t) and Sλc(t) or Sλb(t) and Sλc(t) input to two regression filters 80a and 80b, the adaptive noise canceler will function much like an adaptive multiple notch filter and remove frequency components present in both the noise reference signal n′(t) and the measured signals from the measured signals Sλa(t) and Sλc(t) or Sλb(t) and Sλc(t). Thus, the adaptive noise canceler is able to remove erratic noise caused in the venous portion of the measured signals Sλa(t), Sλb(t), and Sλc(t) even though the venous portion of the measured signals Sλa(t) and Sλb(t) was not incorporated in the noise reference signal n′(t). However, the low frequency absorption caused by venous blood moving through the veins is generally not one of the frequencies incorporated into the noise reference signal n′(t). Thus, the adaptive noise canceler generally will not remove this portion of the undesired signal. However, a band pass filter applied to the approximations to the desired signals Y′λa(t) and Yλc(t) or Yλb(t) and Y′λc(t) can remove this portion of the undesired signal corresponding to the low frequency venous absorption.

For pulse oximetry measurements using the constant saturation method, the signals (logarithm converted) transmitted through the finger 310 at each wavelength λa and λb are:

Sλa(t)=Sλred1(t)=εHbO2,λaCA HbO2XA(t)+εHb,λaCA HbXA(t)+εHbO2,λaCV HbO2Xv(t)+εHb,λaCV HbXV(t)+nλa(t).   (88)

Sλb(t)=SλIR(t)=εHbO2,λbCA HbO2XA(t)+εHb,λbCA HbXA(t)+εHbO2,λbCV HbO2XV(t)+εHb,λbCV HbXV(t)+nλb(t).   (89)

For the constant saturation method, the wavelengths chosen are typically one in the visible red range, i.e., λa, and one in the infrared range, i.e., λb. Typical wavelength values chosen are λa=660 nm and λb=940 nm. Using the constant saturation method, it is assumed that CHbO2(t)/CHb(t)=constant. The saturation of oxygenated arterial blood changes slowly, it at all, with respect to the sample rate, making this a valid assumption. The proportionality factor between equation (88) and (89) can then be written as: ω s4 ( t ) = ε Hb02 λ a c Hb02 x ( t ) + ε Hb λ a c Hb x ( t ) ε Hb02 λ b c Hb02 x ( t ) + ε Hb λ b c Hb x ( t ) ( 90 ) Y λ a ( t ) / Y λ b ( t ) ; where ( 91 ) n λ a ( t ) ω s4 ( t ) n λ b ( t ) . ( 92 )

Figure USRE038492-20040406-M00008

In pulse oximetry, it is typically the case that both equation (91) and (92) can be satisfied simultaneously.

Multiplying equation (89) by ωs4(t) and then subtracting equation (89) from equation (88), a non-zero noise reference signal n′(t) is determined by:

n′(t)=  (93)

Sλa(t)−ωS4(t)Sλb(t)=εHbO2,λacV HbO2xV(t)+εHb,λacV HbxV(t)+nλa(t)−ωs4HbO2,λbcV HbO2xV(t)+εHb,λbcV HbxV(t)+nλb(t)].   (94)

The constant saturation assumption does not cause the venous contribution to the absorption to be canceled along with the desired signal portions Yλa(t) and Yλb(t), as did the relationship of equation (84) used in the ratiometric method. Thus, frequencies associated with both the low frequency modulated absorption due to venous absorption when the patient is still and the erratically modulated absorption due to venous absorption when the patient is moving are represented in the noise reference signal n′(t). Thus, the adaptive canceler can remove both erratically modulated absorption due to venous blood in the finger under motion and the constant low frequency cyclic absorption of venous blood.

Using either method, a noise reference signal n′(t) is determined by the processor of the present invention for use in an adaptive noise canceler which is defined by software in the microprocessor. The preferred adaptive noise canceler is the joint process estimator 60 described above.

Illustrating the operation of the ratiometric method of the present invention, FIGS. 14, 15 and 16 show signals measured for use in determining the saturation of oxygenated arterial blood using a reference processor of the present invention which employs the ratiometric method, i.e., the signals Sλa(t)=Sλred1(t), Sλb(t)=Sλred2(t), and Sλc(t)=SλIR(t). A first segment 14a, 15a, and 16a of each of the signals is relatively undistributed by motion artifact, i.e., the patient did not move substantially during the time period in which these segments were measured. These segments 14a, 15a, and 16a are thus generally representative of the described plethysmographic waveform at each of the measured wavelengths. A second segment 14b, 15b and 16b of each of the signals is affected by motion artifact, i.e., the patient did move during the time period in which these segments were measured. Each of these segments 14b, 15b, and 16 shows large motion induced excursions in the measured signal. A third segment 14c, 15c, and 16c of each of the signals is again relatively unaffected by motion artifact and is thus generally representative of the desired plethysmographic waveform at each of the measured wavelengths.

FIG. 17 shows the noise reference signal n′(t)=nλa−ωr4nλb(t), as determined by a reference processor of the present invention utilizing the ratiometric method. As discussed previously, the noise reference signal n′(t) is correlated to the undesired signal portions nλa, nλb, and nλc. Thus, a first segment 17a of the noise reference signal n′(t) is generally flat, corresponding to the fact that there is very little motion induced noise in the first segments 14a, 15a, and 16a of each signal. A second segment 17b of the noise reference signal n′(t) exhibits large excursions, corresponding to the large motion induced excursions in each of the measured signals. A third segment 17c of the noise reference signal n′(t) is generally flat, again corresponding to the lack of motion artifact in the third segments 14a, 14b, and 14c of each measured signal.

FIGS. 18 and 19 show the approximation Y′λa(t) and Y′λc(t) to the desired signals Yλa(t) and Yλc(t) as estimated by the joint process estimator 60 using a noise reference signal n′(t) determined by the ratiometric method. Note that the scale of FIGS. 14 through 19 is not the same for each figure to better illustrate changes in each signal. FIGS. 18 and 19 illustrate the effect of the joint process estimator adaptive noise canceler using the nose reference signal n′(t) as determined by the reference processor of the present invention using the ratiometric method. Segments 18b and 19b are not dominated by motion induced noise as were segments 14b, 15b, and 16b of the measured signals. Additionally, segments 18a, 19a, 18c, and 19c have not been substantially changed from the measured signal segments 14a, 15a, 16a, 14c, 15c, and 16c where there was no motion induced noise.

Illustrating the operation of the constant saturation method of the present invention. FIGS. 20 and 21 show signals measured for input to a reference processor of the present invention which employs the constant saturation method, i.e., the signals Sλa(t)=Sλred1(t) and Sλb(t)=SλIR(t). A first segment 20a and 21a of each of the signals is relatively undistributed by motion artifact, i.e., the patient did not move substantially during the time period in which these segments were measured. These segments 20a and 21a are thus generally representative of the desired plethysmographic waveform at each of the measured wavelengths. A second segment 20b and 21b of each of the signals is affected by motion artifact, i.e., the patient did move during the time period in which these segments were measured. Each of these segments 20b and 21b shows large motion induced excursions in the measured signal. A third segment 20c and 21c of each of the signals is again relatively unaffected by motion artifact and is thus generally representative of the desired plethysmographic waveform at each of the measured wavelengths.

FIG. 22 shows the noise reference signal n′(t)=nλa(t)−ωs4nλb(t), as determined by a reference processor of the present invention utilizing the constant saturation method. Again, the noise reference signal n′(t) is correlated to the undesired signal portions nλa and nλb. Thus, a first segment 22a of the noise reference signal n′(t) is generally flat, corresponding to the fact that there is very little motion induced noise in the first segments 20a and 21a of each signal. A second segment 22b of the noise reference signal n′(t) exhibits large excursions, corresponding to the large motion induced excursions in each of the measured signals. A third segment 22c of the noise reference signal n′(t) is generally flat, again corresponding to the lack of motion artifact in the third segments 20b and 21c of each measured signal.

FIGS. 23 and 24 show the approximations Y′λa(t) and Y′λb(t) to the desired signals Y′λa(t) and Y′λb(t) as estimated by the joint process estimator 60 using a noise reference signal n′(t) determined by the constant saturation method. Note that the scale of FIGS. 20 and 24 is not the same for each figure to better illustrate changes in each signal. FIGS. 23 and 24 illustrate the effect of the joint process estimator adaptive noise canceler using the noise reference signal n′(t) as determined by a reference processor of the present invention utilizing the constant saturation method. Segments 23b and 24b are not dominated by motion induced noise as were segments 20b and 21b of the measured signals. Additionally, segments 23a, 24a, 23c, and 24c have not been substantially changed from the measured signal segments 20a, 21a, 20c, and 21c where there was no motion induced noise.

Method for Estimating Desired Portions of Measured Signals in a Pulse Oximeter

A copy of a computer program subroutine written in the C programming language, calculates a noise reference signal n′(t) using the ratiometric method and, using a joint process estimator 60, estimates the desired signal portions of two measured signals, each having an undesired portion which is correlated to the noise reference signal n′(t) and one of which was not used to calculate the noise reference signal n′(t), is appended in Appendix A. For example, Sλa(t)=Sλred1(t)=Sλ650nm(t) and Sλc(t)=SλIR(t)=Sλ940nm(t) can be input to the computer subroutine. One skilled in the art will realize that Sλa(t)=Sλred2(t)=Sλ685nm(t) and Sλc(t)=SλIR(t)=Sλ940nm(t) will also work. This subroutine is one way to implement the steps illustrated in the flowchart of FIG. 8 for a monitor particularly adapted for pulse oximetry.

The program estimates the desired signal portions of two light energy signals, one preferably corresponding to light in the visible red range and the other preferably corresponding to light in the infrared range such that a determination of the amount of oxygen available to the body, or the saturation of oxygen in the arterial blood, may be made. The calculation of the saturation is performed in a separate subroutine. Various methods for calculation of the oxygen saturation are known to those skilled in the art. One such calculation is described in the articles by G. A. Mook, et al, and Michael R. Neuman cited above. Once the concentration of oxygenated hemoglobin and deoxygenated hemoglobin are determined, the value of the saturation is determined similarly to equations (73) through (80) wherein measurements at times t1 and t2 are made at different, yet proximate times over which the saturation is relatively constant. For pulse oximetry, the average saturation at time t=(t1+t2)/2 is then determined by: Saturation ( t ) = C Hb02 ( t ) / [ C Hb02 ( t ) + C Hb ( t ) ] . ( 95 ) = ε Hb λ a - ε Hb λ b ( Δ Y λ a Δ Y λ b ) ε Hb λ a - ε Hb02 λ a - ( ε Hb λ b - ε Hb02 λ b ) ( Δ Y λ a Δ Y λ b ) ( 96 )

Figure USRE038492-20040406-M00009

Using the ratiometric method, three signals Sλa(t), Sλb(t), and Sλc(t) are input to the subroutine. Sλa(t) and Sλb(t) are used to calculate the noise reference signal n′(t). As described above, the wavelengths of light at which Sλa(t) and Sλb(t) are measured are chosen to satisfy the relationship of equation (84). Once the noise reference signal n′(t) is determined, the desired signal portions Yλa(t) and Yλc(t0 of the measured signals Sλa(t0 and Sλc(t) are estimated for use in calculation of the oxygen saturation.

The correspondence of the program variables to the variables defined in the discussion of the joint process estimator is as follows:

Δm(t)=nc[].Delta

Γf,m(t)=nc[].fref

Γb,m(t)=nc[].bref

fm(t)=nc[].ferr

bm(t)=nc[].berr

m(t)=nc[].Fswsqr

βm(t)=nc[].Bswsqr

γm(t)=nc[].Gamma

ρm,λa(t)=nc[].Roh_a

ρm,λc(t)=nc[].Roh_c

em,λa(t)=nc[].err_a

em,λc(t)=nc[].err_c

κm,λa(t)=nc[].K_a

κm,λc(t)=nc[].K_c

A first portion of the program performs the initialization of the registers 90, 92, 96, and 98 and intermediate variable values as in the “INITIALIZE NOISE CANCELER” box 120 and equations (40) through (44) and equations (61), (62), (65), and (66). A second portion of the program performs the time updates of the delay element variables 110 where the value at the input of each delay element variable 110 is stored in the delay element variable 110 as in the “TIME UPDATE OF [Z−1] ELEMENTS” box 130.

A third portion of the program calculates the noise reference signal, as in the “CALCULATE NOISE REFERENCE (n+(t)) FOR TWO MEASURED SIGNAL SAMPLES” box 140 using the proportionality constant ωr4 determined by the ratiometric method as in equation (85).

A fourth portion of the program performs the zero-stage update as in the “ZERO-STAGE UPDATE” box 150 where the zero-stage forward prediction error fo(t) and the zero-stage backward prediction error bo(t) are set equal to the value of the noise reference signal n′(t) just calculated. Additionally, zero-stage values of intermediate variables ℑ0(t) and β0(t) (nc[].Fswsqr and nc[].Bswsqr in the program) are calculated for use in setting register 90, 92, 96, and 98 values in the least-squares lattice predictor 70 and the regression filters 80a and 80b.

A fifth portion of the program is an iterative loop wherein the loop counter, m, is reset to zero with a maximum of m=NC_CELLS, as in the “m=0” box 160 in FIG. 8. NC_CELLS is a predetermined maximum value of iterations for the loop. A typical value of NC_CELLS is between 60 and 80, for example. The conditions of the loop are set such that the loop iterates a minimum of five times and continues to iterate until a test for conversion is met or m=NC_CELLS. The test for conversion is whether or not the sum of the weighted sum of forward prediction errors plus the weighted sum of backward prediction errors is less than a small number, typically 0.00001 (i.e, ℑm(t)+βm(t) ≦0.00001).

A sixth portion of the program calculates the forward and backward reflection coefficient Γm/(t) and Γm,b(t) register 90 and 92 values (nc[].fref and nc[].bref in the program) as in the “ORDER UPDATE mth-STAGE OF LSL-PREDICTOR” box 170 and equations (49) and (50). Then forward and backward prediction errors fm(t) and bm(t) (nc[].ferr and nc[].berr in the program) are calculated as in equations (51) and (52). Additionally, intermediate variables ℑm(t), βm(t) and γm(t) (nc[].Fswsqr, nc[].Bswsqr, nc[].Gamma in the program) are calculated, as in equations (53), (54), and (55). The first cycle of the loop uses the values for nc[0].Fswsqr and nc[0].Bswsqr calculated in the ZERO-STAGE UPDATE portion of the program.

A seventh portion of the program, still within the loop, calculates the regression coefficient κm,λa(t) and εm,λc(t) register 96 and 98 values (nc[].K_a and nc[].K_c in the program) in both regression filters, as in the “ORDER UPDATE mth STAGE OF REGRESSION FILTERS(S)” box 180 and equations (57) through (68). Intermediate error signals and variables em,λa(t), em,λc(t), ρm,λa(t), and ρm,λc(t) (nc[].err_a and nc[].err_c, nc[].roh_a, and nc[].roh_c in the subroutine) are also calculated as in equations (58), (64), (56), and (60), respectively.

The test for convergence of the joint process estimator is performed each time the loop iterates, analogously to the “DONE” box 190. If the sum of the weighted sums of the forward and backward prediction errors ℑm(t)+βm(t) is less than or equal to 0.00001, the loop terminates. Otherwise, the sixth and seventh portions of the program repeat.

When either the convergence test is passes for m=NC_CELLS, an eight portion of the program calculates the output of the joint process estimator 60 adaptive noise canceler as in the “CALCULATE OUTPUT” box 200. This output is good approximation to both of the desired signals Y′λa(t) and Y′λc(t) for the set of samples Sλa(t), Sλb(t), and Sλc(t) input to the program. After many sets of samples are processed by the joint process estimator, a compilation of the outputs provides output waves which are good approximations to the plethysmographic wave at each wavelength, λa and λc.

Another copy of a computer program subroutine, written in the C programming language, which calculates a noise reference signal n′(t) using the constant saturation method and, using a joint process estimator 60, estimates a good approximation to the desired signal portions of two measured signals, each having an undesired portion which is correlated to the noise reference signal n′(t) and each having been used to calculate the noise reference signal n′(t), is appended in Appendix B. This subroutine is another way to implement the steps illustrated in the flowchart of FIG. 8 for a monitor particularly adapted for pulse oximetry. The two signals are measured at two different wavelengths λa and λb, where λa is typically in the visible region and λb is typically in the infrared region. For example, in one embodiment of the present invention, tailored specifically to perform pulse oximetry using the constant saturation method, λa=660 nm and λb=940 nm.

The correspondence of the program variables to the variables defined in the discussion of the joint process estimator is as follows:

Δm(t)=nc[].Delta

Γf,m(t)=nc[].fref

Γb,m(t)=nc[].bref

fm(t)=nc[′].ferr

bm(t)=nc[].berr

m(t)=nc[].Fswsqr

βm(t)=nc[].Bswsqr

γm(t)=nc[].Gamma

ρm,λa(t)=nc[].Roh_a

ρm,λc(t)=nc[].Roh_b

em,λa(t)=nc[].err_a

em,λb(t)=nc[].err_b

κm,λa(t)=nc[].K_a

κm,λb(t)=nc[].K_b

First and second portions of the subroutine are the same as the first and second portions of the above described subroutine tailored for the ratiometric method of determining the noise reference signal n′(t).

A third portion of the subroutine calculates the noise reference signal, as in the “CALCULATE NOISE REFERENCE (n′(t)) FOR TWO MEASURED SIGNAL SAMPLES” box 140 for signals Sλa(t) and Sλb(t) using the a proportionality constant ωs4(t) determined by the constant saturation method as in equations (90) and (91). The saturation is calculated in a separate subroutine and a value of ωs4(t) is imported to the present subroutine for estimating the desired portions Yλa(t) and Yλb(t) of the composite measured signals Sλa(t) and Sλb(t).

Fourth, fifth, and sixth portions of the subroutine are similar to the fourth, fifth, and sixth portions of the above described program tailored for the ratiometric method. However, the signals being used to estimate the desired signal portions Yλa(t) and Yλb(t) in the present subroutine tailored for the constant saturation method, are Sλa(t) and Sλb(t), the same signals that were used to calculate the noise reference signal n′(t).

A seventh portion of the program, still within the loop begun in the fifth portion of the program, calculates the regression coefficient register 96 and 98 values κm,λa(t) and εm,λb(5) (nc[].K_a and nc[].K_b in the program) in both regression filters, as in the “ORDER UPDATE mth STAGE OF REGRESSION FILTER(S)” box 180 and equations (57) through (67). Intermediate error signals and variables εm,λa(t), em,λb(t), ρm,λa(t), and ρm,λb(t) (nc[].err_a and nc[].err_b, nc[].roh_a, and nc[].rob_b in the subroutine) are also calculated as in equations (58), (63), (56), and (59), respectively.

The loop iterates until the test for convergence is passed, the test being the same as described above for the subroutine tailored for the ratiometric method. The output of the present subroutine is a good approximation to the desired signals Y′λa(t) and Y′λb(t) for the set of samples Sλa(t) and Sλb(t) input to the program. After approximations to the desired signal portions of many sets of measured signal samples are estimated by the joint process estimator, a compilation of the outputs provides waves which are good approximations to the plethysmographic wave at each wavelength, λa and λb. The estimating process of the iterative loop is the same in either subroutine, only the sample values Sλa(t) and Sλc(t) or Sλa(t) and Sλb(t) input to the subroutine for use in estimation of the desired signal portions Yλa(t) and Yλc(t) or Yλa(t) and Yλb(t) and how the noise reference signal n′(t) is calculated are different for the ratiometric method and the constant saturation methods.

Independent of the method used, ratiometric or constant saturation, the approximations to the desired signal values Y′λa(t) and Y′λc(t) or Y′λa(t) and Y′λb(t) are input to a separate subroutine in which the saturation of oxygen in the arterial blood is calculated. If the constant saturation method is used, the saturation calculation subroutine also determines a value for the proportionality constant ωs4(t) as defined in equations (90) and (91) and discussed above. The concentration of oxygenated arterial blood can be found from the approximations to the desired signal values since the desired signals are made up to terms comprising x(t), the thickness of arterial blood in the finger; absorption coefficients of oxygenated and de-oxygenated hemoglobin, at each measured wavelength; and CHbO2(t) and CHB(t), the concentrations of oxygenated and de-oxygenated hemoglobin, respectively. The saturation is a ratio of the concentration of one constituent, A5, with respect to the total concentration of constituents in the volume containing A5 and A6. Thus, the thickness, x(t), is divided out of the saturation calculation and need not be predetermined. Additionally, the absorption coefficients are constant at each wavelength. The saturation of oxygenated arterial blood is then determined as in equations (95) and (96).

While one embodiment of a physiological monitor incorporating a processor of the present invention for determining a noise reference signal for use in an adaptive noise canceler to remove erratic noise components from a physiological measurement has been described in the form of a pulse oximeter, it will be obvious to one skilled in the art that other types of physiological monitors may also employ the above described techniques for noise reduction on a composite measured signal in the presence of noise.

Furthermore, it will be understood that transformations of measured signals other than logarithmic conversion and determination of a proportionality factor which allows removal of the desired signal portions for determination of a noise reference signal are possible. Additionally, although the proportionality factor ω has been described herein as a ratio of a portion of a first signal to a portion of a second signal, a similar proportionality constant determined as a ratio of a portion of a second signal to a portion of a first signal could equally well be utilized in the processor of the present invention. In the latter case, a noise reference signal would generally resemble n′(t)=nλb(t)−ωnλa(t).

It will also be obvious to one skilled in the art that for most physiological measurements, two wavelengths may be determined which will enable a signal to be measured which is indicative of a quantity of a component about which information is desired. Information about a constituent of any energy absorbing physiological material may be determined by a physiological monitor incorporating a signal processor of the present invention and an adaptive noise canceler by determining wavelengths which are absorbed primarily by the constituent of interest. For most physiological measurements, this is a simple determination.

Moreover, one skilled in the art will realize that any portion of a patient or a material derived from a patient may be used to take measurements for a physiological monitor incorporating a processor of the present invention and an adaptive noise canceler. Such areas include a digital such as a finger, but are not limited to a finger.

One skilled in the art will realize that many different types of physiological monitors may employ a signal processor of the present invention in conjunction with an adaptive noise canceler. Other types of physiological monitors include, but are not limited to, electron cardiographs, blood pressure monitors, blood gas saturation (other than oxygen saturation) monitors, capnographs, heart rate monitors, respiration monitors, or depth of anesthesia monitors. Additionally, monitors which measure the pressure and quantity of a substance within the body such as a breathalizer, a drug monitor, a cholesterol monitor, a glucose monitor, a carbon dioxide monitor, a glucose monitor, or a carbon monoxide monitor may also employ the above described techniques for removal of undesired signal portions.

Furthermore, one skilled in the art will realize that the above described techniques of noise removal from a composite signal including noise components can also be performed on signals made up of reflected energy, rather than transmitted energy. One skilled in the art will also realize that a desired portion of a measured signal of any type of energy, including but not limited to sound energy, X-ray energy, gamma ray energy, or light energy can be estimated by the noise removal techniques described above. Thus, one skilled in the art will realize that the processor of the present invention and an adaptive noise canceler can be applied in such monitors as those using ultrasound where a signal is transmitted through a portion of the body and reflected back from within the body back through this portion of the body. Additionally, monitors such as echo cardiographs may also utilize the techniques of the present invention since they too rely on transmission and reflection.

