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`US 20090105556Al
`
`(19) United States
`(12) Patent Application Publication (10) Pub. No.: US 2009/0105556 A1
`Fricke et al.
`(43) Pub. Date:
`Apr. 23, 2009
`
`[54) MEASUREMENT OF PHYSIOLOGICAL
`SIGNALS
`
`(75)
`
`inventors:
`
`John Robert I"ricke. Lexington.
`MA (US): Matthew Corbin
`Wiggins. Concord. MA (US)
`
`(‘o rrcspondencc Address:
`()CCIIIU'I‘I ROIIIJCICK & TSAO, LIP
`It] FAWCETT STREET
`CAMBRIDGE, MA 02138 (US)
`
`Assignee:
`
`'I'lax LLC. Cambridge. MA (US)
`
`App1.No.:
`
`12/240,651
`
`Filed:
`
`Sep. 29, 2008
`
`Related U.S.Applieation Data
`
`Provisional application No. 60/995723. filed on Sep.
`28. 200?,
`
`A system includes an optical sensor and a signal processing
`module. The optical sensor is configured to be positioned on
`an area oi'skitt oi'a patient. The optical sensor includes a light
`source for illuminating a capillary bed in the area oi'skin and
`a photodetector. The photodetcctor is configured to receive illl
`optical signal l'rom the capillary bed resulting from the illu-
`mination and to convert the optical signal into an electricle
`signal. the optical signal characterizing a fluctuation in a level
`of blood in the capillary bed. The signal processing module is
`configured to process the eIectrie signal using a nonstationary
`frequency estimation method to obtain a processed signal
`related to at least one Ufa heart rate and a respiration rate of
`the patient. Another aspect relates to obtaining a quantity
`related to the blood pressure of the patient in addition to or
`instead ofoblaiaing a processed signal related to at least one
`of the heart rate and the respiration rate of the patient.
`
`US. Patent No. 8,929,965
`
`Publication Classification
`
`Int. (11.
`A613 5/00
`A613 5/I455
`U.S. Cl.
`
`(so
`
`(52)
`(so
`
`(2006.01)
`(2006,01)
`
`ABSTRACT
`
`6001301: fitiflfllfl
`
`Apple Inc.
`APL1016
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`Apple Inc.
`APL1016
`U.S. Patent No. 8,929,965
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`US 2009i’0105556 A1
`
`Apr. 23, 2009
`
`MEASUREMENT OF PHYSIOLOGICAL
`SIGNALS
`
`CROSS-R101 liRl-iNCli T