While the present invention has been described in terms of a physiological monitor, one skilled in the art will realize that the signal processing techniques of the present invention can be applied in many areas, including but not limited to the processing of a physiological signal. The present invention may be applied in any situation where a signal processor comprising a detector receives a first signal which includes a first desired signal portion and a first undesired signal portion and a second signal which includes a second desired signal portion and a second undesired signal portion. The first and second signals propagate through a common medium and the first and second desired signal portions are correlated with one another. Additionally, at least a portion of the first and second undesired signal portions are correlated with one another due to a perturbation of the medium while the first and second signals are propagating through the medium. The processor receives the first and second signals and combines the first and second signals to generate a noise reference signal in which the primary component is derived from the first and second undesired signal portions. Thus, the signal processor of the present invention is readily applicable to numerous signal processing areas.

/************************************************************
**********************APPENDIX A*************************
***********************Least Square Lattice*********************
*************************Noise Cancelling*********************
/* Example for ratiometric approach to noise cancelling */
#define LAMBDA 0.95
void OxiLSL_NC ( int reSet,
int passes,
int *signal_1,
int *signal_2,
int *signal_3,
int *target_1,
int *target_2,) {
int i, ii, k, m, n, contraction;
static int *s_a, *s_b, *s_c, *out_a, *out_c;
static float Delta_sqr, scale, noise_ref;
if( reset == TRUE) {
s_a = signal_1;
s_b = signal_2;
s_c = signal_3;
out_a = target_1;
out_c = target_2;
factor = 1.5;
scale = 1.0/4160.0;
/* noise canceller initialization at tiue t=0 */
nc[0].berr = 0.0;
nc[0].Gamma = 1.0;
for(m=0; m<NC_CELLS; m++) {
nc[m].err_a = 0.0;
nc[m].err_b = 0.0;
nc[m].Roh_a = 0.0;
nc[m].Roh_c = 0.0;
nc[m].Delta = 0.0;
nc[m].Fswsqr = 0.00001;
nc[m].Bswsqr = 0.00001;
}
}
/*================ END INITIALIZATION ================*/
For (k=0; k<passes; k++){
contraction = FALSE;
for(m=0; m<NC_CELLS; m++) { /* Update delay elements */
nc[m].berr1 = nc[m ].berr;
nc[m].Bswsqr1 = nc[m].Bswsqr;
}
noise_ref = factor * log(1.0 − (*s_a) * scale)
= log(1.0 − (*s_b) * scale) ;
nc[0].err_n = log(1.0 − (*s_b) * scale);
nc[0].err_b = log(1.0 − (*s_c) * scale);
++s_a;
++s_b;
++s_c;
nc[0].ferr = noise_ref ;
nc[0].berr = noise_ref ;
nc[0].Fswsqr = LAMBDA * nc[0].Fswsqr +
noise_ref * noise_ref;
nc[0].Bswsqr = nc[0].Fswsqr;
/* Order Update */
for(n=1;( n < NC_CELLS) && (contraction == FALSE); n++) {
/* Adaptive Lattice Section */
m = n − 1;
ii = n − 1;
nc[m].Delta *= LAMBDA;
nc[m].Delta += nc[m].berr1 * nc[m].ferr / nc[m].Gamma ;
Delta_sqr = nc[m].Delta * nc[m].Delta;
nc[n]fref = −nc[m].Delta / nc[m].Bswsqr1;
nc[n].bref = −nc[m].Delta / nc[m].Fswsqr;
nc[n].ferr = nc[m].ferr + nc[n].fref * nc[m].berr1;
nc[n].berr = nc[m].berr1 + nc[n].bref * nc[m].ferr;
nc[n].Fswsqr = nc[m].Fswsqr − Delta_sqr / nc[m].Bswsqr1;
nc[n].Bswsqr = nc[m].Bswsqr1 − Delta_sqr / nc[m].Fswsqr;
if( (nc[n].Fswsqr + nc[n].Bswsqr) > 0.00001 ∥ (n < 5) ) {
nc[n].Gamma = nc[m].Gamma − nc[m].berr1 *
 nc[m].berr1 / nc[m].Bswsqr1;
if(nc[n].Gamma < 0.05) nc[n].Gamma = 0.05;
if(nc[n].Gamma > 1.00) nc[n].Gamma = 1.00;
/* Joint Process Estimation Section */
nc[m].Roh_a *= LAMBDA;
nc[m].Roh_a += nc[m].berr * nc[m].err_a / nc[m].Gamma ;
nc[m].k_a  = nc[m].Roh_a / nc[m].Bswsqr;
nc[n].err_a  = nc[m].err_a − nc[m].k_a * nc[m].berr;
nc[m].Roh_c *= LAMBDA;
nc[m].Roh_c += nc[m].berr* nc[m].err_b / nc[m].Gamma;
nc[m].k_c  = nc[m].Roh_c / nc[m].Bswsqr;
nc[n].err_b  = nc[m].err_b − nc[m].k_c * nc[m].berr;
}
else {
contraction = TRUE;
for(i=n; i<NC_CELLS; i++) {
nc[i].err_a = 0.0;
nc[i].Roh_a = 0.0;
nc[i].err_b = 0.0;
nc[i].Roh_c = 0.0;
nc[i].Delta = 0.0;
nc[i].Fswsqr = 0.00001;
nc[i].Bswsqr = 0.00001;
nc[i].Bswsqr1 = 0.00001;
}
}
}
*out_a++ = (int) ((−exp(nc[ii].err_a) +1.0) / scale) ;
*out_c++ = (int) ((−exp(nc[ii].err_b) +1.0) / scale) ;
}
}
/******************* Least Square Lattice ***********************
*******************        ***********************
************************************************************/

/***********************************************************
********************** APPENDIX B *************************
******************** Least Square Lattice ***********************
************************Noise Cancelling ************************/
/* Example for constant saturation approach to noise cancelling */
#define LAMBDA 0.95
void OxilSL_NC ( int reset,
int passes,
int sat_factor,
int *signal_1,
int signal_2,
int target_1,
int target_2) {
int i, ii, k, m, n, contraction;
static int *s_a, *s_b, *s_c, *out_b;
static int Delta_sqr, scale, noise_ref;
if( reset == TRUE){
s_a = signal_1;
s_b = signal_2;
out_a = target_1;
out_b = target_2;
scale = 1.0/4160.0;
/*noise canceller initialization at time t=0 */
nc[0].berr = 0.0;
nc[0].Gamma = 1.0;
for(m=0; m<NC_CELLS; m++) {
nc[m].err_a = 0.0;
nc[m].err_b = 0.0;
nc[m].Roh_a = 0.0;
nc[m].Roh_b = 0.0;
nc[m].Delta = 0.0;
nc[m].Fswsqr = 0.00001;
nc[m].Bswsqr = 0.00001;
}
}
/*================ END INITIALIZATION ================*/
For (k=0; k<passes; k++) {
contraction = FALSE;
for(m=0; ms<NC_CELLS; m++) {  /*Update delay elements */
nc[m].berr1 = nc[m].berr;
nc[m].Bswsqr1 = nc[m].Bswsqr;
}
noise_ref = sat_factor * log(1.0 − (*s_a) * scale)
− log(1.0 − (*s_b) * scale) ;
nc[0].err_a = log(1.0 − (*s_a) * scale);
nc[0].err_b = log(1.0 − (*s_b) * scale);
++s_a;
++s_b;
nc[0].ferr = noise_ref ;
nc[0].berr = noise_ref ;
nc[0].Fswsqr = LAMBDA * nc[0].Fswsqr +
noise_ref * noise_ref;
nc[0].Bswsqr = nc[0].Fswsqr;
/* Order Update */
for(n=1;( n < NC_CELLS) &&
(contraction == FALSE); n++) {
/* Adaptive Lattice Section */
m = n−1;
ii = n−1;
nc[m].Delta *= LAMBDA;
nc[m].Delta += nc[m].berr1 * nc[m].ferr / nc[m].Gamma ;
Delta_sqr  = nc[m].Delta * nc[m].Delta;
nc[n].fref  = −nc[m].Delta / nc[m].Bswsqr1;
nc[n].bref  = −nc[m].Delta / nc[m].Fswsqr;
nc[n].ferr  = nc[m].ferr + nc[n].fref * nc[m].berr1;
nc[n].berr  = nc[m].berr1 + nc[n].bref * nc[m].ferr;
nc[n].Fswsqr  = nc[m].Fswsqr − Delta_sqr / nc[m].Bswsqr1;
nc[n].Bswsqr  = nc[m].Bswsqr1 − Delta_sqr / nc[m].Fswsqr;
if( (nc[n].Fswsqr + nc[n].Bswsqr) > 0.00001 ∥ (n < 5) ) {
nc[n].Gamma = nc[m].Gamma − nc[m].berr1 *
 nc[m].berr1 / nc[m].Bswsqr1;
if(nc[n].Gamma < 0.05) nc[n].Gamma = 0.05;
if(nc[n].Gamma > 1.00) nc[n].Gamma = 1.00;
/* Joint Process Estimation Section */
nc[m].Roh_a *= LAMBDA;
nc[m].Roh_a += nc[m]berr * nc[m].err_a / nc[m].Gamma ;
nc[m].k_a  = nc[m].Roh_a / nc[m].Bswsqr;
nc[m].err_a  = nc[m]err_a − nc[m].k_a * nc[m].berr;
nc[m].Roh_b *= LAMBDA;
nc[m].Roh_b += nc[m]berr * nc[m].err_b / nc[m].Gamma ;
nc[m].k_b  = nc[m].Roh_b / nc[m].Bswsqr;
nc[n].err_b  = nc[m].err_b − nc[m].k_b * nc[m].berr;
}
else {
contraction = TRUE;
for(i=n; i<NC_CELLS; i++) {
nc[i].err_a = 0.0;
nc[i].Roh_a = 0.0;
nc[i].err_b = 0.0;
nc[i].Roh_b = 0.0;
nc[i].Delta = 0.0;
nc[i].Fswsqr = 0.00001;
nc[i].Bswsqr = 0.00001;
nc[i].Bswsqr1 = 0.00001;
}
}
}
*out_a++ = (int) ((−exp(nc[ii].err_a) +1.0) /
scale) ;
*out_b++ = (int) ((−exp(nc[ii].err_b) +1.0) /
scale) ;
}
}
/******************* Least Square Lattice ***********************
*******************        ***********************
************************************************************/

Claims (44)

What is claimed is:
1. A method of determining an indication of blood oxygen saturation comprising the steps of:
transmitting light of at least first and second wavelengths through body tissue carrying blood to a light-sensitive detector to generate first and second measured intensity signals;
filtering at least one of said first and second intensity signals with an adaptive canceler to provide at least one output signal; and
calculating oxygen saturation based upon said at least one output signal.
2. The method of claim 1, wherein said step of transmitting light comprises the steps of transmitting a first wavelength in the red wavelength range and a second wavelength in the infrared wavelength range.
3. The method of claim 2, wherein said first and said second signals have at least a desired physiological component and an artifact component.
4. The method of claim 3, wherein said first and second wavelengths are selected based on light absorption characteristics of the physiologic medium, such that a substantially linear relationship exists between the desired physiologic components of said first and second measured signals.
5. The method of claim 1, further comprising the step of converting said first and second intensity signals to a digital representation of said first and second intensity signals, and wherein said step of filtering at least one of said first and second intensity signals with said adaptive canceler comprising filtering said digital representation.
6. The method of claim 1, further comprising the step of filtering said first and second signals with a predetermined filter prior to said filtering at least one of said first and second intensity signals with an adaptive canceler.
7. The method of claim 6, further comprising the step of converting said first and second intensity signals to a digital representation of said first and second intensity signals, and wherein said step of filtering at least one of said first and second intensity signals with an adaptive canceler comprises filtering said digital representation.
8. The method of claim 6, further comprising the step of displaying said oxygen saturation on a display.
9. The method of claim 1, further comprising the step of displaying said oxygen saturation on a display.
10. The method of claim 1, wherein said step of filtering at least one of said first and second intensity signals with an adaptive canceler comprises the steps of:
multiplying at least one of said first and second intensity signals by a predetermined constant and subtracting the result from the other of said first and second intensity signals to provide a reference signal; and
filtering at least one of said first and second intensity signals based upon said reference signal.
11. The method of claim 5, wherein said step of filtering at least one of said first and second intensity signals with an adaptive canceler comprises the steps of:
multiplying at least one of the digital representations of said first and second intensity signals by a predetermined constant and subtracting the result from the other of said first and second intensity signals to provide a reference signal; and
adaptively filtering at least one of the digital representations of said first and second intensity signals based upon said reference signal.
12. The method of claim 11, further comprising the step of filtering said first and second signals with a predetermined filter prior to filtering at least one of said first and second intensity signals with an adaptive canceler.
13. The method of claim 5, where said adaptive canceler is a dynamic multiple notch filter.
14. The method of claim 13, wherein said adaptive canceler adjusts its transfer function in accordance with a predetermined algorithm.
15. The method of claim 14, wherein said predetermined algorithm is a least-squares algorithm.
16. A pulse oximeter which measures the oxygen saturation of blood in body tissue, said pulse oximeter comprising:
a light emitter adapted to emit light of at least first and second wavelengths;
a light detector responsive to light from said light emitter which has passed through body tissue having blood, said light detector providing intensity signals;
an adaptive filter responsive to said intensity signals to provide at least one filtered signal; and
a oxygen saturation module responsive to at least said filtered signal to calculate oxygen saturation of said blood.
17. The pulse oximeter of claim 16, further comprising a display coupled to said oxygen saturation module.
18. The pulse oximeter of claim 16, wherein said adaptive filter comprises a first predetermined filter and adaptive correlation canceler an adaptive noise canceler.
19. The pulse oximeter of claim 16, further comprising an analog to digital converter in communication with said light detector, said analog to digital converter providing digital representations of said intensity signals, said analog to digital converter providing said digital representations to said adaptive filter.
20. The pulse oximeter of claim 19, further comprising a signal conditioner coupled between said light detector and said analog to digital converter.
21. The pulse oximeter of claim 16 18, wherein said adaptive filternoise canceler is coupled to comprises a multiplication unit and an adaptive correlation noise canceler .
22. The pulse oximeter of claim 21 wherein said adaptive filter noise canceler further comprises a predetermined filter.
23. The pulse oximeter of claim 16, wherein said adaptive filter is a dynamic multiple notch filter.
24. The pulse oximeter of claim 23, wherein said adaptive filter adjusts its transfer function in accordance with a predetermined algorithm.
25. The pulse oximeter of claim 24, wherein said predetermined algorithm is a least squares lattice.
26. A pulse oximeter which measures the oxygen saturation of blood in body tissue, said pulse oximeter comprising:
a light emitter adapted to emit light of at least first and second wavelengths;
a light detector responsive to light from said light emitter which has passed through body tissue having blood, said light detector providing intensity signals;
a digital to analog converter which digitizes the intensity signals from said light detector;
a multiple notch filter responsive to said intensity signals after conversion with said digital to analog converter to provide at least one filtered signal; and
an oxygen saturation module responsive to at least said filtered signal to calculate oxygen saturation of said blood.
27. The method of claim 26, wherein said multiple notch filter adjusts its transfer function based on a predetermined algorithm.
28. A pulse oximeter which measures the oxygen saturation of blood in body tissue, said pulse oximeter comprising:
a light emitter adapted to emit light of at least first and second wavelengths;
a light detector responsive to light from said light emitter which has passed through body tissue having blood, said light detector providing intensity signals having desired and undesired signal portions;
an analog to digital converter which digitizes the intensity signals from said light detector;
an adaptive signal processor responsive to that operates to process said intensity signals to adaptively filter said intensity signals to provide processed intensity signals; and
an oxygen saturation module routine responsive to at least one signal said processed intensity signals to calculate oxygen saturation of said blood.
29. The pulse oximeter of claim 28, wherein said adaptive signal processor comprises an adaptive noise canceler.
30. The pulse oximeter of claim 29, wherein said adaptive noise canceler comprises a joint process estimator with a least-squares lattice predictor.
31. The pulse oximeter of claim 29, wherein said adaptive noise canceler operates as a multiple notch filter.
32. The pulse oximeter of claim 28, wherein said adaptive signal processor comprises an adaptive filter.
33. A method for determining oxygen saturation of blood in body tissue, the method comprising the steps of:
emitting light of at least first and second wavelengths;
detecting the light of at least first and second wavelengths that has passed through body tissue including pulsing blood to produce at least one intensity signal;
processing a representation of the at least one intensity signal using an adaptive algorithm to provide at least one output signal; and
calculating an oxygen saturation of the pulsing blood using the at least one output signal.
34. The method of claim 33, wherein the representation of the at least one intensity signal is a digital representation.
35. The method of claim 33, further comprising the step of displaying the calculated oxygen saturation of the blood.
36. The method of claim 33, wherein the step of processing comprises processing the representation using a component of an adaptive noise canceler.
37. The method of claim 33, wherein the adaptive algorithm is a least squares algorithm.
38. The method of claim 33, wherein the adaptive algorithm is a least mean square algorithm.
39. A pulse oximeter comprising:
light emitters that emit light of at least first and second wavelengths;
a light detector responsive to the light from the light emitters that has passed through body tissue including pulsing blood, the light detector providing at least one intensity signal;
an analog to digital converter that provides a digital representation of the at least one intensity signal;
a processor including a first routine to adaptively process the digital representation of the at least one intensity signal to provide at least one output signal; and
a second routine responsive to the at least one output signal to calculate oxygen saturation of the blood.
40. The pulse oximeter of claim 39, wherein the first routine comprises an adaptive algorithm.
41. The pulse oximeter of claim 40, wherein the adaptive algorithm comprises a least squares algorithm.
42. The pulse oximeter of claim 40, wherein the adaptive algorithm comprises a least mean square algorithm.
43. The pulse oximeter of claim 41, wherein the adaptive algorithm comprises a least squares lattice algorithm.
44. The pulse oximeter of claim 39, wherein the processor comprises a component of an adaptive noise canceler.
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Cited By (301)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20020140675A1 (en) * 1999-01-25 2002-10-03 Ali Ammar Al System and method for altering a display mode based on a gravity-responsive sensor
US20030055325A1 (en) * 2001-06-29 2003-03-20 Weber Walter M. Signal component processor
US20030167391A1 (en) * 2002-03-01 2003-09-04 Ammar Al-Ali Encryption interface cable
US20030212312A1 (en) * 2002-01-07 2003-11-13 Coffin James P. Low noise patient cable
US20030220576A1 (en) * 2002-02-22 2003-11-27 Diab Mohamed K. Pulse and active pulse spectraphotometry
US20030218386A1 (en) * 2002-01-25 2003-11-27 David Dalke Power supply rail controller
US20040039272A1 (en) * 2002-08-01 2004-02-26 Yassir Abdul-Hafiz Low noise optical housing
US20040068164A1 (en) * 1991-03-07 2004-04-08 Diab Mohamed K. Signal processing apparatus
US20040107065A1 (en) * 2002-11-22 2004-06-03 Ammar Al-Ali Blood parameter measurement system
US20040122301A1 (en) * 2002-09-25 2004-06-24 Kiani Massl E. Parameter compensated pulse oximeter
US20040133088A1 (en) * 1999-12-09 2004-07-08 Ammar Al-Ali Resposable pulse oximetry sensor
US20040133087A1 (en) * 1999-01-07 2004-07-08 Ali Ammar Al Pulse oximetry data confidence indicator
US20040147822A1 (en) * 2003-01-24 2004-07-29 Ammar Al-Ali Optical sensor including disposable and reusable elements
US20040147824A1 (en) * 1995-06-07 2004-07-29 Diab Mohamed Kheir Manual and automatic probe calibration
US20040181133A1 (en) * 2001-07-02 2004-09-16 Ammar Al-Ali Low power pulse oximeter
US20040204638A1 (en) * 1991-03-07 2004-10-14 Diab Mohamed Kheir Signal processing apparatus and method
US20040204637A1 (en) * 1997-04-14 2004-10-14 Diab Mohamed K. Signal processing apparatus and method
US6822564B2 (en) 2002-01-24 2004-11-23 Masimo Corporation Parallel measurement alarm processor
US20040242980A1 (en) * 2002-09-25 2004-12-02 Kiani Massi E. Parameter compensated physiological monitor
US6842635B1 (en) * 1998-08-13 2005-01-11 Edwards Lifesciences Llc Optical device
US20050020893A1 (en) * 2000-08-18 2005-01-27 Diab Mohamed K. Optical spectroscopy pathlength measurement system
US6850788B2 (en) 2002-03-25 2005-02-01 Masimo Corporation Physiological measurement communications adapter
US20050043600A1 (en) * 1991-03-21 2005-02-24 Mohamed Diab Low-noise optical probes for reducing ambient noise
US20050055276A1 (en) * 2003-06-26 2005-03-10 Kiani Massi E. Sensor incentive method
US20050075548A1 (en) * 2003-07-25 2005-04-07 Ammar Al-Ali Multipurpose sensor port
US20050085735A1 (en) * 1995-08-07 2005-04-21 Nellcor Incorporated, A Delaware Corporation Method and apparatus for estimating a physiological parameter
US20050085704A1 (en) * 2003-10-14 2005-04-21 Christian Schulz Variable pressure reusable sensor
US20050085702A1 (en) * 1998-12-30 2005-04-21 Diab Mohamed K. Plethysmograph pulse recognition processor
US20050090724A1 (en) * 2003-08-28 2005-04-28 Ammar Al-Ali Physiological parameter tracking system
US20050101848A1 (en) * 2003-11-05 2005-05-12 Ammar Al-Ali Pulse oximeter access apparatus and method
US20050101849A1 (en) * 2003-11-07 2005-05-12 Ammar Al-Ali Pulse oximetry data capture system
US20050143631A1 (en) * 1999-08-26 2005-06-30 Ammar Al-Ali Systems and methods for indicating an amount of use of a sensor
US6934570B2 (en) 2002-01-08 2005-08-23 Masimo Corporation Physiological sensor combination
US20050187440A1 (en) * 2004-02-20 2005-08-25 Yassir Abdul-Hafiz Connector switch
US20050197551A1 (en) * 1998-06-03 2005-09-08 Ammar Al-Ali Stereo pulse oximeter
US20050197550A1 (en) * 2004-01-05 2005-09-08 Ammar Al-Ali Pulse oximetry sensor
US20050197555A1 (en) * 2004-03-06 2005-09-08 Calisto Medical, Inc. Methods and devices for non-invasively measuring quantitative information of substances in living organisms
US20050203352A1 (en) * 2004-03-08 2005-09-15 Ammar Al-Ali Physiological parameter system
US6950687B2 (en) 1999-12-09 2005-09-27 Masimo Corporation Isolation and communication element for a resposable pulse oximetry sensor
US6970792B1 (en) 2002-12-04 2005-11-29 Masimo Laboratories, Inc. Systems and methods for determining blood oxygen saturation values using complex number encoding
US20060004293A1 (en) * 1996-06-26 2006-01-05 Flaherty Bryan P Rapid non-invasive blood pressure measuring device
US6985764B2 (en) 2001-05-03 2006-01-10 Masimo Corporation Flex circuit shielded optical sensor
US20060009687A1 (en) * 2004-03-31 2006-01-12 Claudio De Felice Physiological assessment system
US20060020185A1 (en) * 2004-07-09 2006-01-26 Ammar Al-Ali Cyanotic infant sensor
US6999904B2 (en) 2000-06-05 2006-02-14 Masimo Corporation Variable indication estimator
US7003338B2 (en) 2003-07-08 2006-02-21 Masimo Corporation Method and apparatus for reducing coupling between signals
US20060058691A1 (en) * 2004-09-07 2006-03-16 Kiani Massi E Noninvasive hypovolemia monitor
US20060073719A1 (en) * 2004-09-29 2006-04-06 Kiani Massi E Multiple key position plug
US20060189871A1 (en) * 2005-02-18 2006-08-24 Ammar Al-Ali Portable patient monitor
US20060258922A1 (en) * 2005-03-21 2006-11-16 Eugene Mason Variable aperture sensor
US20070007612A1 (en) * 1998-03-10 2007-01-11 Mills Michael A Method of providing an optoelectronic element with a non-protruding lens
US20070032715A1 (en) * 2005-08-08 2007-02-08 Darius Eghbal Compliant diaphragm medical sensor and technique for using the same
US20070032712A1 (en) * 2005-08-08 2007-02-08 William Raridan Unitary medical sensor assembly and technique for using the same
US7184809B1 (en) 2005-11-08 2007-02-27 Woolsthorpe Technologies, Llc Pulse amplitude indexing method and apparatus
US20070073116A1 (en) * 2005-08-17 2007-03-29 Kiani Massi E Patient identification using physiological sensor
US7215987B1 (en) 2005-11-08 2007-05-08 Woolsthorpe Technologies Method and apparatus for processing signals reflecting physiological characteristics
US7225006B2 (en) 2003-01-23 2007-05-29 Masimo Corporation Attachment and optical probe
US20070123763A1 (en) * 2005-11-29 2007-05-31 Ammar Al-Ali Optical sensor including disposable and reusable elements
US20070180140A1 (en) * 2005-12-03 2007-08-02 Welch James P Physiological alarm notification system
US20070188495A1 (en) * 2006-01-03 2007-08-16 Kiani Massi E Virtual display
US7269537B1 (en) 2005-02-26 2007-09-11 Duane Loren Mattern Infrasound sensor with disturbance filtering
US20070219437A1 (en) * 2006-03-17 2007-09-20 Glucolight Corporation System and method for creating a stable optical interface
US20070244377A1 (en) * 2006-03-14 2007-10-18 Cozad Jenny L Pulse oximeter sleeve
US20080021293A1 (en) * 2004-08-11 2008-01-24 Glucolight Corporation Method and apparatus for monitoring glucose levels in a biological tissue
US20080064965A1 (en) * 2006-09-08 2008-03-13 Jay Gregory D Devices and methods for measuring pulsus paradoxus
US20080071155A1 (en) * 2006-09-20 2008-03-20 Kiani Massi E Congenital heart disease monitor
US20080071153A1 (en) * 2006-09-20 2008-03-20 Ammar Al-Ali Duo connector patient cable
US20080081325A1 (en) * 2006-09-29 2008-04-03 Nellcor Puritan Bennett Inc. Modulation ratio determination with accommodation of uncertainty
US20080094228A1 (en) * 2006-10-12 2008-04-24 Welch James P Patient monitor using radio frequency identification tags
US20080103375A1 (en) * 2006-09-22 2008-05-01 Kiani Massi E Patient monitor user interface
US20080132771A1 (en) * 1998-07-04 2008-06-05 Whitland Research Limited Measurement of blood oxygen saturation
US20080154104A1 (en) * 2004-07-07 2008-06-26 Masimo Laboratories, Inc. Multi-Wavelength Physiological Monitor
US20080188760A1 (en) * 2006-12-09 2008-08-07 Ammar Al-Ali Plethysmograph variability processor
US20080197301A1 (en) * 2006-12-22 2008-08-21 Diab Mohamed K Detector shield
US20080221464A1 (en) * 2007-01-20 2008-09-11 Ammar Al-Ali Perfusion trend indicator
US20080228052A1 (en) * 2002-01-24 2008-09-18 Ammar Al-Ali Physiological trend monitor
US20080255435A1 (en) * 2007-04-16 2008-10-16 Masimo Corporation Low noise oximetry cable including conductive cords
US7438683B2 (en) 2004-03-04 2008-10-21 Masimo Corporation Application identification sensor
US20090030330A1 (en) * 2007-06-28 2009-01-29 Kiani Massi E Disposable active pulse sensor
US20090093687A1 (en) * 2007-03-08 2009-04-09 Telfort Valery G Systems and methods for determining a physiological condition using an acoustic monitor
US20090099430A1 (en) * 1991-03-07 2009-04-16 Masimo Corporation Signal processing apparatus
US20090099423A1 (en) * 2007-10-12 2009-04-16 Ammar Al-Ali Connector assembly
US20090112073A1 (en) * 1999-03-25 2009-04-30 Diab Mohamed K Pulse oximeter probe-off detector
US20090149764A1 (en) * 2007-02-28 2009-06-11 Semler Herbert J Circulation monitoring system and method
US20090156913A1 (en) * 2007-10-12 2009-06-18 Macneish Iii William Jack Ceramic emitter substrate
US20090171173A1 (en) * 2007-12-31 2009-07-02 Nellcor Puritan Bennett Llc System and method for reducing motion artifacts in a sensor
US20090171171A1 (en) * 2007-12-31 2009-07-02 Nellcor Puritan Bennett Llc Oximetry sensor overmolding location features
US20090182211A1 (en) * 1994-10-07 2009-07-16 Masimo Corporation Signal processing apparatus
US20090275809A1 (en) * 2008-05-01 2009-11-05 Starr Life Sciences Corp. Portable Modular Kiosk Based Physiologic Sensor System with Display and Data Storage for Clinical and Research Applications including Cross Calculating and Cross Checked Physiologic Parameters Based Upon Combined Sensor Input
US20090275810A1 (en) * 2008-05-01 2009-11-05 Starr Life Sciences Corp. Portable modular pc based system for continuous monitoring of blood oxygenation and respiratory parameters
US20090275844A1 (en) * 2008-05-02 2009-11-05 Masimo Corporation Monitor configuration system
US20090299157A1 (en) * 2008-05-05 2009-12-03 Masimo Corporation Pulse oximetry system with electrical decoupling circuitry
US7647083B2 (en) 2005-03-01 2010-01-12 Masimo Laboratories, Inc. Multiple wavelength sensor equalization
US7650177B2 (en) 2005-09-29 2010-01-19 Nellcor Puritan Bennett Llc Medical sensor for reducing motion artifacts and technique for using the same
US20100014723A1 (en) * 2008-07-15 2010-01-21 Nellcor Puritan Bennett Ireland Signal processing systems and methods using multiple signals
US7657295B2 (en) 2005-08-08 2010-02-02 Nellcor Puritan Bennett Llc Medical sensor and technique for using the same
USD609193S1 (en) 2007-10-12 2010-02-02 Masimo Corporation Connector assembly
US20100026995A1 (en) * 2008-08-04 2010-02-04 Masimo Laboratories, Inc. Multi-stream sensor for noninvasive measurement of blood constituents
US7658652B2 (en) 2006-09-29 2010-02-09 Nellcor Puritan Bennett Llc Device and method for reducing crosstalk
US7680522B2 (en) 2006-09-29 2010-03-16 Nellcor Puritan Bennett Llc Method and apparatus for detecting misapplied sensors
US7684842B2 (en) 2006-09-29 2010-03-23 Nellcor Puritan Bennett Llc System and method for preventing sensor misuse
US7689259B2 (en) 2000-04-17 2010-03-30 Nellcor Puritan Bennett Llc Pulse oximeter sensor with piece-wise function
US20100094107A1 (en) * 2008-10-13 2010-04-15 Masimo Corporation Reflection-detector sensor position indicator
USD614305S1 (en) 2008-02-29 2010-04-20 Masimo Corporation Connector assembly
USRE41317E1 (en) 1998-10-15 2010-05-04 Masimo Corporation Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices
US7720516B2 (en) 1996-10-10 2010-05-18 Nellcor Puritan Bennett Llc Motion compatible sensor for non-invasive optical blood analysis
US7729736B2 (en) 2005-09-29 2010-06-01 Nellcor Puritan Bennett Llc Medical sensor and technique for using the same
USD621516S1 (en) 2008-08-25 2010-08-10 Masimo Laboratories, Inc. Patient monitoring sensor
US7796403B2 (en) 2006-09-28 2010-09-14 Nellcor Puritan Bennett Llc Means for mechanical registration and mechanical-electrical coupling of a faraday shield to a photodetector and an electrical circuit
US20100234718A1 (en) * 2009-03-12 2010-09-16 Anand Sampath Open architecture medical communication system
US7822452B2 (en) 2004-08-11 2010-10-26 Glt Acquisition Corp. Method for data reduction and calibration of an OCT-based blood glucose monitor
USRE41912E1 (en) 1998-10-15 2010-11-02 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatus
US20100298675A1 (en) * 2009-05-20 2010-11-25 Ammar Al-Ali Hemoglobin Display and Patient Treatment
US20100331639A1 (en) * 2009-06-30 2010-12-30 O'reilly Michael Pulse Oximetry System for Adjusting Medical Ventilation
US20110004069A1 (en) * 2009-07-06 2011-01-06 Nellcor Puritan Bennett Ireland Systems And Methods For Processing Physiological Signals In Wavelet Space
US20110021892A1 (en) * 2009-07-23 2011-01-27 Nellcor Puritan Bennett Ireland Systems and methods for respiration monitoring
US20110021941A1 (en) * 2009-07-23 2011-01-27 Nellcor Puritan Bennett Ireland Systems and methods for respiration monitoring
US7880626B2 (en) 2006-10-12 2011-02-01 Masimo Corporation System and method for monitoring the life of a physiological sensor
US7880884B2 (en) 2008-06-30 2011-02-01 Nellcor Puritan Bennett Llc System and method for coating and shielding electronic sensor components
US20110028806A1 (en) * 2009-07-29 2011-02-03 Sean Merritt Reflectance calibration of fluorescence-based glucose measurements
US20110026784A1 (en) * 2009-07-30 2011-02-03 Nellcor Puritan Bennett Ireland Systems And Methods For Determining Physiological Information Using Selective Transform Data
US20110028809A1 (en) * 2009-07-29 2011-02-03 Masimo Corporation Patient monitor ambient display device
US7890153B2 (en) 2006-09-28 2011-02-15 Nellcor Puritan Bennett Llc System and method for mitigating interference in pulse oximetry
US7887345B2 (en) 2008-06-30 2011-02-15 Nellcor Puritan Bennett Llc Single use connector for pulse oximetry sensors
US20110040197A1 (en) * 2009-07-20 2011-02-17 Masimo Corporation Wireless patient monitoring system
US7894869B2 (en) 2007-03-09 2011-02-22 Nellcor Puritan Bennett Llc Multiple configuration medical sensor and technique for using the same
US20110077484A1 (en) * 2009-09-30 2011-03-31 Nellcor Puritan Bennett Ireland Systems And Methods For Identifying Non-Corrupted Signal Segments For Use In Determining Physiological Parameters
US20110087083A1 (en) * 2009-09-17 2011-04-14 Jeroen Poeze Analyte monitoring using one or more accelerometers
US7941199B2 (en) 2006-05-15 2011-05-10 Masimo Laboratories, Inc. Sepsis monitor
US20110109459A1 (en) * 2009-07-24 2011-05-12 Masimo Laboratories, Inc. Interference detector for patient monitor
US7962188B2 (en) 2005-10-14 2011-06-14 Masimo Corporation Robust alarm system
US20110169644A1 (en) * 2008-10-10 2011-07-14 Bilal Muhsin Systems and methods for storing, analyzing, retrieving and displaying streaming medical data
US20110208015A1 (en) * 2009-07-20 2011-08-25 Masimo Corporation Wireless patient monitoring system
US20110213212A1 (en) * 2010-03-01 2011-09-01 Masimo Corporation Adaptive alarm system
US20110213273A1 (en) * 2009-10-15 2011-09-01 Telfort Valery G Acoustic respiratory monitoring sensor having multiple sensing elements
US20110213226A1 (en) * 2010-02-28 2011-09-01 Nellcor Puritan Bennett Llc Motion compensation in a sensor
US20110218816A1 (en) * 2009-09-14 2011-09-08 Masimo Laboratories, Inc. Spot check monitor credit system
US20110230733A1 (en) * 2010-01-19 2011-09-22 Masimo Corporation Wellness analysis system
USRE42753E1 (en) 1995-06-07 2011-09-27 Masimo Laboratories, Inc. Active pulse blood constituent monitoring
US20110237911A1 (en) * 2004-07-07 2011-09-29 Masimo Laboratories, Inc. Multiple-wavelength physiological monitor
US8028701B2 (en) 2006-05-31 2011-10-04 Masimo Corporation Respiratory monitoring
US8036727B2 (en) 2004-08-11 2011-10-11 Glt Acquisition Corp. Methods for noninvasively measuring analyte levels in a subject
US8048040B2 (en) 2007-09-13 2011-11-01 Masimo Corporation Fluid titration system
US8062221B2 (en) 2005-09-30 2011-11-22 Nellcor Puritan Bennett Llc Sensor for tissue gas detection and technique for using the same
US8068891B2 (en) 2006-09-29 2011-11-29 Nellcor Puritan Bennett Llc Symmetric LED array for pulse oximetry
US8071935B2 (en) * 2008-06-30 2011-12-06 Nellcor Puritan Bennett Llc Optical detector with an overmolded faraday shield
US8073518B2 (en) 2006-05-02 2011-12-06 Nellcor Puritan Bennett Llc Clip-style medical sensor and technique for using the same
US8070508B2 (en) 2007-12-31 2011-12-06 Nellcor Puritan Bennett Llc Method and apparatus for aligning and securing a cable strain relief
US8092379B2 (en) 2005-09-29 2012-01-10 Nellcor Puritan Bennett Llc Method and system for determining when to reposition a physiological sensor
US8092993B2 (en) 2007-12-31 2012-01-10 Nellcor Puritan Bennett Llc Hydrogel thin film for use as a biosensor
US8116841B2 (en) 2007-09-14 2012-02-14 Corventis, Inc. Adherent device with multiple physiological sensors
US8133176B2 (en) 1999-04-14 2012-03-13 Tyco Healthcare Group Lp Method and circuit for indicating quality and accuracy of physiological measurements
US8145288B2 (en) 2006-08-22 2012-03-27 Nellcor Puritan Bennett Llc Medical sensor for reducing signal artifacts and technique for using the same
US8175667B2 (en) 2006-09-29 2012-05-08 Nellcor Puritan Bennett Llc Symmetric LED array for pulse oximetry
US8175671B2 (en) 2006-09-22 2012-05-08 Nellcor Puritan Bennett Llc Medical sensor for reducing signal artifacts and technique for using the same
US8175672B2 (en) 1999-04-12 2012-05-08 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatii
US8182443B1 (en) 2006-01-17 2012-05-22 Masimo Corporation Drug administration controller
US8190224B2 (en) 2006-09-22 2012-05-29 Nellcor Puritan Bennett Llc Medical sensor for reducing signal artifacts and technique for using the same
US8199007B2 (en) 2007-12-31 2012-06-12 Nellcor Puritan Bennett Llc Flex circuit snap track for a biometric sensor
US8203438B2 (en) 2008-07-29 2012-06-19 Masimo Corporation Alarm suspend system
US8216136B2 (en) 2009-03-05 2012-07-10 Nellcor Puritan Bennett Llc Systems and methods for monitoring heart rate and blood pressure correlation
US8219170B2 (en) 2006-09-20 2012-07-10 Nellcor Puritan Bennett Llc System and method for practicing spectrophotometry using light emitting nanostructure devices
US8221319B2 (en) 2009-03-25 2012-07-17 Nellcor Puritan Bennett Llc Medical device for assessing intravascular blood volume and technique for using the same
US8224412B2 (en) 2000-04-17 2012-07-17 Nellcor Puritan Bennett Llc Pulse oximeter sensor with piece-wise function
US8233954B2 (en) 2005-09-30 2012-07-31 Nellcor Puritan Bennett Llc Mucosal sensor for the assessment of tissue and blood constituents and technique for using the same
US8249686B2 (en) 2007-09-14 2012-08-21 Corventis, Inc. Adherent device for sleep disordered breathing
US8255026B1 (en) 2006-10-12 2012-08-28 Masimo Corporation, Inc. Patient monitor capable of monitoring the quality of attached probes and accessories
US8255029B2 (en) 2003-02-27 2012-08-28 Nellcor Puritan Bennett Llc Method of analyzing and processing signals
US8260391B2 (en) 2005-09-12 2012-09-04 Nellcor Puritan Bennett Llc Medical sensor for reducing motion artifacts and technique for using the same
US8265723B1 (en) 2006-10-12 2012-09-11 Cercacor Laboratories, Inc. Oximeter probe off indicator defining probe off space
US8265724B2 (en) 2007-03-09 2012-09-11 Nellcor Puritan Bennett Llc Cancellation of light shunting
US8274360B2 (en) 2007-10-12 2012-09-25 Masimo Corporation Systems and methods for storing, analyzing, and retrieving medical data
US8280469B2 (en) 2007-03-09 2012-10-02 Nellcor Puritan Bennett Llc Method for detection of aberrant tissue spectra
US8280473B2 (en) 2006-10-12 2012-10-02 Masino Corporation, Inc. Perfusion index smoother
US8311601B2 (en) 2009-06-30 2012-11-13 Nellcor Puritan Bennett Llc Reflectance and/or transmissive pulse oximeter
US8315685B2 (en) 2006-09-27 2012-11-20 Nellcor Puritan Bennett Llc Flexible medical sensor enclosure
US8346328B2 (en) 2007-12-21 2013-01-01 Covidien Lp Medical sensor and technique for using the same
US8352004B2 (en) 2007-12-21 2013-01-08 Covidien Lp Medical sensor and technique for using the same
US8352009B2 (en) 2005-09-30 2013-01-08 Covidien Lp Medical sensor and technique for using the same
US8352010B2 (en) 2005-09-30 2013-01-08 Covidien Lp Folding medical sensor and technique for using the same
US8364220B2 (en) 2008-09-25 2013-01-29 Covidien Lp Medical sensor and technique for using the same
US8366613B2 (en) 2007-12-26 2013-02-05 Covidien Lp LED drive circuit for pulse oximetry and method for using same
US8374665B2 (en) 2007-04-21 2013-02-12 Cercacor Laboratories, Inc. Tissue profile wellness monitor
US8374688B2 (en) 2007-09-14 2013-02-12 Corventis, Inc. System and methods for wireless body fluid monitoring
US8386002B2 (en) 2005-09-30 2013-02-26 Covidien Lp Optically aligned pulse oximetry sensor and technique for using the same
US8391941B2 (en) 2009-07-17 2013-03-05 Covidien Lp System and method for memory switching for multiple configuration medical sensor
US8396527B2 (en) 2006-09-22 2013-03-12 Covidien Lp Medical sensor for reducing signal artifacts and technique for using the same
US8401602B2 (en) 2008-10-13 2013-03-19 Masimo Corporation Secondary-emitter sensor position indicator
US8412317B2 (en) 2008-04-18 2013-04-02 Corventis, Inc. Method and apparatus to measure bioelectric impedance of patient tissue
US8417309B2 (en) 2008-09-30 2013-04-09 Covidien Lp Medical sensor
US8417310B2 (en) 2009-08-10 2013-04-09 Covidien Lp Digital switching in multi-site sensor
US8423112B2 (en) 2008-09-30 2013-04-16 Covidien Lp Medical sensor and technique for using the same
US8418524B2 (en) 2009-06-12 2013-04-16 Masimo Corporation Non-invasive sensor calibration device
US8428675B2 (en) 2009-08-19 2013-04-23 Covidien Lp Nanofiber adhesives used in medical devices
US8430817B1 (en) 2009-10-15 2013-04-30 Masimo Corporation System for determining confidence in respiratory rate measurements
US8437822B2 (en) 2008-03-28 2013-05-07 Covidien Lp System and method for estimating blood analyte concentration
US8437825B2 (en) 2008-07-03 2013-05-07 Cercacor Laboratories, Inc. Contoured protrusion for improving spectroscopic measurement of blood constituents
US8442608B2 (en) 2007-12-28 2013-05-14 Covidien Lp System and method for estimating physiological parameters by deconvolving artifacts
US8452366B2 (en) 2009-03-16 2013-05-28 Covidien Lp Medical monitoring device with flexible circuitry
US8452364B2 (en) 2007-12-28 2013-05-28 Covidien LLP System and method for attaching a sensor to a patient's skin
US8460189B2 (en) 2007-09-14 2013-06-11 Corventis, Inc. Adherent cardiac monitor with advanced sensing capabilities
US8473020B2 (en) 2009-07-29 2013-06-25 Cercacor Laboratories, Inc. Non-invasive physiological sensor cover
US8478538B2 (en) 2009-05-07 2013-07-02 Nellcor Puritan Bennett Ireland Selection of signal regions for parameter extraction
US8483790B2 (en) 2002-10-18 2013-07-09 Covidien Lp Non-adhesive oximeter sensor for sensitive skin
US8494786B2 (en) 2009-07-30 2013-07-23 Covidien Lp Exponential sampling of red and infrared signals
US8505821B2 (en) 2009-06-30 2013-08-13 Covidien Lp System and method for providing sensor quality assurance
US8509869B2 (en) 2009-05-15 2013-08-13 Covidien Lp Method and apparatus for detecting and analyzing variations in a physiologic parameter
US8532932B2 (en) 2008-06-30 2013-09-10 Nellcor Puritan Bennett Ireland Consistent signal selection by signal segment selection techniques
US8532727B2 (en) 1999-01-25 2013-09-10 Masimo Corporation Dual-mode pulse oximeter
US8560034B1 (en) 1993-10-06 2013-10-15 Masimo Corporation Signal processing apparatus
US8571618B1 (en) 2009-09-28 2013-10-29 Cercacor Laboratories, Inc. Adaptive calibration system for spectrophotometric measurements
US8571617B2 (en) 2008-03-04 2013-10-29 Glt Acquisition Corp. Flowometry in optical coherence tomography for analyte level estimation
US8577434B2 (en) 2007-12-27 2013-11-05 Covidien Lp Coaxial LED light sources
US8588880B2 (en) 2009-02-16 2013-11-19 Masimo Corporation Ear sensor
US8584345B2 (en) 2010-03-08 2013-11-19 Masimo Corporation Reprocessing of a physiological sensor
US8600467B2 (en) 2006-11-29 2013-12-03 Cercacor Laboratories, Inc. Optical sensor including disposable and reusable elements
US8634891B2 (en) 2009-05-20 2014-01-21 Covidien Lp Method and system for self regulation of sensor component contact pressure
US8641631B2 (en) 2004-04-08 2014-02-04 Masimo Corporation Non-invasive monitoring of respiratory rate, heart rate and apnea
US8666468B1 (en) 2010-05-06 2014-03-04 Masimo Corporation Patient monitor for determining microcirculation state
US8684925B2 (en) 2007-09-14 2014-04-01 Corventis, Inc. Injectable device for physiological monitoring
US8688183B2 (en) 2009-09-03 2014-04-01 Ceracor Laboratories, Inc. Emitter driver for noninvasive patient monitor
US8712494B1 (en) 2010-05-03 2014-04-29 Masimo Corporation Reflective non-invasive sensor
US8718752B2 (en) 2008-03-12 2014-05-06 Corventis, Inc. Heart failure decompensation prediction based on cardiac rhythm
US8718737B2 (en) 1997-04-14 2014-05-06 Masimo Corporation Method and apparatus for demodulating signals in a pulse oximetry system
US8723677B1 (en) 2010-10-20 2014-05-13 Masimo Corporation Patient safety system with automatically adjusting bed
US8740792B1 (en) 2010-07-12 2014-06-03 Masimo Corporation Patient monitor capable of accounting for environmental conditions
US8755872B1 (en) 2011-07-28 2014-06-17 Masimo Corporation Patient monitoring system for indicating an abnormal condition
US8771204B2 (en) 2008-12-30 2014-07-08 Masimo Corporation Acoustic sensor assembly
US8781544B2 (en) 2007-03-27 2014-07-15 Cercacor Laboratories, Inc. Multiple wavelength optical sensor
US8790259B2 (en) 2009-10-22 2014-07-29 Corventis, Inc. Method and apparatus for remote detection and monitoring of functional chronotropic incompetence
US8801613B2 (en) 2009-12-04 2014-08-12 Masimo Corporation Calibration for multi-stage physiological monitors
US8821415B2 (en) 2009-10-15 2014-09-02 Masimo Corporation Physiological acoustic monitoring system
US8821397B2 (en) 2010-09-28 2014-09-02 Masimo Corporation Depth of consciousness monitor including oximeter
US8830449B1 (en) 2011-04-18 2014-09-09 Cercacor Laboratories, Inc. Blood analysis system
US8840549B2 (en) 2006-09-22 2014-09-23 Masimo Corporation Modular patient monitor
US8852094B2 (en) 2006-12-22 2014-10-07 Masimo Corporation Physiological parameter system
US8870792B2 (en) 2009-10-15 2014-10-28 Masimo Corporation Physiological acoustic monitoring system
US8897847B2 (en) 2009-03-23 2014-11-25 Masimo Corporation Digit gauge for noninvasive optical sensor
US8897850B2 (en) 2007-12-31 2014-11-25 Covidien Lp Sensor with integrated living hinge and spring
US8897868B2 (en) 2007-09-14 2014-11-25 Medtronic, Inc. Medical device automatic start-up upon contact to patient tissue
US8911377B2 (en) 2008-09-15 2014-12-16 Masimo Corporation Patient monitor including multi-parameter graphical display
US8914088B2 (en) 2008-09-30 2014-12-16 Covidien Lp Medical sensor and technique for using the same
US8965498B2 (en) 2010-04-05 2015-02-24 Corventis, Inc. Method and apparatus for personalized physiologic parameters
US8989831B2 (en) 2009-05-19 2015-03-24 Masimo Corporation Disposable components for reusable physiological sensor
US8998809B2 (en) 2006-05-15 2015-04-07 Cercacor Laboratories, Inc. Systems and methods for calibrating minimally invasive and non-invasive physiological sensor devices
US9010634B2 (en) 2009-06-30 2015-04-21 Covidien Lp System and method for linking patient data to a patient and providing sensor quality assurance
US9066666B2 (en) 2011-02-25 2015-06-30 Cercacor Laboratories, Inc. Patient monitor for monitoring microcirculation
US9095316B2 (en) 2011-04-20 2015-08-04 Masimo Corporation System for generating alarms based on alarm patterns
US9106038B2 (en) 2009-10-15 2015-08-11 Masimo Corporation Pulse oximetry system with low noise cable hub
US9131881B2 (en) 2012-04-17 2015-09-15 Masimo Corporation Hypersaturation index
US9138180B1 (en) 2010-05-03 2015-09-22 Masimo Corporation Sensor adapter cable
US9153112B1 (en) 2009-12-21 2015-10-06 Masimo Corporation Modular patient monitor
US9161696B2 (en) 2006-09-22 2015-10-20 Masimo Corporation Modular patient monitor
US9176141B2 (en) 2006-05-15 2015-11-03 Cercacor Laboratories, Inc. Physiological monitor calibration system
US9192329B2 (en) 2006-10-12 2015-11-24 Masimo Corporation Variable mode pulse indicator
US9192351B1 (en) 2011-07-22 2015-11-24 Masimo Corporation Acoustic respiratory monitoring sensor with probe-off detection
US9195385B2 (en) 2012-03-25 2015-11-24 Masimo Corporation Physiological monitor touchscreen interface
US9211095B1 (en) 2010-10-13 2015-12-15 Masimo Corporation Physiological measurement logic engine
US9218454B2 (en) 2009-03-04 2015-12-22 Masimo Corporation Medical monitoring system
US9245668B1 (en) 2011-06-29 2016-01-26 Cercacor Laboratories, Inc. Low noise cable providing communication between electronic sensor components and patient monitor
US9307928B1 (en) 2010-03-30 2016-04-12 Masimo Corporation Plethysmographic respiration processor
US9323894B2 (en) 2011-08-19 2016-04-26 Masimo Corporation Health care sanitation monitoring system
USD755392S1 (en) 2015-02-06 2016-05-03 Masimo Corporation Pulse oximetry sensor
US9326712B1 (en) 2010-06-02 2016-05-03 Masimo Corporation Opticoustic sensor
US9386961B2 (en) 2009-10-15 2016-07-12 Masimo Corporation Physiological acoustic monitoring system
US9392945B2 (en) 2012-01-04 2016-07-19 Masimo Corporation Automated CCHD screening and detection
US9411936B2 (en) 2007-09-14 2016-08-09 Medtronic Monitoring, Inc. Dynamic pairing of patients to data collection gateways
US9408542B1 (en) 2010-07-22 2016-08-09 Masimo Corporation Non-invasive blood pressure measurement system
US9436645B2 (en) 2011-10-13 2016-09-06 Masimo Corporation Medical monitoring hub
US9445759B1 (en) 2011-12-22 2016-09-20 Cercacor Laboratories, Inc. Blood glucose calibration system
US9451897B2 (en) 2009-12-14 2016-09-27 Medtronic Monitoring, Inc. Body adherent patch with electronics for physiologic monitoring
US9474474B2 (en) 2013-03-14 2016-10-25 Masimo Corporation Patient monitor as a minimally invasive glucometer
US9480435B2 (en) 2012-02-09 2016-11-01 Masimo Corporation Configurable patient monitoring system
US9517024B2 (en) 2009-09-17 2016-12-13 Masimo Corporation Optical-based physiological monitoring system
US9532722B2 (en) 2011-06-21 2017-01-03 Masimo Corporation Patient monitoring system
US9560996B2 (en) 2012-10-30 2017-02-07 Masimo Corporation Universal medical system
US9579039B2 (en) 2011-01-10 2017-02-28 Masimo Corporation Non-invasive intravascular volume index monitor
US9622692B2 (en) 2011-05-16 2017-04-18 Masimo Corporation Personal health device
US9649054B2 (en) 2010-08-26 2017-05-16 Cercacor Laboratories, Inc. Blood pressure measurement method
USD788312S1 (en) 2012-02-09 2017-05-30 Masimo Corporation Wireless patient monitoring device
US9697928B2 (en) 2012-08-01 2017-07-04 Masimo Corporation Automated assembly sensor cable
US9717458B2 (en) 2012-10-20 2017-08-01 Masimo Corporation Magnetic-flap optical sensor
US9724016B1 (en) 2009-10-16 2017-08-08 Masimo Corp. Respiration processor
US9724025B1 (en) 2013-01-16 2017-08-08 Masimo Corporation Active-pulse blood analysis system
US9749232B2 (en) 2012-09-20 2017-08-29 Masimo Corporation Intelligent medical network edge router
US9750442B2 (en) 2013-03-09 2017-09-05 Masimo Corporation Physiological status monitor
US9750461B1 (en) 2013-01-02 2017-09-05 Masimo Corporation Acoustic respiratory monitoring sensor with probe-off detection
US9778079B1 (en) 2011-10-27 2017-10-03 Masimo Corporation Physiological monitor gauge panel
US9775545B2 (en) 2010-09-28 2017-10-03 Masimo Corporation Magnetic electrical connector for patient monitors
US9782077B2 (en) 2011-08-17 2017-10-10 Masimo Corporation Modulated physiological sensor
US9787568B2 (en) 2012-11-05 2017-10-10 Cercacor Laboratories, Inc. Physiological test credit method
US9808188B1 (en) 2011-10-13 2017-11-07 Masimo Corporation Robust fractional saturation determination
US9839379B2 (en) 2013-10-07 2017-12-12 Masimo Corporation Regional oximetry pod
US9839381B1 (en) 2009-11-24 2017-12-12 Cercacor Laboratories, Inc. Physiological measurement system with automatic wavelength adjustment
US9861305B1 (en) 2006-10-12 2018-01-09 Masimo Corporation Method and apparatus for calibration to reduce coupling between signals in a measurement system
US9891079B2 (en) 2013-07-17 2018-02-13 Masimo Corporation Pulser with double-bearing position encoder for non-invasive physiological monitoring
US9924897B1 (en) 2014-06-12 2018-03-27 Masimo Corporation Heated reprocessing of physiological sensors
US9936917B2 (en) 2013-03-14 2018-04-10 Masimo Laboratories, Inc. Patient monitor placement indicator
US9943269B2 (en) 2011-10-13 2018-04-17 Masimo Corporation System for displaying medical monitoring data
US9955937B2 (en) 2012-09-20 2018-05-01 Masimo Corporation Acoustic patient sensor coupler
US9986952B2 (en) 2014-03-10 2018-06-05 Masimo Corporation Heart sound simulator

Families Citing this family (336)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5632272A (en) * 1991-03-07 1997-05-27 Masimo Corporation Signal processing apparatus
US7376453B1 (en) 1993-10-06 2008-05-20 Masimo Corporation Signal processing apparatus
US6987994B1 (en) * 1991-09-03 2006-01-17 Datex-Ohmeda, Inc. Pulse oximetry SpO2 determination
US9042952B2 (en) 1997-01-27 2015-05-26 Lawrence A. Lynn System and method for automatic detection of a plurality of SPO2 time series pattern types
US7758503B2 (en) * 1997-01-27 2010-07-20 Lynn Lawrence A Microprocessor system for the analysis of physiologic and financial datasets
US20060155206A1 (en) 1997-01-27 2006-07-13 Lynn Lawrence A System and method for sound and oximetry integration
US9521971B2 (en) 1997-07-14 2016-12-20 Lawrence A. Lynn System and method for automatic detection of a plurality of SPO2 time series pattern types
US9468378B2 (en) 1997-01-27 2016-10-18 Lawrence A. Lynn Airway instability detection system and method
US5421329A (en) 1994-04-01 1995-06-06 Nellcor, Inc. Pulse oximeter sensor optimized for low saturation
US6662033B2 (en) 1994-04-01 2003-12-09 Nellcor Incorporated Pulse oximeter and sensor optimized for low saturation
US6371921B1 (en) * 1994-04-15 2002-04-16 Masimo Corporation System and method of determining whether to recalibrate a blood pressure monitor
CA2176631A1 (en) * 1995-05-17 1996-11-18 Jonathan Tien System and method for the algebraic derivation of physiological signals
US5662105A (en) * 1995-05-17 1997-09-02 Spacelabs Medical, Inc. System and method for the extractment of physiological signals
US5645060A (en) * 1995-06-14 1997-07-08 Nellcor Puritan Bennett Incorporated Method and apparatus for removing artifact and noise from pulse oximetry
US5588427A (en) * 1995-11-20 1996-12-31 Spacelabs Medical, Inc. Enhancement of physiological signals using fractal analysis
US5692505A (en) * 1996-04-25 1997-12-02 Fouts; James Michael Data processing systems and methods for pulse oximeters
US5893102A (en) * 1996-12-06 1999-04-06 Unisys Corporation Textual database management, storage and retrieval system utilizing word-oriented, dictionary-based data compression/decompression
US6050950A (en) 1996-12-18 2000-04-18 Aurora Holdings, Llc Passive/non-invasive systemic and pulmonary blood pressure measurement
US9053222B2 (en) 2002-05-17 2015-06-09 Lawrence A. Lynn Patient safety processor
US20070093721A1 (en) * 2001-05-17 2007-04-26 Lynn Lawrence A Microprocessor system for the analysis of physiologic and financial datasets
US8932227B2 (en) * 2000-07-28 2015-01-13 Lawrence A. Lynn System and method for CO2 and oximetry integration
CA2283856C (en) * 1997-03-21 2005-11-22 Nellcor Puritan Bennett Inc. Method and apparatus for harmonically filtering data
US5873836A (en) * 1997-07-09 1999-02-23 Bp Sure, Llc Blood pressure monitoring with improved noise rejection
US5971930A (en) * 1997-10-17 1999-10-26 Siemens Medical Systems, Inc. Method and apparatus for removing artifact from physiological signals
US5995855A (en) * 1998-02-11 1999-11-30 Masimo Corporation Pulse oximetry sensor adapter
US6094592A (en) * 1998-05-26 2000-07-25 Nellcor Puritan Bennett, Inc. Methods and apparatus for estimating a physiological parameter using transforms
US7400918B2 (en) * 1998-07-04 2008-07-15 Edwards Lifesciences Measurement of blood oxygen saturation
US9373251B2 (en) 1999-08-09 2016-06-21 Kamilo Feher Base station devices and automobile wireless communication systems
US9813270B2 (en) 1999-08-09 2017-11-07 Kamilo Feher Heart rate sensor and medical diagnostics wireless devices
US9307407B1 (en) 1999-08-09 2016-04-05 Kamilo Feher DNA and fingerprint authentication of mobile devices
US7991448B2 (en) * 1998-10-15 2011-08-02 Philips Electronics North America Corporation Method, apparatus, and system for removing motion artifacts from measurements of bodily parameters
US6393311B1 (en) 1998-10-15 2002-05-21 Ntc Technology Inc. Method, apparatus and system for removing motion artifacts from measurements of bodily parameters
US6519486B1 (en) 1998-10-15 2003-02-11 Ntc Technology Inc. Method, apparatus and system for removing motion artifacts from measurements of bodily parameters
WO2000027284A1 (en) * 1998-11-09 2000-05-18 Xinde Li System and method for processing low signal-to-noise ratio signals
US6770028B1 (en) 1999-01-25 2004-08-03 Masimo Corporation Dual-mode pulse oximeter
GB9923069D0 (en) * 1999-09-29 1999-12-01 Nokia Telecommunications Oy Estimating an indicator for a communication path
DE60133533T2 (en) 2000-02-10 2009-06-25 Draeger Medical Systems, Inc., Danvers A method and apparatus for detecting a physiological parameter
JP2001257565A (en) * 2000-03-10 2001-09-21 Fujitsu Ltd Method and device for updating reflection coefficient of lattice type filter
JP4441974B2 (en) * 2000-03-24 2010-03-31 ソニー株式会社 A method of manufacturing a semiconductor device
US6697656B1 (en) 2000-06-27 2004-02-24 Masimo Corporation Pulse oximetry sensor compatible with multiple pulse oximetry systems
US6889153B2 (en) 2001-08-09 2005-05-03 Thomas Dietiker System and method for a self-calibrating non-invasive sensor
JP2004507293A (en) * 2000-08-15 2004-03-11 ザ リージェンツ オブ ザ ユニバーシティ オブ カリフォルニア Method and apparatus for reducing contamination of the electrical signal
WO2002024065A1 (en) 2000-09-22 2002-03-28 Knobbe, Lim & Buckingham Method and apparatus for real-time estimation and control of pysiological parameters
US6434408B1 (en) * 2000-09-29 2002-08-13 Datex-Ohmeda, Inc. Pulse oximetry method and system with improved motion correction
US6505060B1 (en) 2000-09-29 2003-01-07 Datex-Ohmeda, Inc. Method and apparatus for determining pulse oximetry differential values
US20020042558A1 (en) 2000-10-05 2002-04-11 Cybro Medical Ltd. Pulse oximeter and method of operation
US20020093908A1 (en) * 2000-11-24 2002-07-18 Esion Networks Inc. Noise/interference suppression system
US6517283B2 (en) 2001-01-16 2003-02-11 Donald Edward Coffey Cascading chute drainage system
WO2002093142A1 (en) * 2001-05-16 2002-11-21 X-Rite, Incorporated Color measurement instrument with modulated illumination
US20060195041A1 (en) 2002-05-17 2006-08-31 Lynn Lawrence A Centralized hospital monitoring system for automatically detecting upper airway instability and for preventing and aborting adverse drug reactions
WO2003009478A3 (en) * 2001-07-17 2003-08-21 Honeywell Int Inc Dual analog-to-digital converter system for increased dynamic range
US6754516B2 (en) * 2001-07-19 2004-06-22 Nellcor Puritan Bennett Incorporated Nuisance alarm reductions in a physiological monitor
EP1424934B1 (en) * 2001-09-13 2008-08-06 ConMed Corporation A signal processing method and device for signal-to-noise improvement
US6564077B2 (en) 2001-10-10 2003-05-13 Mortara Instrument, Inc. Method and apparatus for pulse oximetry
US6748254B2 (en) * 2001-10-12 2004-06-08 Nellcor Puritan Bennett Incorporated Stacked adhesive optical sensor
US7020507B2 (en) * 2002-01-31 2006-03-28 Dolphin Medical, Inc. Separating motion from cardiac signals using second order derivative of the photo-plethysmogram and fast fourier transforms
US6709402B2 (en) 2002-02-22 2004-03-23 Datex-Ohmeda, Inc. Apparatus and method for monitoring respiration with a pulse oximeter
US6896661B2 (en) * 2002-02-22 2005-05-24 Datex-Ohmeda, Inc. Monitoring physiological parameters based on variations in a photoplethysmographic baseline signal
EP1485009A1 (en) * 2002-02-22 2004-12-15 Datex-Ohmeda, Inc. Monitoring physiological parameters based on variations in a photoplethysmographic signal
US6805673B2 (en) 2002-02-22 2004-10-19 Datex-Ohmeda, Inc. Monitoring mayer wave effects based on a photoplethysmographic signal
US6702752B2 (en) 2002-02-22 2004-03-09 Datex-Ohmeda, Inc. Monitoring respiration based on plethysmographic heart rate signal
KR100455289B1 (en) 2002-03-16 2004-11-08 삼성전자주식회사 Method of diagnosing using a ray and apparatus thereof
WO2004010861A3 (en) * 2002-07-26 2004-05-13 Obi Aps Method system and devices for converting venous blood values to arterial blood values
US7096052B2 (en) * 2002-10-04 2006-08-22 Masimo Corporation Optical probe including predetermined emission wavelength based on patient type
WO2004034898A3 (en) * 2002-10-15 2004-07-15 Andreas Bindszus Method for the presentation of information concerning variations of the perfusion
US6948761B2 (en) * 2002-11-01 2005-09-27 The Colonel's International, Inc. Tonneau cover apparatus
WO2004047632A1 (en) * 2002-11-21 2004-06-10 General Hospital Corporation Apparatus and method for ascertaining and recording electrophysiological signals
US6954663B2 (en) * 2003-01-07 2005-10-11 Art Advanced Research Technologies Inc. Continuous wave optical imaging assuming a scatter-law
US7006856B2 (en) * 2003-01-10 2006-02-28 Nellcor Puritan Bennett Incorporated Signal quality metrics design for qualifying data for a physiological monitor
US7016715B2 (en) 2003-01-13 2006-03-21 Nellcorpuritan Bennett Incorporated Selection of preset filter parameters based on signal quality
EP2392257A3 (en) * 2003-03-12 2012-02-29 Yale University Method of assessing blood volume using photoelectric plethysmography
US7025728B2 (en) 2003-06-30 2006-04-11 Nihon Kohden Corporation Method for reducing noise, and pulse photometer using the method
US7455643B1 (en) 2003-07-07 2008-11-25 Nellcor Puritan Bennett Ireland Continuous non-invasive blood pressure measurement apparatus and methods providing automatic recalibration
US7242775B2 (en) * 2003-11-12 2007-07-10 Magiq Technologies, Inc. Optical pulse calibration for quantum key distribution
US6968032B2 (en) * 2003-12-18 2005-11-22 Ge Medical Systems Global Technologies Company, Llc Systems and methods for filtering images
US7163040B2 (en) 2004-01-13 2007-01-16 Sanford L.P. Correction tape applicator tip with cylindrical projection
US7254425B2 (en) * 2004-01-23 2007-08-07 Abbott Laboratories Method for detecting artifacts in data
JP4643153B2 (en) * 2004-02-06 2011-03-02 東芝メディカルシステムズ株式会社 Non-invasive subject-information imaging apparatus
US7142142B2 (en) * 2004-02-25 2006-11-28 Nelicor Puritan Bennett, Inc. Multi-bit ADC with sigma-delta modulation
US7162288B2 (en) * 2004-02-25 2007-01-09 Nellcor Purtain Bennett Incorporated Techniques for detecting heart pulses and reducing power consumption in sensors
US7120479B2 (en) * 2004-02-25 2006-10-10 Nellcor Puritan Bennett Inc. Switch-mode oximeter LED drive with a single inductor
US7190985B2 (en) 2004-02-25 2007-03-13 Nellcor Puritan Bennett Inc. Oximeter ambient light cancellation
RU2345705C2 (en) * 2004-02-26 2009-02-10 Диабетес Тулз Сведен Аб Metabolic control, method and device for obtaining indications about health-determining state of examined person
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
US7534212B2 (en) * 2004-03-08 2009-05-19 Nellcor Puritan Bennett Llc Pulse oximeter with alternate heart-rate determination
US7277741B2 (en) * 2004-03-09 2007-10-02 Nellcor Puritan Bennett Incorporated Pulse oximetry motion artifact rejection using near infrared absorption by water
US20050234317A1 (en) * 2004-03-19 2005-10-20 Kiani Massi E Low power and personal pulse oximetry systems
JP4485396B2 (en) * 2004-03-27 2010-06-23 三星電子株式会社Samsung Electronics Co.,Ltd. Biosignal simultaneous measurement device, a control method and a computer-readable recording medium
US20060111621A1 (en) * 2004-11-03 2006-05-25 Andreas Coppi Musical personal trainer
US20070048096A1 (en) * 2004-12-07 2007-03-01 Hubbs Jonathan W Soil conditioner
JP2008526443A (en) 2005-01-13 2008-07-24 ウェルチ・アリン・インコーポレーテッド Vital signs monitor
US8116839B1 (en) 2005-02-25 2012-02-14 General Electric Company System for detecting potential probe malfunction conditions in a pulse oximeter
US7392075B2 (en) 2005-03-03 2008-06-24 Nellcor Puritan Bennett Incorporated Method for enhancing pulse oximetry calculations in the presence of correlated artifacts
US7403806B2 (en) 2005-06-28 2008-07-22 General Electric Company System for prefiltering a plethysmographic signal
US7260369B2 (en) 2005-08-03 2007-08-21 Kamilo Feher Location finder, tracker, communication and remote control system
US7280810B2 (en) 2005-08-03 2007-10-09 Kamilo Feher Multimode communication system
US7548787B2 (en) 2005-08-03 2009-06-16 Kamilo Feher Medical diagnostic and communication system
US7725146B2 (en) 2005-09-29 2010-05-25 Nellcor Puritan Bennett Llc System and method for pre-processing waveforms
US7899510B2 (en) * 2005-09-29 2011-03-01 Nellcor Puritan Bennett Llc Medical sensor and technique for using the same
US7725147B2 (en) * 2005-09-29 2010-05-25 Nellcor Puritan Bennett Llc System and method for removing artifacts from waveforms
US20070106126A1 (en) 2005-09-30 2007-05-10 Mannheimer Paul D Patient monitoring alarm escalation system and method
US7881762B2 (en) * 2005-09-30 2011-02-01 Nellcor Puritan Bennett Llc Clip-style medical sensor and technique for using the same
US7530942B1 (en) 2005-10-18 2009-05-12 Masimo Corporation Remote sensing infant warmer
CN100423688C (en) * 2005-10-19 2008-10-08 深圳迈瑞生物医疗电子股份有限公司 Method and apparatus for inhibiting power frequency common-mode interference
US20070100220A1 (en) * 2005-10-28 2007-05-03 Baker Clark R Jr Adjusting parameters used in pulse oximetry analysis
US7879355B2 (en) * 2005-11-08 2011-02-01 Plensat Llc Method and system for treatment of eating disorders
CN100515335C (en) 2005-12-23 2009-07-22 深圳迈瑞生物医疗电子股份有限公司 Blood oxygen measuring method and device capable of eliminating moving inteference
US20070191697A1 (en) 2006-02-10 2007-08-16 Lynn Lawrence A System and method for SPO2 instability detection and quantification
US7668579B2 (en) * 2006-02-10 2010-02-23 Lynn Lawrence A System and method for the detection of physiologic response to stimulation
DE102006022056A1 (en) 2006-02-20 2007-08-30 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Device for determining spectral ratio between two signals with two spectrums, which depends on biological size, has computer for computation of wave ratio between spectral value of former spectrum
DE102006022120A1 (en) 2006-02-20 2007-09-06 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Spread spectrum for determination of vital signs
DE102006022055A1 (en) 2006-02-20 2007-08-30 Fraunhofer-Gesellschaft zur Förderung der angewandten Forschung e.V. Device for reducing noise component in time-discrete signal, has primary provisioning unit for provisioning time-discrete signal with noise component, where secondary provisioning device provisions primary time-discrete reference signal
US20070208259A1 (en) * 2006-03-06 2007-09-06 Mannheimer Paul D Patient monitoring alarm escalation system and method
US8702606B2 (en) * 2006-03-21 2014-04-22 Covidien Lp Patient monitoring help video system and method
US7522948B2 (en) * 2006-05-02 2009-04-21 Nellcor Puritan Bennett Llc Medical sensor and technique for using the same
US20070260132A1 (en) * 2006-05-04 2007-11-08 Sterling Bernhard B Method and apparatus for processing signals reflecting physiological characteristics from multiple sensors
US20070282181A1 (en) * 2006-06-01 2007-12-06 Carol Findlay Visual medical sensor indicator
US20080039735A1 (en) * 2006-06-06 2008-02-14 Hickerson Barry L Respiratory monitor display
US8380271B2 (en) 2006-06-15 2013-02-19 Covidien Lp System and method for generating customizable audible beep tones and alarms
WO2008002405A8 (en) * 2006-06-16 2008-02-28 Medtor Llc System and method for a non-invasive medical sensor
US20080064940A1 (en) * 2006-09-12 2008-03-13 Raridan William B Sensor cable design for use with spectrophotometric sensors and method of using the same
US8064975B2 (en) * 2006-09-20 2011-11-22 Nellcor Puritan Bennett Llc System and method for probability based determination of estimated oxygen saturation
US20080076977A1 (en) * 2006-09-26 2008-03-27 Nellcor Puritan Bennett Inc. Patient monitoring device snapshot feature system and method
US7869849B2 (en) * 2006-09-26 2011-01-11 Nellcor Puritan Bennett Llc Opaque, electrically nonconductive region on a medical sensor
US8696593B2 (en) 2006-09-27 2014-04-15 Covidien Lp Method and system for monitoring intracranial pressure
US7922665B2 (en) 2006-09-28 2011-04-12 Nellcor Puritan Bennett Llc System and method for pulse rate calculation using a scheme for alternate weighting
US7706896B2 (en) * 2006-09-29 2010-04-27 Nellcor Puritan Bennett Llc User interface and identification in a medical device system and method
US20080081956A1 (en) * 2006-09-29 2008-04-03 Jayesh Shah System and method for integrating voice with a medical device
US8068890B2 (en) * 2006-09-29 2011-11-29 Nellcor Puritan Bennett Llc Pulse oximetry sensor switchover
US7698002B2 (en) * 2006-09-29 2010-04-13 Nellcor Puritan Bennett Llc Systems and methods for user interface and identification in a medical device
US8728059B2 (en) * 2006-09-29 2014-05-20 Covidien Lp System and method for assuring validity of monitoring parameter in combination with a therapeutic device
US8160668B2 (en) * 2006-09-29 2012-04-17 Nellcor Puritan Bennett Llc Pathological condition detector using kernel methods and oximeters
US7925511B2 (en) * 2006-09-29 2011-04-12 Nellcor Puritan Bennett Llc System and method for secure voice identification in a medical device
US20080082338A1 (en) * 2006-09-29 2008-04-03 O'neil Michael P Systems and methods for secure voice identification and medical device interface
US20080097175A1 (en) * 2006-09-29 2008-04-24 Boyce Robin S System and method for display control of patient monitor
WO2008071643A1 (en) 2006-12-11 2008-06-19 Cnsystems Medizintechnik Gmbh Device for continuous, non-invasive measurement of arterial blood pressure and uses thereof
US20080200819A1 (en) * 2007-02-20 2008-08-21 Lynn Lawrence A Orthostasis detection system and method
US20080200775A1 (en) * 2007-02-20 2008-08-21 Lynn Lawrence A Maneuver-based plethysmographic pulse variation detection system and method
US8710957B2 (en) * 2007-02-28 2014-04-29 Rf Surgical Systems, Inc. Method, apparatus and article for detection of transponder tagged objects, for example during surgery
US20080221426A1 (en) * 2007-03-09 2008-09-11 Nellcor Puritan Bennett Llc Methods and apparatus for detecting misapplied optical sensors
US7696877B2 (en) * 2007-05-01 2010-04-13 Rf Surgical Systems, Inc. Method, apparatus and article for detection of transponder tagged objects, for example during surgery
WO2008154643A1 (en) * 2007-06-12 2008-12-18 Triage Wireless, Inc. Vital sign monitor for measuring blood pressure using optical, electrical, and pressure waveforms
US8602997B2 (en) * 2007-06-12 2013-12-10 Sotera Wireless, Inc. Body-worn system for measuring continuous non-invasive blood pressure (cNIBP)
US20090076342A1 (en) * 2007-09-14 2009-03-19 Corventis, Inc. Adherent Multi-Sensor Device with Empathic Monitoring
WO2009036319A1 (en) * 2007-09-14 2009-03-19 Corventis, Inc. Adherent emergency patient monitor
US20090076341A1 (en) * 2007-09-14 2009-03-19 Corventis, Inc. Adherent Athletic Monitor
EP2195076A4 (en) * 2007-09-14 2014-12-31 Corventis Inc Adherent device for cardiac rhythm management
WO2009036260A1 (en) * 2007-09-14 2009-03-19 Corventis, Inc. Data collection in a multi-sensor patient monitor
US8251903B2 (en) 2007-10-25 2012-08-28 Valencell, Inc. Noninvasive physiological analysis using excitation-sensor modules and related devices and methods
RU2491549C2 (en) * 2007-12-10 2013-08-27 БАЙЕР ХЕЛТКЭА ЭлЭлСи Amperometry with strobing and fast reading
US8204567B2 (en) * 2007-12-13 2012-06-19 Nellcor Puritan Bennett Llc Signal demodulation
US20090171167A1 (en) * 2007-12-27 2009-07-02 Nellcor Puritan Bennett Llc System And Method For Monitor Alarm Management
US20090168050A1 (en) * 2007-12-27 2009-07-02 Nellcor Puritan Bennett Llc Optical Sensor System And Method
US20090171176A1 (en) * 2007-12-28 2009-07-02 Nellcor Puritan Bennett Llc Snapshot Sensor
US20090171226A1 (en) * 2007-12-31 2009-07-02 Nellcor Puritan Bennett Llc System and method for evaluating variation in the timing of physiological events
US20090171166A1 (en) * 2007-12-31 2009-07-02 Nellcor Puritan Bennett Llc Oximeter with location awareness
US8750953B2 (en) * 2008-02-19 2014-06-10 Covidien Lp Methods and systems for alerting practitioners to physiological conditions
US8275553B2 (en) 2008-02-19 2012-09-25 Nellcor Puritan Bennett Llc System and method for evaluating physiological parameter data
US20090247851A1 (en) * 2008-03-26 2009-10-01 Nellcor Puritan Bennett Llc Graphical User Interface For Monitor Alarm Management
US20090247854A1 (en) * 2008-03-27 2009-10-01 Nellcor Puritan Bennett Llc Retractable Sensor Cable For A Pulse Oximeter
US8140272B2 (en) * 2008-03-27 2012-03-20 Nellcor Puritan Bennett Llc System and method for unmixing spectroscopic observations with nonnegative matrix factorization
US20090247850A1 (en) * 2008-03-28 2009-10-01 Nellcor Puritan Bennett Llc Manually Powered Oximeter
US8112375B2 (en) * 2008-03-31 2012-02-07 Nellcor Puritan Bennett Llc Wavelength selection and outlier detection in reduced rank linear models
US8364224B2 (en) * 2008-03-31 2013-01-29 Covidien Lp System and method for facilitating sensor and monitor communication
US8292809B2 (en) 2008-03-31 2012-10-23 Nellcor Puritan Bennett Llc Detecting chemical components from spectroscopic observations
US20090281838A1 (en) * 2008-05-07 2009-11-12 Lawrence A. Lynn Medical failure pattern search engine
CN102014745B (en) * 2008-05-09 2013-06-19 皇家飞利浦电子股份有限公司 Contactless respiration monitoring of patient
EP2123320A1 (en) * 2008-05-20 2009-11-25 General Electric Company Arrangement and method for supervising medical monitor
US8358212B2 (en) * 2008-05-27 2013-01-22 Rf Surgical Systems, Inc. Multi-modal transponder and method and apparatus to detect same
JP4518189B2 (en) * 2008-05-28 2010-08-04 ソニー株式会社 The information processing apparatus and method, program, and recording medium
US8111162B2 (en) * 2008-05-28 2012-02-07 Rf Surgical Systems, Inc. Method, apparatus and article for detection of transponder tagged objects, for example during surgery
CN102065763A (en) * 2008-05-28 2011-05-18 尼图尔医疗有限公司 Method and apparatus for CO2 evaluation
CN102065749B (en) * 2008-06-16 2013-06-19 皇家飞利浦电子股份有限公司 Monitoring a vital parameter of a patient with ''in-situ'' modulation scheme to avoid interference
US20100130875A1 (en) * 2008-06-18 2010-05-27 Triage Wireless, Inc. Body-worn system for measuring blood pressure
USD626562S1 (en) 2008-06-30 2010-11-02 Nellcor Puritan Bennett Llc Triangular saturation pattern detection indicator for a patient monitor display panel
US8660799B2 (en) 2008-06-30 2014-02-25 Nellcor Puritan Bennett Ireland Processing and detecting baseline changes in signals
USD626561S1 (en) 2008-06-30 2010-11-02 Nellcor Puritan Bennett Llc Circular satseconds indicator and triangular saturation pattern detection indicator for a patient monitor display panel
US8398556B2 (en) 2008-06-30 2013-03-19 Covidien Lp Systems and methods for non-invasive continuous blood pressure determination
US20090327515A1 (en) * 2008-06-30 2009-12-31 Thomas Price Medical Monitor With Network Connectivity
US9895068B2 (en) * 2008-06-30 2018-02-20 Covidien Lp Pulse oximeter with wait-time indication
US8862194B2 (en) 2008-06-30 2014-10-14 Covidien Lp Method for improved oxygen saturation estimation in the presence of noise
US20090326347A1 (en) * 2008-06-30 2009-12-31 Bennett Scharf Synchronous Light Detection Utilizing CMOS/CCD Sensors For Oximetry Sensing
US8506498B2 (en) 2008-07-15 2013-08-13 Nellcor Puritan Bennett Ireland Systems and methods using induced perturbation to determine physiological parameters
US8370080B2 (en) * 2008-07-15 2013-02-05 Nellcor Puritan Bennett Ireland Methods and systems for determining whether to trigger an alarm
US8385675B2 (en) * 2008-07-15 2013-02-26 Nellcor Puritan Bennett Ireland Systems and methods for filtering a signal using a continuous wavelet transform
US8805482B2 (en) * 2008-07-28 2014-08-12 General Electric Conpany System and method for signal quality indication and false alarm reduction in ECG monitoring systems
US20100191310A1 (en) * 2008-07-29 2010-07-29 Corventis, Inc. Communication-Anchor Loop For Injectable Device
US20100076276A1 (en) * 2008-09-25 2010-03-25 Nellcor Puritan Bennett Llc Medical Sensor, Display, and Technique For Using The Same
US20100076319A1 (en) * 2008-09-25 2010-03-25 Nellcor Puritan Bennett Llc Pathlength-Corrected Medical Spectroscopy
US20100081912A1 (en) * 2008-09-30 2010-04-01 Nellcor Puritan Bennett Llc Ultrasound-Optical Doppler Hemometer and Technique for Using the Same
US8532751B2 (en) 2008-09-30 2013-09-10 Covidien Lp Laser self-mixing sensors for biological sensing
US8968193B2 (en) * 2008-09-30 2015-03-03 Covidien Lp System and method for enabling a research mode on physiological monitors
US8410951B2 (en) 2008-09-30 2013-04-02 Covidien Lp Detecting a signal quality decrease in a measurement system
US8386000B2 (en) * 2008-09-30 2013-02-26 Covidien Lp System and method for photon density wave pulse oximetry and pulse hemometry
US8433382B2 (en) * 2008-09-30 2013-04-30 Covidien Lp Transmission mode photon density wave system and method
US9301697B2 (en) 2008-09-30 2016-04-05 Nellcor Puritan Bennett Ireland Systems and methods for recalibrating a non-invasive blood pressure monitor
US9314168B2 (en) 2008-09-30 2016-04-19 Nellcor Puritan Bennett Ireland Detecting sleep events using localized blood pressure changes
US9687161B2 (en) 2008-09-30 2017-06-27 Nellcor Puritan Bennett Ireland Systems and methods for maintaining blood pressure monitor calibration
US9078609B2 (en) * 2008-10-02 2015-07-14 Nellcor Puritan Bennett Ireland Extraction of physiological measurements from a photoplethysmograph (PPG) signal
US20100088957A1 (en) * 2008-10-09 2010-04-15 Hubbs Jonathan W Natural turf with binder
US8264342B2 (en) 2008-10-28 2012-09-11 RF Surgical Systems, Inc Method and apparatus to detect transponder tagged objects, for example during medical procedures
US8512260B2 (en) * 2008-10-29 2013-08-20 The Regents Of The University Of Colorado, A Body Corporate Statistical, noninvasive measurement of intracranial pressure
US20110172545A1 (en) * 2008-10-29 2011-07-14 Gregory Zlatko Grudic Active Physical Perturbations to Enhance Intelligent Medical Monitoring
US8489056B2 (en) * 2008-12-01 2013-07-16 Rockstar Consortium Us Lp Frequency agile filter using a digital filter and bandstop filtering
US20090171172A1 (en) * 2008-12-19 2009-07-02 Nellcor Puritan Bennett Llc Method and system for pulse gating
US20100216639A1 (en) * 2009-02-20 2010-08-26 Hubbs Jonathon W Gypsum soil conditioner
US9750462B2 (en) 2009-02-25 2017-09-05 Valencell, Inc. Monitoring apparatus and methods for measuring physiological and/or environmental conditions
US8788002B2 (en) 2009-02-25 2014-07-22 Valencell, Inc. Light-guiding devices and monitoring devices incorporating same
EP3127476A1 (en) 2009-02-25 2017-02-08 Valencell, Inc. Light-guiding devices and monitoring devices incorporating same
US20100224191A1 (en) * 2009-03-06 2010-09-09 Cardinal Health 207, Inc. Automated Oxygen Delivery System
US20100240972A1 (en) * 2009-03-20 2010-09-23 Nellcor Puritan Bennett Llc Slider Spot Check Pulse Oximeter
US20100249550A1 (en) * 2009-03-25 2010-09-30 Neilcor Puritan Bennett LLC Method And Apparatus For Optical Filtering Of A Broadband Emitter In A Medical Sensor
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
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
US20100318146A1 (en) * 2009-06-10 2010-12-16 Can Cinbis Tissue Oxygenation Monitoring in Heart Failure
US8352008B2 (en) * 2009-06-10 2013-01-08 Medtronic, Inc. Active noise cancellation in an optical sensor signal
US8346332B2 (en) * 2009-06-10 2013-01-01 Medtronic, Inc. Absolute calibrated tissue oxygen saturation and total hemoglobin volume fraction
EP2440116B1 (en) * 2009-06-10 2018-02-28 Medtronic, Inc. Device and method for monitoring of absolute oxygen saturation and tissue hemoglobin concentration
WO2010144648A1 (en) * 2009-06-10 2010-12-16 Medtronic, Inc. Shock reduction using absolute calibrated tissue oxygen saturation and total hemoglobin volume fraction
US20100324387A1 (en) * 2009-06-17 2010-12-23 Jim Moon Body-worn pulse oximeter
US8290730B2 (en) 2009-06-30 2012-10-16 Nellcor Puritan Bennett Ireland Systems and methods for assessing measurements in physiological monitoring devices
US9198582B2 (en) 2009-06-30 2015-12-01 Nellcor Puritan Bennett Ireland Determining a characteristic physiological parameter
US20100331631A1 (en) * 2009-06-30 2010-12-30 Nellcor Puritan Bennett Llc Oxygen saturation ear sensor design that optimizes both attachment method and signal quality
US8628477B2 (en) 2009-07-31 2014-01-14 Nellcor Puritan Bennett Ireland Systems and methods for non-invasive determination of blood pressure
US20110029865A1 (en) * 2009-07-31 2011-02-03 Nellcor Puritan Bennett Llc Control Interface For A Medical Monitor
US20110087081A1 (en) * 2009-08-03 2011-04-14 Kiani Massi Joe E Personalized physiological monitor
US8494606B2 (en) * 2009-08-19 2013-07-23 Covidien Lp Photoplethysmography with controlled application of sensor pressure
US20110066017A1 (en) * 2009-09-11 2011-03-17 Medtronic, Inc. Method and apparatus for post-shock evaluation using tissue oxygenation measurements
US8740807B2 (en) * 2009-09-14 2014-06-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiration rate
US20110066043A1 (en) * 2009-09-14 2011-03-17 Matt Banet System for measuring vital signs during hemodialysis
US8364250B2 (en) * 2009-09-15 2013-01-29 Sotera Wireless, Inc. Body-worn vital sign monitor
US20110066010A1 (en) * 2009-09-15 2011-03-17 Jim Moon Body-worn vital sign monitor
US8321004B2 (en) * 2009-09-15 2012-11-27 Sotera Wireless, Inc. Body-worn vital sign monitor
US20110066045A1 (en) * 2009-09-15 2011-03-17 Jim Moon Body-worn vital sign monitor
US20110066044A1 (en) * 2009-09-15 2011-03-17 Jim Moon Body-worn vital sign monitor
US8527038B2 (en) * 2009-09-15 2013-09-03 Sotera Wireless, Inc. Body-worn vital sign monitor
US9220440B2 (en) 2009-09-21 2015-12-29 Nellcor Puritan Bennett Ireland Determining a characteristic respiration rate
US8788001B2 (en) * 2009-09-21 2014-07-22 Covidien Lp Time-division multiplexing in a multi-wavelength photon density wave system
US8704666B2 (en) * 2009-09-21 2014-04-22 Covidien Lp Medical device interface customization systems and methods
US8494604B2 (en) * 2009-09-21 2013-07-23 Covidien Lp Wavelength-division multiplexing in a multi-wavelength photon density wave system
US8798704B2 (en) * 2009-09-24 2014-08-05 Covidien Lp Photoacoustic spectroscopy method and system to discern sepsis from shock
US8515511B2 (en) 2009-09-29 2013-08-20 Covidien Lp Sensor with an optical coupling material to improve plethysmographic measurements and method of using the same
US8376955B2 (en) * 2009-09-29 2013-02-19 Covidien Lp Spectroscopic method and system for assessing tissue temperature
US9554739B2 (en) 2009-09-29 2017-01-31 Covidien Lp Smart cable for coupling a medical sensor to an electronic patient monitor
US9066660B2 (en) 2009-09-29 2015-06-30 Nellcor Puritan Bennett Ireland Systems and methods for high-pass filtering a photoplethysmograph signal
US20110077470A1 (en) * 2009-09-30 2011-03-31 Nellcor Puritan Bennett Llc Patient Monitor Symmetry Control
US8463347B2 (en) 2009-09-30 2013-06-11 Nellcor Puritan Bennett Ireland Systems and methods for normalizing a plethysmograph signal for improved feature analysis
US20110074342A1 (en) * 2009-09-30 2011-03-31 Nellcor Puritan Bennett Llc Wireless electricity for electronic devices
US20110082711A1 (en) * 2009-10-06 2011-04-07 Masimo Laboratories, Inc. Personal digital assistant or organizer for monitoring glucose levels
US20110118561A1 (en) 2009-11-13 2011-05-19 Masimo Corporation Remote control for a medical monitoring device
US9226686B2 (en) * 2009-11-23 2016-01-05 Rf Surgical Systems, Inc. Method and apparatus to account for transponder tagged objects used during medical procedures
CA2782512A1 (en) 2009-12-02 2011-06-09 Neetour Medical Ltd. Hemodynamics-based monitoring and evaluation of a respiratory condition
DE102009047660A1 (en) * 2009-12-08 2011-06-09 Endress + Hauser Conducta Gesellschaft für Mess- und Regeltechnik mbH + Co. KG Method for compensating variation of light intensity of light beam during optical measurement, involves supplying light beam signal to adaptive filter, and evaluating filter output signal based on optical characteristics of measuring medium
US9675282B2 (en) * 2010-02-11 2017-06-13 Koninklijke Philips N.V. Method and apparatus for determining a respiration signal
US20110224498A1 (en) * 2010-03-10 2011-09-15 Sotera Wireless, Inc. Body-worn vital sign monitor
EP2549926A1 (en) * 2010-03-23 2013-01-30 Koninklijke Philips Electronics N.V. Interference reduction in monitoring a vital parameter of a patient
US9451887B2 (en) 2010-03-31 2016-09-27 Nellcor Puritan Bennett Ireland Systems and methods for measuring electromechanical delay of the heart
US9339209B2 (en) 2010-04-19 2016-05-17 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
US8888700B2 (en) 2010-04-19 2014-11-18 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
US9173593B2 (en) 2010-04-19 2015-11-03 Sotera Wireless, Inc. Body-worn monitor for measuring respiratory rate
US8898037B2 (en) 2010-04-28 2014-11-25 Nellcor Puritan Bennett Ireland Systems and methods for signal monitoring using Lissajous figures
US7884933B1 (en) 2010-05-05 2011-02-08 Revolutionary Business Concepts, Inc. Apparatus and method for determining analyte concentrations
US8930145B2 (en) 2010-07-28 2015-01-06 Covidien Lp Light focusing continuous wave photoacoustic spectroscopy and its applications to patient monitoring
US8825428B2 (en) 2010-11-30 2014-09-02 Neilcor Puritan Bennett Ireland Methods and systems for recalibrating a blood pressure monitor with memory
US20120226117A1 (en) 2010-12-01 2012-09-06 Lamego Marcelo M Handheld processing device including medical applications for minimally and non invasive glucose measurements
US9357934B2 (en) 2010-12-01 2016-06-07 Nellcor Puritan Bennett Ireland Systems and methods for physiological event marking
US9259160B2 (en) 2010-12-01 2016-02-16 Nellcor Puritan Bennett Ireland Systems and methods for determining when to measure a physiological parameter
US20140249440A1 (en) 2010-12-28 2014-09-04 Matt Banet Body-worn system for continuous, noninvasive measurement of cardiac output, stroke volume, cardiac power, and blood pressure
CN103582449B (en) 2011-02-18 2017-06-09 索泰拉无线公司 Modular wrist-worn processor for patient monitoring of
US9109902B1 (en) 2011-06-13 2015-08-18 Impact Sports Technologies, Inc. Monitoring device with a pedometer
US20130023775A1 (en) 2011-07-20 2013-01-24 Cercacor Laboratories, Inc. Magnetic Reusable Sensor
EP2734103A4 (en) 2011-07-22 2014-11-05 Flashback Technologies Inc Hemodynamic reserve monitor and hemodialysis control
US9427191B2 (en) 2011-07-25 2016-08-30 Valencell, Inc. Apparatus and methods for estimating time-state physiological parameters
RU2466493C1 (en) * 2011-07-26 2012-11-10 Федеральное государственное бюджетное образовательное учреждение высшего профессионального образования "Ставропольский государственный аграрный университет" Method to generate reference voltage
US9801552B2 (en) 2011-08-02 2017-10-31 Valencell, Inc. Systems and methods for variable filter adjustment by heart rate metric feedback
JP5837785B2 (en) 2011-09-13 2015-12-24 日本光電工業株式会社 Biological signal measuring device
US9402554B2 (en) 2011-09-23 2016-08-02 Nellcor Puritan Bennett Ireland Systems and methods for determining respiration information from a photoplethysmograph
US9119597B2 (en) 2011-09-23 2015-09-01 Nellcor Puritan Bennett Ireland Systems and methods for determining respiration information from a photoplethysmograph
US9675274B2 (en) 2011-09-23 2017-06-13 Nellcor Puritan Bennett Ireland Systems and methods for determining respiration information from a photoplethysmograph
US9693709B2 (en) 2011-09-23 2017-07-04 Nellcot Puritan Bennett Ireland Systems and methods for determining respiration information from a photoplethysmograph
US9060695B2 (en) 2011-11-30 2015-06-23 Covidien Lp Systems and methods for determining differential pulse transit time from the phase difference of two analog plethysmographs
US9693736B2 (en) 2011-11-30 2017-07-04 Nellcor Puritan Bennett Ireland Systems and methods for determining respiration information using historical distribution
JP6102055B2 (en) * 2012-01-25 2017-03-29 セイコーエプソン株式会社 Pulse wave measuring device and the signal processing unit
JP6179064B2 (en) * 2012-01-25 2017-08-16 セイコーエプソン株式会社 Pulse wave measuring device and the signal processing unit
US9833146B2 (en) 2012-04-17 2017-12-05 Covidien Lp Surgical system and method of use of the same
US8619927B2 (en) 2012-05-29 2013-12-31 Magnolia Broadband Inc. System and method for discrete gain control in hybrid MIMO/RF beamforming
US8971452B2 (en) 2012-05-29 2015-03-03 Magnolia Broadband Inc. Using 3G/4G baseband signals for tuning beamformers in hybrid MIMO RDN systems
US8861635B2 (en) 2012-05-29 2014-10-14 Magnolia Broadband Inc. Setting radio frequency (RF) beamformer antenna weights per data-stream in a multiple-input-multiple-output (MIMO) system
US8644413B2 (en) 2012-05-29 2014-02-04 Magnolia Broadband Inc. Implementing blind tuning in hybrid MIMO RF beamforming systems
US8811522B2 (en) 2012-05-29 2014-08-19 Magnolia Broadband Inc. Mitigating interferences for a multi-layer MIMO system augmented by radio distribution network
US8837650B2 (en) 2012-05-29 2014-09-16 Magnolia Broadband Inc. System and method for discrete gain control in hybrid MIMO RF beamforming for multi layer MIMO base station
US8842765B2 (en) 2012-05-29 2014-09-23 Magnolia Broadband Inc. Beamformer configurable for connecting a variable number of antennas and radio circuits
US8767862B2 (en) 2012-05-29 2014-07-01 Magnolia Broadband Inc. Beamformer phase optimization for a multi-layer MIMO system augmented by radio distribution network
US9154204B2 (en) 2012-06-11 2015-10-06 Magnolia Broadband Inc. Implementing transmit RDN architectures in uplink MIMO systems
US9414752B2 (en) 2012-11-09 2016-08-16 Elwha Llc Embolism deflector
US8797969B1 (en) 2013-02-08 2014-08-05 Magnolia Broadband Inc. Implementing multi user multiple input multiple output (MU MIMO) base station using single-user (SU) MIMO co-located base stations
US9343808B2 (en) 2013-02-08 2016-05-17 Magnotod Llc Multi-beam MIMO time division duplex base station using subset of radios
US8774150B1 (en) 2013-02-13 2014-07-08 Magnolia Broadband Inc. System and method for reducing side-lobe contamination effects in Wi-Fi access points
US20140226740A1 (en) 2013-02-13 2014-08-14 Magnolia Broadband Inc. Multi-beam co-channel wi-fi access point
US8989103B2 (en) 2013-02-13 2015-03-24 Magnolia Broadband Inc. Method and system for selective attenuation of preamble reception in co-located WI FI access points
WO2014159132A1 (en) 2013-03-14 2014-10-02 Cercacor Laboratories, Inc. Systems and methods for testing patient monitors
WO2014149781A8 (en) 2013-03-15 2015-04-02 Cercacor Laboratories, Inc. Cloud-based physiological monitoring system
US20140275878A1 (en) * 2013-03-15 2014-09-18 Covidien Lp Methods and systems for equalizing physiological signals
US9155110B2 (en) 2013-03-27 2015-10-06 Magnolia Broadband Inc. System and method for co-located and co-channel Wi-Fi access points
US9100968B2 (en) 2013-05-09 2015-08-04 Magnolia Broadband Inc. Method and system for digital cancellation scheme with multi-beam
US9425882B2 (en) 2013-06-28 2016-08-23 Magnolia Broadband Inc. Wi-Fi radio distribution network stations and method of operating Wi-Fi RDN stations
US8995416B2 (en) 2013-07-10 2015-03-31 Magnolia Broadband Inc. System and method for simultaneous co-channel access of neighboring access points
US8824596B1 (en) 2013-07-31 2014-09-02 Magnolia Broadband Inc. System and method for uplink transmissions in time division MIMO RDN architecture
US9497781B2 (en) 2013-08-13 2016-11-15 Magnolia Broadband Inc. System and method for co-located and co-channel Wi-Fi access points
US9088898B2 (en) 2013-09-12 2015-07-21 Magnolia Broadband Inc. System and method for cooperative scheduling for co-located access points
US9060362B2 (en) 2013-09-12 2015-06-16 Magnolia Broadband Inc. Method and system for accessing an occupied Wi-Fi channel by a client using a nulling scheme
US9830424B2 (en) 2013-09-18 2017-11-28 Hill-Rom Services, Inc. Bed/room/patient association systems and methods
US9172454B2 (en) 2013-11-01 2015-10-27 Magnolia Broadband Inc. Method and system for calibrating a transceiver array
US8891598B1 (en) 2013-11-19 2014-11-18 Magnolia Broadband Inc. Transmitter and receiver calibration for obtaining the channel reciprocity for time division duplex MIMO systems
US8929322B1 (en) * 2013-11-20 2015-01-06 Magnolia Broadband Inc. System and method for side lobe suppression using controlled signal cancellation
US8942134B1 (en) 2013-11-20 2015-01-27 Magnolia Broadband Inc. System and method for selective registration in a multi-beam system
US9294177B2 (en) 2013-11-26 2016-03-22 Magnolia Broadband Inc. System and method for transmit and receive antenna patterns calibration for time division duplex (TDD) systems
US9014066B1 (en) 2013-11-26 2015-04-21 Magnolia Broadband Inc. System and method for transmit and receive antenna patterns calibration for time division duplex (TDD) systems
US9042276B1 (en) 2013-12-05 2015-05-26 Magnolia Broadband Inc. Multiple co-located multi-user-MIMO access points
US9788794B2 (en) 2014-02-28 2017-10-17 Valencell, Inc. Method and apparatus for generating assessments using physical activity and biometric parameters
US9172446B2 (en) 2014-03-19 2015-10-27 Magnolia Broadband Inc. Method and system for supporting sparse explicit sounding by implicit data
US9100154B1 (en) 2014-03-19 2015-08-04 Magnolia Broadband Inc. Method and system for explicit AP-to-AP sounding in an 802.11 network
US9271176B2 (en) 2014-03-28 2016-02-23 Magnolia Broadband Inc. System and method for backhaul based sounding feedback
US9514341B2 (en) 2014-03-31 2016-12-06 Covidien Lp Method, apparatus and article for detection of transponder tagged objects, for example during surgery
GB201409599D0 (en) * 2014-05-30 2014-07-16 Huntleigh Technology Ltd Tissue variability compensation apparatus and method
US9179849B1 (en) 2014-07-25 2015-11-10 Impact Sports Technologies, Inc. Mobile plethysmographic device
US20160029898A1 (en) 2014-07-30 2016-02-04 Valencell, Inc. Physiological Monitoring Devices and Methods Using Optical Sensors
US20160066824A1 (en) 2014-09-04 2016-03-10 Masimo Corporation Total hemoglobin screening sensor
US9794653B2 (en) 2014-09-27 2017-10-17 Valencell, Inc. Methods and apparatus for improving signal quality in wearable biometric monitoring devices
WO2016057553A1 (en) 2014-10-07 2016-04-14 Masimo Corporation Modular physiological sensors
US9690963B2 (en) 2015-03-02 2017-06-27 Covidien Lp Hand-held dual spherical antenna system
USD775331S1 (en) 2015-03-02 2016-12-27 Covidien Lp Hand-held antenna system
US20160361003A1 (en) * 2015-06-12 2016-12-15 ChroniSense Medical Ltd. Pulse Oximetry

Citations (68)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3638640A (en) 1967-11-01 1972-02-01 Robert F Shaw Oximeter and method for in vivo determination of oxygen saturation in blood using three or more different wavelengths
US3647299A (en) 1970-04-20 1972-03-07 American Optical Corp Oximeter
US3704706A (en) 1969-10-23 1972-12-05 Univ Drexel Heart rate and respiratory monitor
US3991277A (en) 1973-02-15 1976-11-09 Yoshimutsu Hirata Frequency division multiplex system using comb filters
US3998550A (en) 1974-10-14 1976-12-21 Minolta Camera Corporation Photoelectric oximeter
US4038536A (en) * 1976-03-29 1977-07-26 Rockwell International Corporation Adaptive recursive least mean square error filter
US4063551A (en) 1976-04-06 1977-12-20 Unisen, Inc. Blood pulse sensor and readout
US4086915A (en) 1975-04-30 1978-05-02 Harvey I. Kofsky Ear oximetry process and apparatus
US4095117A (en) 1975-06-30 1978-06-13 Medicor Muvek Circuit for defining the dye dilution curves in vivo and in vitro for calculating the cardiac blood flowrate value per minute
US4238746A (en) 1978-03-20 1980-12-09 The United States Of America As Represented By The Secretary Of The Navy Adaptive line enhancer
US4243935A (en) 1979-05-18 1981-01-06 The United States Of America As Represented By The Secretary Of The Navy Adaptive detector
US4266554A (en) 1978-06-22 1981-05-12 Minolta Camera Kabushiki Kaisha Digital oximeter
US4305398A (en) 1977-12-30 1981-12-15 Minolta Camera Kabushiki Kaisha Eye fundus oximeter
US4407290A (en) 1981-04-01 1983-10-04 Biox Technology, Inc. Blood constituent measuring device and method
US4446871A (en) 1980-01-25 1984-05-08 Minolta Kabushiki Kaisha Optical analyzer for measuring a construction ratio between components in the living tissue
DE3323862A1 (en) 1983-06-29 1985-01-03 Affeld Klaus Dr Dipl Ing The safety drive for an artificial heart
US4519396A (en) 1979-03-30 1985-05-28 American Home Products Corporation (Del.) Fetal heart rate monitor apparatus and method for combining electrically and mechanically derived cardiographic signals
US4537200A (en) 1983-07-07 1985-08-27 The Board Of Trustees Of The Leland Stanford Junior University ECG enhancement by adaptive cancellation of electrosurgical interference
GB2166326A (en) 1984-10-29 1986-04-30 Hazeltine Corp LMS adaptive loop module
US4586513A (en) 1982-02-19 1986-05-06 Minolta Camera Kabushiki Kaisha Noninvasive device for photoelectrically measuring the property of arterial blood
US4617589A (en) 1984-12-17 1986-10-14 Rca Corporation Adaptive frame comb filter system
US4649505A (en) 1984-07-02 1987-03-10 General Electric Company Two-input crosstalk-resistant adaptive noise canceller
US4653498A (en) 1982-09-13 1987-03-31 Nellcor Incorporated Pulse oximeter monitor
US4714341A (en) 1984-02-23 1987-12-22 Minolta Camera Kabushiki Kaisha Multi-wavelength oximeter having a means for disregarding a poor signal
US4751931A (en) 1986-09-22 1988-06-21 Allegheny-Singer Research Institute Method and apparatus for determining his-purkinje activity
US4773422A (en) 1987-04-30 1988-09-27 Nonin Medical, Inc. Single channel pulse oximeter
US4781200A (en) 1985-10-04 1988-11-01 Baker Donald A Ambulatory non-invasive automatic fetal monitoring system
US4799493A (en) 1987-03-13 1989-01-24 Cardiac Pacemakers, Inc. Dual channel coherent fibrillation detection system
US4800495A (en) 1986-08-18 1989-01-24 Physio-Control Corporation Method and apparatus for processing signals used in oximetry
US4802486A (en) 1985-04-01 1989-02-07 Nellcor Incorporated Method and apparatus for detecting optical pulses
US4807631A (en) 1987-10-09 1989-02-28 Critikon, Inc. Pulse oximetry system
US4819646A (en) 1986-08-18 1989-04-11 Physio-Control Corporation Feedback-controlled method and apparatus for processing signals used in oximetry
US4824242A (en) 1986-09-26 1989-04-25 Sensormedics Corporation Non-invasive oximeter and method
US4848901A (en) 1987-10-08 1989-07-18 Critikon, Inc. Pulse oximeter sensor control system
US4858199A (en) 1988-09-06 1989-08-15 Mobile Oil Corporation Method and apparatus for cancelling nonstationary sinusoidal noise from seismic data
US4859056A (en) 1986-08-18 1989-08-22 Physio-Control Corporation Multiple-pulse method and apparatus for use in oximetry
US4860759A (en) 1987-09-08 1989-08-29 Criticare Systems, Inc. Vital signs monitor
US4863265A (en) 1987-10-16 1989-09-05 Mine Safety Appliances Company Apparatus and method for measuring blood constituents
US4867571A (en) 1986-09-26 1989-09-19 Sensormedics Corporation Wave form filter pulse detector and method for modulated signal
US4869253A (en) 1986-08-18 1989-09-26 Physio-Control Corporation Method and apparatus for indicating perfusion and oxygen saturation trends in oximetry
US4869254A (en) 1988-03-30 1989-09-26 Nellcor Incorporated Method and apparatus for calculating arterial oxygen saturation
EP0335357A2 (en) 1988-03-30 1989-10-04 Nellcor Incorporated Improved method and apparatus for detecting optical pulses
EP0341327A1 (en) 1988-05-09 1989-11-15 Hewlett-Packard GmbH A method for processing signals, particularly for oximetric measurements on living human tissue
US4883356A (en) 1988-09-13 1989-11-28 The Perkin-Elmer Corporation Spectrometer detector mounting assembly
US4883353A (en) 1988-02-11 1989-11-28 Puritan-Bennett Corporation Pulse oximeter
US4892101A (en) 1986-08-18 1990-01-09 Physio-Control Corporation Method and apparatus for offsetting baseline portion of oximeter signal
US4907594A (en) 1987-07-18 1990-03-13 Nicolay Gmbh Method for the determination of the saturation of the blood of a living organism with oxygen and electronic circuit for performing this method
US4913150A (en) 1986-08-18 1990-04-03 Physio-Control Corporation Method and apparatus for the automatic calibration of signals employed in oximetry
US4927264A (en) 1987-12-02 1990-05-22 Omron Tateisi Electronics Co. Non-invasive measuring method and apparatus of blood constituents
US4928692A (en) 1985-04-01 1990-05-29 Goodman David E Method and apparatus for detecting optical pulses
US4934372A (en) 1985-04-01 1990-06-19 Nellcor Incorporated Method and apparatus for detecting optical pulses
US4942877A (en) 1986-09-05 1990-07-24 Minolta Camera Kabushiki Kaisha Device for measuring oxygen saturation degree in arterial blood
US4948248A (en) 1988-07-22 1990-08-14 Invivo Research Inc. Blood constituent measuring device and method
US4955379A (en) 1987-08-14 1990-09-11 National Research Development Corporation Motion artefact rejection system for pulse oximeters
US4956867A (en) 1989-04-20 1990-09-11 Massachusetts Institute Of Technology Adaptive beamforming for noise reduction
US4960126A (en) 1988-01-15 1990-10-02 Criticare Systems, Inc. ECG synchronized pulse oximeter
GB2235288A (en) 1989-07-27 1991-02-27 Nat Res Dev Oximeters
US5042499A (en) 1988-09-30 1991-08-27 Frank Thomas H Noninvasive electrocardiographic method of real time signal processing for obtaining and displaying instantaneous fetal heart rate and fetal heart rate beat-to-beat variability
US5057695A (en) 1988-12-19 1991-10-15 Otsuka Electronics Co., Ltd. Method of and apparatus for measuring the inside information of substance with the use of light scattering
US5246002A (en) 1992-02-11 1993-09-21 Physio-Control Corporation Noise insensitive pulse transmittance oximeter
US5259381A (en) 1986-08-18 1993-11-09 Physio-Control Corporation Apparatus for the automatic calibration of signals employed in oximetry
US5273036A (en) 1991-04-03 1993-12-28 Ppg Industries, Inc. Apparatus and method for monitoring respiration
US5431170A (en) 1990-05-26 1995-07-11 Mathews; Geoffrey R. Pulse responsive device
US5482036A (en) 1991-03-07 1996-01-09 Masimo Corporation Signal processing apparatus and method
US5490505A (en) 1991-03-07 1996-02-13 Masimo Corporation Signal processing apparatus
EP0760223A1 (en) 1995-08-31 1997-03-05 Hewlett-Packard GmbH Apparatus for monitoring, in particular pulse oximeter
US5842981A (en) 1996-07-17 1998-12-01 Criticare Systems, Inc. Direct to digital oximeter
EP0761159B1 (en) 1995-08-31 1999-09-29 Hewlett-Packard Company Apparatus for medical monitoring, in particular pulse oximeter

Family Cites Families (37)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE3328862A1 (en) * 1982-09-16 1985-02-28 Siemens Ag Tissue photometry method and device, in particular for quantatively determining the blood oxygen saturation from photometric measurements
JPH0535930B2 (en) * 1985-12-06 1993-05-27 Nippon Electric Co
US4793361A (en) * 1987-03-13 1988-12-27 Cardiac Pacemakers, Inc. Dual channel P-wave detection in surface electrocardiographs
GB8722899D0 (en) * 1987-09-30 1987-11-04 Kirk D L Fetal monitoring during labour
US4819752A (en) * 1987-10-02 1989-04-11 Datascope Corp. Blood constituent measuring device and method
US4949710A (en) * 1988-10-06 1990-08-21 Protocol Systems, Inc. Method of artifact rejection for noninvasive blood-pressure measurement by prediction and adjustment of blood-pressure data
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
US5638818A (en) * 1991-03-21 1997-06-17 Masimo Corporation Low noise optical probe
DE69227545T2 (en) * 1991-07-12 1999-04-29 Mark R Robinson Oximeter for reliable clinical determination of blood oxygen saturation in a fetus
US5337744A (en) * 1993-07-14 1994-08-16 Masimo Corporation Low noise finger cot probe
US6371921B1 (en) * 1994-04-15 2002-04-16 Masimo Corporation System and method of determining whether to recalibrate a blood pressure monitor
US5638816A (en) * 1995-06-07 1997-06-17 Masimo Corporation Active pulse blood constituent monitoring
US5758644A (en) * 1995-06-07 1998-06-02 Masimo Corporation Manual and automatic probe calibration
US5743262A (en) * 1995-06-07 1998-04-28 Masimo Corporation Blood glucose monitoring system
US5760910A (en) * 1995-06-07 1998-06-02 Masimo Corporation Optical filter for spectroscopic measurement and method of producing the optical filter
US5645440A (en) * 1995-10-16 1997-07-08 Masimo Corporation Patient cable connector
US5890929A (en) * 1996-06-19 1999-04-06 Masimo Corporation Shielded medical connector
US5919134A (en) * 1997-04-14 1999-07-06 Masimo Corp. Method and apparatus for demodulating signals in a pulse oximetry system
US6229856B1 (en) * 1997-04-14 2001-05-08 Masimo Corporation Method and apparatus for demodulating signals in a pulse oximetry system
US6002952A (en) * 1997-04-14 1999-12-14 Masimo Corporation Signal processing apparatus and method
US6184521B1 (en) * 1998-01-06 2001-02-06 Masimo Corporation Photodiode detector with integrated noise shielding
US5995855A (en) * 1998-02-11 1999-11-30 Masimo Corporation Pulse oximetry sensor adapter
US6525386B1 (en) * 1998-03-10 2003-02-25 Masimo Corporation Non-protruding optoelectronic lens
EP2319398A1 (en) * 1998-06-03 2011-05-11 Masimo Corporation Stereo pulse oximeter
US6606511B1 (en) * 1999-01-07 2003-08-12 Masimo Corporation Pulse oximetry pulse indicator
US6684090B2 (en) * 1999-01-07 2004-01-27 Masimo Corporation Pulse oximetry data confidence indicator
DE60037106T2 (en) * 1999-01-25 2008-09-11 Masimo Corp., Irvine Universal / improving pulse oximeter
US6360114B1 (en) * 1999-03-25 2002-03-19 Masimo Corporation Pulse oximeter probe-off detector
WO2000078209A3 (en) * 1999-06-18 2001-04-19 Masimo Corp Pulse oximeter probe-off detection system
US6580086B1 (en) * 1999-08-26 2003-06-17 Masimo Corporation Shielded optical probe and method
US6515273B2 (en) * 1999-08-26 2003-02-04 Masimo Corporation System for indicating the expiration of the useful operating life of a pulse oximetry sensor
US6542764B1 (en) * 1999-12-01 2003-04-01 Masimo Corporation Pulse oximeter monitor for expressing the urgency of the patient's condition
US6377829B1 (en) * 1999-12-09 2002-04-23 Masimo Corporation Resposable pulse oximetry sensor
US6430525B1 (en) * 2000-06-05 2002-08-06 Masimo Corporation Variable mode averager
US6697656B1 (en) * 2000-06-27 2004-02-24 Masimo Corporation Pulse oximetry sensor compatible with multiple pulse oximetry systems
US6697658B2 (en) * 2001-07-02 2004-02-24 Masimo Corporation Low power pulse oximeter

Patent Citations (73)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US3638640A (en) 1967-11-01 1972-02-01 Robert F Shaw Oximeter and method for in vivo determination of oxygen saturation in blood using three or more different wavelengths
US3704706A (en) 1969-10-23 1972-12-05 Univ Drexel Heart rate and respiratory monitor
US3647299A (en) 1970-04-20 1972-03-07 American Optical Corp Oximeter
US3991277A (en) 1973-02-15 1976-11-09 Yoshimutsu Hirata Frequency division multiplex system using comb filters
US3998550A (en) 1974-10-14 1976-12-21 Minolta Camera Corporation Photoelectric oximeter
US4086915A (en) 1975-04-30 1978-05-02 Harvey I. Kofsky Ear oximetry process and apparatus
US4095117A (en) 1975-06-30 1978-06-13 Medicor Muvek Circuit for defining the dye dilution curves in vivo and in vitro for calculating the cardiac blood flowrate value per minute
US4038536A (en) * 1976-03-29 1977-07-26 Rockwell International Corporation Adaptive recursive least mean square error filter
US4063551A (en) 1976-04-06 1977-12-20 Unisen, Inc. Blood pulse sensor and readout
US4305398A (en) 1977-12-30 1981-12-15 Minolta Camera Kabushiki Kaisha Eye fundus oximeter
US4238746A (en) 1978-03-20 1980-12-09 The United States Of America As Represented By The Secretary Of The Navy Adaptive line enhancer
US4266554A (en) 1978-06-22 1981-05-12 Minolta Camera Kabushiki Kaisha Digital oximeter
US4519396A (en) 1979-03-30 1985-05-28 American Home Products Corporation (Del.) Fetal heart rate monitor apparatus and method for combining electrically and mechanically derived cardiographic signals
US4243935A (en) 1979-05-18 1981-01-06 The United States Of America As Represented By The Secretary Of The Navy Adaptive detector
US4446871A (en) 1980-01-25 1984-05-08 Minolta Kabushiki Kaisha Optical analyzer for measuring a construction ratio between components in the living tissue
US4407290A (en) 1981-04-01 1983-10-04 Biox Technology, Inc. Blood constituent measuring device and method
US4407290B1 (en) 1981-04-01 1986-10-14
US4586513A (en) 1982-02-19 1986-05-06 Minolta Camera Kabushiki Kaisha Noninvasive device for photoelectrically measuring the property of arterial blood
US4694833A (en) 1982-02-19 1987-09-22 Minolta Camera Kabushiki Kaisha Noninvasive device for photoelectrically measuring the property of arterial blood
US4653498B1 (en) 1982-09-13 1989-04-18
US4653498A (en) 1982-09-13 1987-03-31 Nellcor Incorporated Pulse oximeter monitor
DE3323862A1 (en) 1983-06-29 1985-01-03 Affeld Klaus Dr Dipl Ing The safety drive for an artificial heart
US4537200A (en) 1983-07-07 1985-08-27 The Board Of Trustees Of The Leland Stanford Junior University ECG enhancement by adaptive cancellation of electrosurgical interference
US4714341A (en) 1984-02-23 1987-12-22 Minolta Camera Kabushiki Kaisha Multi-wavelength oximeter having a means for disregarding a poor signal
US4649505A (en) 1984-07-02 1987-03-10 General Electric Company Two-input crosstalk-resistant adaptive noise canceller
GB2166326A (en) 1984-10-29 1986-04-30 Hazeltine Corp LMS adaptive loop module
US4617589A (en) 1984-12-17 1986-10-14 Rca Corporation Adaptive frame comb filter system
US4934372A (en) 1985-04-01 1990-06-19 Nellcor Incorporated Method and apparatus for detecting optical pulses
US4928692A (en) 1985-04-01 1990-05-29 Goodman David E Method and apparatus for detecting optical pulses
US4802486A (en) 1985-04-01 1989-02-07 Nellcor Incorporated Method and apparatus for detecting optical pulses
US4911167A (en) 1985-06-07 1990-03-27 Nellcor Incorporated Method and apparatus for detecting optical pulses
US4781200A (en) 1985-10-04 1988-11-01 Baker Donald A Ambulatory non-invasive automatic fetal monitoring system
US4869253A (en) 1986-08-18 1989-09-26 Physio-Control Corporation Method and apparatus for indicating perfusion and oxygen saturation trends in oximetry
US4819646A (en) 1986-08-18 1989-04-11 Physio-Control Corporation Feedback-controlled method and apparatus for processing signals used in oximetry
US5259381A (en) 1986-08-18 1993-11-09 Physio-Control Corporation Apparatus for the automatic calibration of signals employed in oximetry
US4800495A (en) 1986-08-18 1989-01-24 Physio-Control Corporation Method and apparatus for processing signals used in oximetry
US4892101A (en) 1986-08-18 1990-01-09 Physio-Control Corporation Method and apparatus for offsetting baseline portion of oximeter signal
US4913150A (en) 1986-08-18 1990-04-03 Physio-Control Corporation Method and apparatus for the automatic calibration of signals employed in oximetry
US4859056A (en) 1986-08-18 1989-08-22 Physio-Control Corporation Multiple-pulse method and apparatus for use in oximetry
US4942877A (en) 1986-09-05 1990-07-24 Minolta Camera Kabushiki Kaisha Device for measuring oxygen saturation degree in arterial blood
US4751931A (en) 1986-09-22 1988-06-21 Allegheny-Singer Research Institute Method and apparatus for determining his-purkinje activity
US4867571A (en) 1986-09-26 1989-09-19 Sensormedics Corporation Wave form filter pulse detector and method for modulated signal
US4824242A (en) 1986-09-26 1989-04-25 Sensormedics Corporation Non-invasive oximeter and method
US4799493A (en) 1987-03-13 1989-01-24 Cardiac Pacemakers, Inc. Dual channel coherent fibrillation detection system
US4773422A (en) 1987-04-30 1988-09-27 Nonin Medical, Inc. Single channel pulse oximeter
US4907594A (en) 1987-07-18 1990-03-13 Nicolay Gmbh Method for the determination of the saturation of the blood of a living organism with oxygen and electronic circuit for performing this method
US4955379A (en) 1987-08-14 1990-09-11 National Research Development Corporation Motion artefact rejection system for pulse oximeters
US4860759A (en) 1987-09-08 1989-08-29 Criticare Systems, Inc. Vital signs monitor
US4848901A (en) 1987-10-08 1989-07-18 Critikon, Inc. Pulse oximeter sensor control system
US4807631A (en) 1987-10-09 1989-02-28 Critikon, Inc. Pulse oximetry system
US4863265A (en) 1987-10-16 1989-09-05 Mine Safety Appliances Company Apparatus and method for measuring blood constituents
US4927264A (en) 1987-12-02 1990-05-22 Omron Tateisi Electronics Co. Non-invasive measuring method and apparatus of blood constituents
US4960126A (en) 1988-01-15 1990-10-02 Criticare Systems, Inc. ECG synchronized pulse oximeter
US4883353A (en) 1988-02-11 1989-11-28 Puritan-Bennett Corporation Pulse oximeter
US4869254A (en) 1988-03-30 1989-09-26 Nellcor Incorporated Method and apparatus for calculating arterial oxygen saturation
EP0335357A2 (en) 1988-03-30 1989-10-04 Nellcor Incorporated Improved method and apparatus for detecting optical pulses
EP0341327A1 (en) 1988-05-09 1989-11-15 Hewlett-Packard GmbH A method for processing signals, particularly for oximetric measurements on living human tissue
US4948248A (en) 1988-07-22 1990-08-14 Invivo Research Inc. Blood constituent measuring device and method
US4858199A (en) 1988-09-06 1989-08-15 Mobile Oil Corporation Method and apparatus for cancelling nonstationary sinusoidal noise from seismic data
US4883356A (en) 1988-09-13 1989-11-28 The Perkin-Elmer Corporation Spectrometer detector mounting assembly
US5042499A (en) 1988-09-30 1991-08-27 Frank Thomas H Noninvasive electrocardiographic method of real time signal processing for obtaining and displaying instantaneous fetal heart rate and fetal heart rate beat-to-beat variability
US5057695A (en) 1988-12-19 1991-10-15 Otsuka Electronics Co., Ltd. Method of and apparatus for measuring the inside information of substance with the use of light scattering
US4956867A (en) 1989-04-20 1990-09-11 Massachusetts Institute Of Technology Adaptive beamforming for noise reduction
GB2235288A (en) 1989-07-27 1991-02-27 Nat Res Dev Oximeters
US5431170A (en) 1990-05-26 1995-07-11 Mathews; Geoffrey R. Pulse responsive device
US5769785A (en) 1991-03-07 1998-06-23 Masimo Corporation Signal processing apparatus and method
US5482036A (en) 1991-03-07 1996-01-09 Masimo Corporation Signal processing apparatus and method
US5490505A (en) 1991-03-07 1996-02-13 Masimo Corporation Signal processing apparatus
US5273036A (en) 1991-04-03 1993-12-28 Ppg Industries, Inc. Apparatus and method for monitoring respiration
US5246002A (en) 1992-02-11 1993-09-21 Physio-Control Corporation Noise insensitive pulse transmittance oximeter
EP0760223A1 (en) 1995-08-31 1997-03-05 Hewlett-Packard GmbH Apparatus for monitoring, in particular pulse oximeter
EP0761159B1 (en) 1995-08-31 1999-09-29 Hewlett-Packard Company Apparatus for medical monitoring, in particular pulse oximeter
US5842981A (en) 1996-07-17 1998-12-01 Criticare Systems, Inc. Direct to digital oximeter

Non-Patent Citations (44)

* Cited by examiner, † Cited by third party
Title
Braun, S., et al., "Mechanical Signature Analysis-Theory and Applications," pp. 142-145, 202-203 (1986).
Braun, S., et al., "Mechanical Signature Analysis—Theory and Applications," pp. 142-145, 202-203 (1986).
Brown, D.P., "Evaluation of Pulse Oximeters Using Theoretical Models and Experimental Studies," Master Thesis, University of Washington, (Nov. 25, 1987).
Cohen, Arnon, "Volume I: Time and Frequency Domains Analysis," Biomedical Signal Processing, CRC Press, Inc., Boca Raton, FL, pp. 152-159.
Ferrara, Earl R., "Fetal Electrocardiogram Enhancement by Time-Sequenced Adaptive Filtering", IEEE Transactions on Biomedical Engineering, vol. BME-29, No. 6, Jun. 1982.
Findings of Fact and Conclusions of Law Regarding Masimo's Motion for Preliminary Injunction, signed by District Court Judge on Nov. 2, 2000.
Glover, Jr., John Richard, "Adaptive Noise Canceling of Sinusoidal Interferences," A Dissertation Submitted to the Department of Electrical Engineering and the Committee on Graduate Studies of Stanford University in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy, pp. iii-82 (1975).
Harris, et al., "Digital Signal Processing with Efficient Polyphase Recursive All-Pass Filters," International Conference, Florence, Italy, (Sep. 4-6, 1991).
Haykin, S., "Adaptive Filter Theory," Chapter 9, Prentice Hall, Englewood Cliffs, NJ (1991).
Hendry, S.D., "Computation of Harmonic Comb filter Weights", IEEE Transactions on Signal Procesing, vol. 41, No. 4, Apr. 1993.
Klimasauskas, Casey, "Neural Nets and Noise Filtering," Dr. Dobb's Journal, pp. 32, (Jan. 1989).
Li, Gang, "A Stable and Efficient Adaptive Notch Filter for Direct Frequency Estimation", IEEE Transactions on Signal Processing, vol. 45, No. 8, Aug. 1997.
Melnikof, S., "Neural Networks for Signal Processing: A Case Study," Dr. Dobb's Journal, pp. 36-37, (Jan. 1989).
Mook, G.A., et al., "Spectrophotometric Determination of Oxygen Saturation of Blood Independent of Presence of Idocyanine Green," Cardiovascular Research, vol. 13, pp. 233-2337, (1979).
Mook, G.A., et al., "Wavelength dependency of the spectrophotometric determination of blood oxygen saturation," Clinical Chemistry Acta, vol. 26, pp. 170-173, (1969).
Nehorai, Arye, "A minimal Parameter Adaptive Notch Filter With Constrained Poles and Zeros", IEEE Transactions On Acoustics, Speech, and Signal Processing, vol. ASSP-33, No. 4, Aug. 1985.
Nehorai, Arye, "Adaptive Comb Filtering for Harmonic Signal Enhancement", IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. ASSP-34, No. 5, Oct. 1986.
Nellcor's and Mallinckrodt's Amended and Supplemental Answer and Counterclaims to Masimo Corporation's First Amended Complaint in Case No. CV 01-7292 MRP (AJWx).
Nellcor's and Mallinckrodt's Amended and Supplemental Answer and Counterclaims to Masimo Corporation's First Amended Complaint in Case No. CV-01-7293-MRP (AJWx).
Nellcor's and Mallinckrodt's First Amended and Supplemental Reply and Counterclaims to Counterclaims of Masimo Corporation.
Nellcor's and Mallinckrodt's Opening Claim Construction Brief on the Patents-In-Suit dated Sep. 16, 2002.
Nellcor's and Mallinckrodt's Reply Claim Construction Brief on the Patents-In-Suit dated Oct. 18, 2002.
Nellcor's Third Amended Complaint for Patent Infrigement.
Neuman, Michael R., "Pulse Oximetry: Physical Principles, Technical Realization and Present Limitations," Continuous Transcutaneous Monitoring, pp. 135-144, Plenum Press, New York, (1987).
Non-Confidential Brief of Defendants-Cross Appellants Mallinckrodt Inc. and Nellcor Puritan Bennett, Inc. dated Jan. 22, 2001.
Non-Confidential Reply Brief of Defendants-Cross Appellants Mallinckrodt Inc. and Nellcor Puritan Bennett, Inc. dated Feb. 22, 2001.
Non-Confidential Reply Brief of Plantiff-Appellant Masimo Corporation dated Feb. 6, 2001.
Non-Conifidental Brief of Plaintiff-Appellant Masimo Corporation dated Dec. 8, 2000.
Pau, L.F., "Acoustic and Vibration Monitoring," Failure Diagnosis and Performance Monitoring, Chapter 13, pp. 295-299.
Rabiner, Lawrence, et al., "Theory and Application of Digital signal Processing," p. 260, (1975).
Severinghaus, M.D., J.W., "Pulse Oximetry Uses and Limitations," pp. 104, ASA Convention, New Orleans (1989).
Strobach, Peter, "Single Section Least Squares Adaptive Notch Filter", IEEE Transactions on Signal Processing, vol. 43, No. 8, Aug. 1995.
Tremper, Kevin K., "Pulse Oximetry: Technical Aspects of Machine Design," Advances in Oxygen Monitoring, pp. 137-153, (1987).
United States Court of Appeals for the Federal Circuit-Opinion, Case No. 01-1038, -1084, Masimo v. Mallinckrodt, Inc. and Nellcor Puritan Bennett, Inc., Decided: Aug. 8, 2001.
United States Court of Appeals for the Federal Circuit—Opinion, Case No. 01-1038, -1084, Masimo v. Mallinckrodt, Inc. and Nellcor Puritan Bennett, Inc., Decided: Aug. 8, 2001.
United States District Court-Civil Minutes, Case No. SA CV 99-1245 AHS (Anx), Masimo v. Mallinckrodt and Nellcor Puritan Bennett, Dated: Oct. 4, 2000.
United States District Court—Civil Minutes, Case No. SA CV 99-1245 AHS (Anx), Masimo v. Mallinckrodt and Nellcor Puritan Bennett, Dated: Oct. 4, 2000.
Widrow, Bernard, "Adaptive Signal Processing," Chapter 12, Prentice Hall, Englewood, NJ, (1985).
Widrow, Bernard, Adaptive Noise Cancelling: Principles and Applications:, Proceedings of the IEEE, vol. 63, No. 12, Dec. 1975.
Wukitsch, Michael, W., et al., "Pulse Oximetry: Analysis of Theory, Technology, and Practive," Journal of Clinical Monitoring, vol. 4, No. 4, pp. 290-301 (Oct. 1988).
Yelderman, M., et al., "Sodium Nitroprusside Infusion By Adaptive Control", Adaptive Control VBY Inverse Modeling, Conference Record: 12<th >Asilosor Conference 90 (1978).
Yelderman, M., et al., "Sodium Nitroprusside Infusion By Adaptive Control", Adaptive Control VBY Inverse Modeling, Conference Record: 12th Asilosor Conference 90 (1978).
Yelderman, Mark, et al., "ECG Enhancement by Adaptive Cancellation of Electrosurgical Interference", IEEE Transactions on Biomedical Engineering, vol. BME-30, No. 7, Jul. 1983.
Yu, C., et al., "Improvement in Arterial Oxygen Control Using Multiple Model Adaptive Control Procedures," pp. 878-883.

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8364226B2 (en) 1991-03-07 2013-01-29 Masimo Corporation Signal processing apparatus
US20040204636A1 (en) * 1991-03-07 2004-10-14 Diab Mohamed K. Signal processing apparatus
US20070249918A1 (en) * 1991-03-07 2007-10-25 Diab Mohamed K Signal processing apparatus
US20090099430A1 (en) * 1991-03-07 2009-04-16 Masimo Corporation Signal processing apparatus
US20040204638A1 (en) * 1991-03-07 2004-10-14 Diab Mohamed Kheir Signal processing apparatus and method
US8942777B2 (en) 1991-03-07 2015-01-27 Masimo Corporation Signal processing apparatus
US20060217609A1 (en) * 1991-03-07 2006-09-28 Diab Mohamed K Signal processing apparatus
US8046041B2 (en) 1991-03-07 2011-10-25 Masimo Corporation Signal processing apparatus
US20040068164A1 (en) * 1991-03-07 2004-04-08 Diab Mohamed K. Signal processing apparatus
US7937130B2 (en) 1991-03-07 2011-05-03 Masimo Corporation Signal processing apparatus
US8128572B2 (en) 1991-03-07 2012-03-06 Masimo Corporation Signal processing apparatus
US7962190B1 (en) 1991-03-07 2011-06-14 Masimo Corporation Signal processing apparatus
US20050256385A1 (en) * 1991-03-07 2005-11-17 Diab Mohamed K Signal processing apparatus
US8036728B2 (en) 1991-03-07 2011-10-11 Masimo Corporation Signal processing apparatus
US8948834B2 (en) 1991-03-07 2015-02-03 Masimo Corporation Signal processing apparatus
US8046042B2 (en) 1991-03-07 2011-10-25 Masimo Corporation Signal processing apparatus
US20040210146A1 (en) * 1991-03-07 2004-10-21 Diab Mohamed K. Signal processing apparatus
US20090143657A1 (en) * 1991-03-21 2009-06-04 Mohamed Diab Low-noise optical probes for reducing ambient noise
US8229533B2 (en) 1991-03-21 2012-07-24 Masimo Corporation Low-noise optical probes for reducing ambient noise
US20050043600A1 (en) * 1991-03-21 2005-02-24 Mohamed Diab Low-noise optical probes for reducing ambient noise
US8670814B2 (en) 1991-03-21 2014-03-11 Masimo Corporation Low-noise optical probes for reducing ambient noise
US8560034B1 (en) 1993-10-06 2013-10-15 Masimo Corporation Signal processing apparatus
US8019400B2 (en) 1994-10-07 2011-09-13 Masimo Corporation Signal processing apparatus
US8755856B2 (en) 1994-10-07 2014-06-17 Masimo Corporation Signal processing apparatus
US8359080B2 (en) 1994-10-07 2013-01-22 Masimo Corporation Signal processing apparatus
US8126528B2 (en) 1994-10-07 2012-02-28 Masimo Corporation Signal processing apparatus
US20090182211A1 (en) * 1994-10-07 2009-07-16 Masimo Corporation Signal processing apparatus
US8463349B2 (en) 1994-10-07 2013-06-11 Masimo Corporation Signal processing apparatus
US20040147824A1 (en) * 1995-06-07 2004-07-29 Diab Mohamed Kheir Manual and automatic probe calibration
US8781543B2 (en) 1995-06-07 2014-07-15 Jpmorgan Chase Bank, National Association Manual and automatic probe calibration
US8145287B2 (en) 1995-06-07 2012-03-27 Masimo Corporation Manual and automatic probe calibration
US20090270703A1 (en) * 1995-06-07 2009-10-29 Masimo Corporation Manual and automatic probe calibration
USRE44875E1 (en) 1995-06-07 2014-04-29 Cercacor Laboratories, Inc. Active pulse blood constituent monitoring
US20070112260A1 (en) * 1995-06-07 2007-05-17 Diab Mohamed K Manual and automatic probe calibration
USRE42753E1 (en) 1995-06-07 2011-09-27 Masimo Laboratories, Inc. Active pulse blood constituent monitoring
US7931599B2 (en) 1995-08-07 2011-04-26 Nellcor Puritan Bennett Llc Method and apparatus for estimating a physiological parameter
US20050085735A1 (en) * 1995-08-07 2005-04-21 Nellcor Incorporated, A Delaware Corporation Method and apparatus for estimating a physiological parameter
US7865224B2 (en) 1995-08-07 2011-01-04 Nellcor Puritan Bennett Llc Method and apparatus for estimating a physiological parameter
US20050143634A1 (en) * 1995-08-07 2005-06-30 Nellcor Incorporated, A Delaware Corporation Method and apparatus for estimating a physiological parameter
US20110071375A1 (en) * 1995-08-07 2011-03-24 Nellcor Incorporated, A Delaware Corporation Method and apparatus for estimating physiological parameters using model-based adaptive filtering
US20100056930A1 (en) * 1996-06-26 2010-03-04 Masimo Corporation Rapid non-invasive blood pressure measuring device
US7951086B2 (en) 1996-06-26 2011-05-31 Masimo Corporation Rapid non-invasive blood pressure measuring device
US7041060B2 (en) 1996-06-26 2006-05-09 Masimo Corporation Rapid non-invasive blood pressure measuring device
US20060004293A1 (en) * 1996-06-26 2006-01-05 Flaherty Bryan P Rapid non-invasive blood pressure measuring device
US20060206030A1 (en) * 1996-06-26 2006-09-14 Flaherty Bryan P Rapid non-invasive blood pressure measuring device
US8649839B2 (en) 1996-10-10 2014-02-11 Covidien Lp Motion compatible sensor for non-invasive optical blood analysis
US7720516B2 (en) 1996-10-10 2010-05-18 Nellcor Puritan Bennett Llc Motion compatible sensor for non-invasive optical blood analysis
US9351673B2 (en) 1997-04-14 2016-05-31 Masimo Corporation Method and apparatus for demodulating signals in a pulse oximetry system
US20080036752A1 (en) * 1997-04-14 2008-02-14 Diab Mohamed K Signal processing apparatus and method
US20060200016A1 (en) * 1997-04-14 2006-09-07 Diab Mohamed K Signal processing apparatus and method
US8190227B2 (en) 1997-04-14 2012-05-29 Masimo Corporation Signal processing apparatus and method
US8718737B2 (en) 1997-04-14 2014-05-06 Masimo Corporation Method and apparatus for demodulating signals in a pulse oximetry system
US20040204637A1 (en) * 1997-04-14 2004-10-14 Diab Mohamed K. Signal processing apparatus and method
US8180420B2 (en) 1997-04-14 2012-05-15 Masimo Corporation Signal processing apparatus and method
US9289167B2 (en) 1997-04-14 2016-03-22 Masimo Corporation Signal processing apparatus and method
US8888708B2 (en) 1997-04-14 2014-11-18 Masimo Corporation Signal processing apparatus and method
US20070007612A1 (en) * 1998-03-10 2007-01-11 Mills Michael A Method of providing an optoelectronic element with a non-protruding lens
US20060258923A1 (en) * 1998-06-03 2006-11-16 Ammar Al-Ali Physiological monitor
US7891355B2 (en) 1998-06-03 2011-02-22 Masimo Corporation Physiological monitor
US7894868B2 (en) 1998-06-03 2011-02-22 Masimo Corporation Physiological monitor
US7899507B2 (en) 1998-06-03 2011-03-01 Masimo Corporation Physiological monitor
US20060258924A1 (en) * 1998-06-03 2006-11-16 Ammar Al-Ali Physiological monitor
US20060281983A1 (en) * 1998-06-03 2006-12-14 Ammar Al-Ali Physiological monitor
US8364223B2 (en) 1998-06-03 2013-01-29 Masimo Corporation Physiological monitor
US20060258925A1 (en) * 1998-06-03 2006-11-16 Ammar Al-Ali Physiological monitor
US9492110B2 (en) 1998-06-03 2016-11-15 Masimo Corporation Physiological monitor
US8255028B2 (en) 1998-06-03 2012-08-28 Masimo Corporation, Inc. Physiological monitor
US20050197551A1 (en) * 1998-06-03 2005-09-08 Ammar Al-Ali Stereo pulse oximeter
US8721541B2 (en) 1998-06-03 2014-05-13 Masimo Corporation Physiological monitor
US7761128B2 (en) 1998-06-03 2010-07-20 Masimo Corporation Physiological monitor
US20080132771A1 (en) * 1998-07-04 2008-06-05 Whitland Research Limited Measurement of blood oxygen saturation
US7774037B2 (en) 1998-07-04 2010-08-10 Whitland Research Limited Non-invasive measurement of blood analytes
US20090005663A1 (en) * 1998-07-04 2009-01-01 Edwards Lifesciences Corporation Non-invasive measurement of blood analytes
US6842635B1 (en) * 1998-08-13 2005-01-11 Edwards Lifesciences Llc Optical device
USRE41912E1 (en) 1998-10-15 2010-11-02 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatus
US8706179B2 (en) 1998-10-15 2014-04-22 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatii
USRE41317E1 (en) 1998-10-15 2010-05-04 Masimo Corporation Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices
USRE43860E1 (en) 1998-10-15 2012-12-11 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatus
USRE43169E1 (en) 1998-10-15 2012-02-07 Masimo Corporation Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices
USRE44823E1 (en) 1998-10-15 2014-04-01 Masimo Corporation Universal modular pulse oximeter probe for use with reusable and disposable patient attachment devices
US20050085702A1 (en) * 1998-12-30 2005-04-21 Diab Mohamed K. Plethysmograph pulse recognition processor
US9675286B2 (en) 1998-12-30 2017-06-13 Masimo Corporation Plethysmograph pulse recognition processor
US7988637B2 (en) 1998-12-30 2011-08-02 Masimo Corporation Plethysmograph pulse recognition processor
US20060206021A1 (en) * 1998-12-30 2006-09-14 Diab Mohamed K Plethysmograph pulse recognition processor
US8046040B2 (en) 1999-01-07 2011-10-25 Masimo Corporation Pulse oximetry data confidence indicator
US20060195025A1 (en) * 1999-01-07 2006-08-31 Ali Ammar A Pulse oximetry data confidence indicator
US20040133087A1 (en) * 1999-01-07 2004-07-08 Ali Ammar Al Pulse oximetry data confidence indicator
US9636055B2 (en) 1999-01-07 2017-05-02 Masimo Corporation Pulse and confidence indicator displayed proximate plethysmograph
US8405608B2 (en) 1999-01-25 2013-03-26 Masimo Corporation System and method for altering a display mode
US8532727B2 (en) 1999-01-25 2013-09-10 Masimo Corporation Dual-mode pulse oximeter
US20030197679A1 (en) * 1999-01-25 2003-10-23 Ali Ammar Al Systems and methods for acquiring calibration data usable in a pause oximeter
US7991446B2 (en) 1999-01-25 2011-08-02 Masimo Corporation Systems and methods for acquiring calibration data usable in a pulse oximeter
US20020140675A1 (en) * 1999-01-25 2002-10-03 Ali Ammar Al System and method for altering a display mode based on a gravity-responsive sensor
US9375185B2 (en) 1999-01-25 2016-06-28 Masimo Corporation Systems and methods for acquiring calibration data usable in a pulse oximeter
US9730640B2 (en) 1999-03-25 2017-08-15 Masimo Corporation Pulse oximeter probe-off detector
US8532728B2 (en) 1999-03-25 2013-09-10 Masimo Corporation Pulse oximeter probe-off detector
US20090112073A1 (en) * 1999-03-25 2009-04-30 Diab Mohamed K Pulse oximeter probe-off detector
US8175672B2 (en) 1999-04-12 2012-05-08 Masimo Corporation Reusable pulse oximeter probe and disposable bandage apparatii
US8133176B2 (en) 1999-04-14 2012-03-13 Tyco Healthcare Group Lp Method and circuit for indicating quality and accuracy of physiological measurements
US20110172942A1 (en) * 1999-08-26 2011-07-14 Ammar Al-Ali Systems and methods for indicating an amount of use of a sensor
US20070156034A1 (en) * 1999-08-26 2007-07-05 Al-Ali Ammar Systems and methods for indicating an amount of use of a sensor
US7910875B2 (en) 1999-08-26 2011-03-22 Masimo Corporation Systems and methods for indicating an amount of use of a sensor
US8399822B2 (en) 1999-08-26 2013-03-19 Masimo Corporation Systems and methods for indicating an amount of use of a sensor
US20050143631A1 (en) * 1999-08-26 2005-06-30 Ammar Al-Ali Systems and methods for indicating an amount of use of a sensor
US20060097135A1 (en) * 1999-08-26 2006-05-11 Ammar Al-Ali Systems and methods for indicating an amount of use of a sensor
US20040133088A1 (en) * 1999-12-09 2004-07-08 Ammar Al-Ali Resposable pulse oximetry sensor
US6950687B2 (en) 1999-12-09 2005-09-27 Masimo Corporation Isolation and communication element for a resposable pulse oximetry sensor
US7734320B2 (en) 1999-12-09 2010-06-08 Masimo Corporation Sensor isolation
US9386953B2 (en) 1999-12-09 2016-07-12 Masimo Corporation Method of sterilizing a reusable portion of a noninvasive optical probe
US8000761B2 (en) 1999-12-09 2011-08-16 Masimo Corporation Resposable pulse oximetry sensor
US7039449B2 (en) 1999-12-09 2006-05-02 Masimo Corporation Resposable pulse oximetry sensor
US20060200018A1 (en) * 1999-12-09 2006-09-07 Ammar Al-Ali Resposable pulse oximetry sensor
US8078246B2 (en) 2000-04-17 2011-12-13 Nellcor Puritan Bennett Llc Pulse oximeter sensor with piece-wise function
US7689259B2 (en) 2000-04-17 2010-03-30 Nellcor Puritan Bennett Llc Pulse oximeter sensor with piece-wise function
US8224412B2 (en) 2000-04-17 2012-07-17 Nellcor Puritan Bennett Llc Pulse oximeter sensor with piece-wise function
US20090204371A1 (en) * 2000-06-05 2009-08-13 Masimo Corporation Variable indication estimator
US9138192B2 (en) 2000-06-05 2015-09-22 Masimo Corporation Variable indication estimator
US8489364B2 (en) 2000-06-05 2013-07-16 Masimo Corporation Variable indication estimator
US7873497B2 (en) 2000-06-05 2011-01-18 Masimo Corporation Variable indication estimator
US8260577B2 (en) 2000-06-05 2012-09-04 Masimo Corporation Variable indication estimator
US20110112799A1 (en) * 2000-06-05 2011-05-12 Masimo Corporation Variable indication estimator
US20060161389A1 (en) * 2000-06-05 2006-07-20 Weber Walter M Variable indication estimator
US6999904B2 (en) 2000-06-05 2006-02-14 Masimo Corporation Variable indication estimator
US7801581B2 (en) 2000-08-18 2010-09-21 Masimo Laboratories, Inc. Optical spectroscopy pathlength measurement system
US20070083093A1 (en) * 2000-08-18 2007-04-12 Diab Mohamed K Optical spectroscopy pathlength measurement system
US20050020893A1 (en) * 2000-08-18 2005-01-27 Diab Mohamed K. Optical spectroscopy pathlength measurement system
US6985764B2 (en) 2001-05-03 2006-01-10 Masimo Corporation Flex circuit shielded optical sensor
US20060084852A1 (en) * 2001-05-03 2006-04-20 Gene Mason Flex circuit shielded optical sensor
US20110160552A1 (en) * 2001-06-29 2011-06-30 Weber Walter M Sine saturation transform
US8498684B2 (en) 2001-06-29 2013-07-30 Masimo Corporation Sine saturation transform
US20080045810A1 (en) * 2001-06-29 2008-02-21 Weber Walter M Sine saturation transform
US20050131285A1 (en) * 2001-06-29 2005-06-16 Weber Walter M. Signal component processor
US8892180B2 (en) 2001-06-29 2014-11-18 Masimo Corporation Sine saturation transform
US7373194B2 (en) 2001-06-29 2008-05-13 Masimo Corporation Signal component processor
US9814418B2 (en) 2001-06-29 2017-11-14 Masimo Corporation Sine saturation transform
US20030055325A1 (en) * 2001-06-29 2003-03-20 Weber Walter M. Signal component processor
US7904132B2 (en) 2001-06-29 2011-03-08 Masimo Corporation Sine saturation transform
US6850787B2 (en) 2001-06-29 2005-02-01 Masimo Laboratories, Inc. Signal component processor
US8457703B2 (en) 2001-07-02 2013-06-04 Masimo Corporation Low power pulse oximeter
US9848806B2 (en) 2001-07-02 2017-12-26 Masimo Corporation Low power pulse oximeter
US20040181133A1 (en) * 2001-07-02 2004-09-16 Ammar Al-Ali Low power pulse oximeter
US20080064936A1 (en) * 2001-07-02 2008-03-13 Ammar Al-Ali Low power pulse oximeter
US20030212312A1 (en) * 2002-01-07 2003-11-13 Coffin James P. Low noise patient cable
US9364181B2 (en) 2002-01-08 2016-06-14 Masimo Corporation Physiological sensor combination
US6934570B2 (en) 2002-01-08 2005-08-23 Masimo Corporation Physiological sensor combination
US20050277819A1 (en) * 2002-01-08 2005-12-15 Kiani Massi E Physiological sensor combination
US20050083193A1 (en) * 2002-01-24 2005-04-21 Ammar Al-Ali Parallel measurement alarm processor
US7030749B2 (en) 2002-01-24 2006-04-18 Masimo Corporation Parallel measurement alarm processor
US8570167B2 (en) 2002-01-24 2013-10-29 Masimo Corporation Physiological trend monitor
US7880606B2 (en) 2002-01-24 2011-02-01 Masimo Corporation Physiological trend monitor
US20080228052A1 (en) * 2002-01-24 2008-09-18 Ammar Al-Ali Physiological trend monitor
US20060192667A1 (en) * 2002-01-24 2006-08-31 Ammar Al-Ali Arrhythmia alarm processor
US20110124990A1 (en) * 2002-01-24 2011-05-26 Ammar Al-Ali Physiological trend monitor
US9636056B2 (en) 2002-01-24 2017-05-02 Masimo Corporation Physiological trend monitor
US8228181B2 (en) 2002-01-24 2012-07-24 Masimo Corporation Physiological trend monitor
US9131883B2 (en) 2002-01-24 2015-09-15 Masimo Corporation Physiological trend monitor
US6822564B2 (en) 2002-01-24 2004-11-23 Masimo Corporation Parallel measurement alarm processor
US20030218386A1 (en) * 2002-01-25 2003-11-27 David Dalke Power supply rail controller
US20060052680A1 (en) * 2002-02-22 2006-03-09 Diab Mohamed K Pulse and active pulse spectraphotometry
US6961598B2 (en) 2002-02-22 2005-11-01 Masimo Corporation Pulse and active pulse spectraphotometry
US8606342B2 (en) 2002-02-22 2013-12-10 Cercacor Laboratories, Inc. Pulse and active pulse spectraphotometry
US20030220576A1 (en) * 2002-02-22 2003-11-27 Diab Mohamed K. Pulse and active pulse spectraphotometry
US20030167391A1 (en) * 2002-03-01 2003-09-04 Ammar Al-Ali Encryption interface cable
US8548548B2 (en) 2002-03-25 2013-10-01 Masimo Corporation Physiological measurement communications adapter
US9113832B2 (en) 2002-03-25 2015-08-25 Masimo Corporation Wrist-mounted physiological measurement device
US9795300B2 (en) 2002-03-25 2017-10-24 Masimo Corporation Wearable portable patient monitor
US9113831B2 (en) 2002-03-25 2015-08-25 Masimo Corporation Physiological measurement communications adapter
US7844314B2 (en) 2002-03-25 2010-11-30 Masimo Corporation Physiological measurement communications adapter
US6850788B2 (en) 2002-03-25 2005-02-01 Masimo Corporation Physiological measurement communications adapter
US7844315B2 (en) 2002-03-25 2010-11-30 Masimo Corporation Physiological measurement communications adapter
US9788735B2 (en) 2002-03-25 2017-10-17 Masimo Corporation Body worn mobile medical patient monitor
US9872623B2 (en) 2002-03-25 2018-01-23 Masimo Corporation Arm mountable portable patient monitor
US20040039272A1 (en) * 2002-08-01 2004-02-26 Yassir Abdul-Hafiz Low noise optical housing
US20070073127A1 (en) * 2002-09-25 2007-03-29 Kiani Massi E Parameter compensated physiological monitor
US20040242980A1 (en) * 2002-09-25 2004-12-02 Kiani Massi E. Parameter compensated physiological monitor
US7274955B2 (en) 2002-09-25 2007-09-25 Masimo Corporation Parameter compensated pulse oximeter
US20040122301A1 (en) * 2002-09-25 2004-06-24 Kiani Massl E. Parameter compensated pulse oximeter
US7142901B2 (en) 2002-09-25 2006-11-28 Masimo Corporation Parameter compensated physiological monitor
US8483790B2 (en) 2002-10-18 2013-07-09 Covidien Lp Non-adhesive oximeter sensor for sensitive skin
US20040107065A1 (en) * 2002-11-22 2004-06-03 Ammar Al-Ali Blood parameter measurement system
US7027849B2 (en) 2002-11-22 2006-04-11 Masimo Laboratories, Inc. Blood parameter measurement system
US7440787B2 (en) 2002-12-04 2008-10-21 Masimo Laboratories, Inc. Systems and methods for determining blood oxygen saturation values using complex number encoding
US6970792B1 (en) 2002-12-04 2005-11-29 Masimo Laboratories, Inc. Systems and methods for determining blood oxygen saturation values using complex number encoding
US8948835B2 (en) 2002-12-04 2015-02-03 Cercacor Laboratories, Inc. Systems and methods for determining blood oxygen saturation values using complex number encoding
US8447374B2 (en) 2002-12-04 2013-05-21 Ceracor Laboratories, Inc. Systems and methods for determining blood oxygen saturation values using complex number encoding
US9622693B2 (en) 2002-12-04 2017-04-18 Masimo Corporation Systems and methods for determining blood oxygen saturation values using complex number encoding
US20060080047A1 (en) * 2002-12-04 2006-04-13 Diab Mohamed K Systems and methods for determining blood oxygen saturation values using complex number encoding
US20090259115A1 (en) * 2002-12-04 2009-10-15 Diab Mohamed K Systems and methods for determining blood oxygen saturations values using complex number encoding
US8921699B2 (en) 2002-12-19 2014-12-30 Masimo Corporation Low noise oximetry cable including conductive cords
US7225006B2 (en) 2003-01-23 2007-05-29 Masimo Corporation Attachment and optical probe
US20050245797A1 (en) * 2003-01-24 2005-11-03 Ammar Al-Ali Optical sensor including disposable and reusable elements
US20070244378A1 (en) * 2003-01-24 2007-10-18 Masimo Corporation Noninvasive oximetry optical sensor including disposable and reusable elements
US8244325B2 (en) 2003-01-24 2012-08-14 Cercacor Laboratories, Inc. Noninvasive oximetry optical sensor including disposable and reusable elements
US9693719B2 (en) 2003-01-24 2017-07-04 Masimo Corporation Noninvasive oximetry optical sensor including disposable and reusable elements
US20040147822A1 (en) * 2003-01-24 2004-07-29 Ammar Al-Ali Optical sensor including disposable and reusable elements
US8781549B2 (en) 2003-01-24 2014-07-15 Cercacor Laboratories, Inc. Noninvasive oximetry optical sensor including disposable and reusable elements
US8255029B2 (en) 2003-02-27 2012-08-28 Nellcor Puritan Bennett Llc Method of analyzing and processing signals
US9198616B2 (en) 2003-02-27 2015-12-01 Nellcor Puritan Bennett Ireland Method of analyzing and processing signals
US9220459B2 (en) 2003-02-27 2015-12-29 Nellcor Puritan Bennett Ireland Method of analyzing and processing signals
US9220460B2 (en) 2003-02-27 2015-12-29 Nellcor Puritan Bennett Ireland Method of analyzing and processing signals
US9192336B2 (en) 2003-02-27 2015-11-24 Nellcor Puritan Bennett Ireland Method of analyzing and processing signals
US20050055276A1 (en) * 2003-06-26 2005-03-10 Kiani Massi E. Sensor incentive method
US7003338B2 (en) 2003-07-08 2006-02-21 Masimo Corporation Method and apparatus for reducing coupling between signals
US9801588B2 (en) 2003-07-08 2017-10-31 Cercacor Laboratories, Inc. Method and apparatus for reducing coupling between signals in a measurement system
US9084569B2 (en) 2003-07-08 2015-07-21 Cercacor Laboratories, Inc. Method and apparatus for reducing coupling between signals in a measurement system
US8676286B2 (en) 2003-07-08 2014-03-18 Cercacor Laboratories, Inc. Method and apparatus for reducing coupling between signals in a measurement system
US7865222B2 (en) 2003-07-08 2011-01-04 Masimo Laboratories Method and apparatus for reducing coupling between signals in a measurement system
US8920317B2 (en) 2003-07-25 2014-12-30 Masimo Corporation Multipurpose sensor port
US20050075548A1 (en) * 2003-07-25 2005-04-07 Ammar Al-Ali Multipurpose sensor port
US7500950B2 (en) 2003-07-25 2009-03-10 Masimo Corporation Multipurpose sensor port
US8385995B2 (en) 2003-08-28 2013-02-26 Masimo Corporation Physiological parameter tracking system
US20080027294A1 (en) * 2003-08-28 2008-01-31 Ammar Al-Ali Physiological parameter tracking system
US9788768B2 (en) 2003-08-28 2017-10-17 Masimo Corporation Physiological parameter tracking system
US7254431B2 (en) 2003-08-28 2007-08-07 Masimo Corporation Physiological parameter tracking system
US20050090724A1 (en) * 2003-08-28 2005-04-28 Ammar Al-Ali Physiological parameter tracking system
US7254434B2 (en) 2003-10-14 2007-08-07 Masimo Corporation Variable pressure reusable sensor
US20050085704A1 (en) * 2003-10-14 2005-04-21 Christian Schulz Variable pressure reusable sensor
US9072474B2 (en) 2003-11-05 2015-07-07 Masimo Corporation Pulse oximeter access apparatus and method
US7483729B2 (en) 2003-11-05 2009-01-27 Masimo Corporation Pulse oximeter access apparatus and method
US20050101848A1 (en) * 2003-11-05 2005-05-12 Ammar Al-Ali Pulse oximeter access apparatus and method
US20090137885A1 (en) * 2003-11-05 2009-05-28 Ammar Al-Ali Pulse oximeter access apparatus and method
US9743887B2 (en) 2003-11-05 2017-08-29 Masimo Corporation Pulse oximeter access apparatus and method
US20050101849A1 (en) * 2003-11-07 2005-05-12 Ammar Al-Ali Pulse oximetry data capture system
US7373193B2 (en) 2003-11-07 2008-05-13 Masimo Corporation Pulse oximetry data capture system
US20050197550A1 (en) * 2004-01-05 2005-09-08 Ammar Al-Ali Pulse oximetry sensor
US7280858B2 (en) 2004-01-05 2007-10-09 Masimo Corporation Pulse oximetry sensor
US20050187440A1 (en) * 2004-02-20 2005-08-25 Yassir Abdul-Hafiz Connector switch
US7371981B2 (en) 2004-02-20 2008-05-13 Masimo Corporation Connector switch
US8337403B2 (en) 2004-03-04 2012-12-25 Masimo Corporation Patient monitor having context-based sensitivity adjustments
US9161713B2 (en) 2004-03-04 2015-10-20 Masimo Corporation Multi-mode patient monitor configured to self-configure for a selected or determined mode of operation
US20090048495A1 (en) * 2004-03-04 2009-02-19 Masimo Corporation Application identification sensor
US7438683B2 (en) 2004-03-04 2008-10-21 Masimo Corporation Application identification sensor
US20050197555A1 (en) * 2004-03-06 2005-09-08 Calisto Medical, Inc. Methods and devices for non-invasively measuring quantitative information of substances in living organisms
US7395104B2 (en) * 2004-03-06 2008-07-01 Calisto Medical, Inc. Methods and devices for non-invasively measuring quantitative information of substances in living organisms
US8721542B2 (en) 2004-03-08 2014-05-13 Masimo Corporation Physiological parameter system
US20080300471A1 (en) * 2004-03-08 2008-12-04 Masimo Corporation Physiological parameter system
US20050203352A1 (en) * 2004-03-08 2005-09-15 Ammar Al-Ali Physiological parameter system
US7415297B2 (en) 2004-03-08 2008-08-19 Masimo Corporation Physiological parameter system
US7292883B2 (en) 2004-03-31 2007-11-06 Masimo Corporation Physiological assessment system
US20060009687A1 (en) * 2004-03-31 2006-01-12 Claudio De Felice Physiological assessment system
US8641631B2 (en) 2004-04-08 2014-02-04 Masimo Corporation Non-invasive monitoring of respiratory rate, heart rate and apnea
US9339220B2 (en) 2004-07-07 2016-05-17 Masimo Corporation Multi-wavelength physiological monitor
US8423106B2 (en) 2004-07-07 2013-04-16 Cercacor Laboratories, Inc. Multi-wavelength physiological monitor
US20080154104A1 (en) * 2004-07-07 2008-06-26 Masimo Laboratories, Inc. Multi-Wavelength Physiological Monitor
US20110237911A1 (en) * 2004-07-07 2011-09-29 Masimo Laboratories, Inc. Multiple-wavelength physiological monitor
US9341565B2 (en) 2004-07-07 2016-05-17 Masimo Corporation Multiple-wavelength physiological monitor
US7937128B2 (en) 2004-07-09 2011-05-03 Masimo Corporation Cyanotic infant sensor
US20110208025A1 (en) * 2004-07-09 2011-08-25 Ammar Al-Ali Cyanotic infant sensor
US9480422B2 (en) 2004-07-09 2016-11-01 Masimo Corporation Cyanotic infant sensor
US20060020185A1 (en) * 2004-07-09 2006-01-26 Ammar Al-Ali Cyanotic infant sensor
US8682407B2 (en) 2004-07-09 2014-03-25 Masimo Corporation Cyanotic infant sensor
US8306596B2 (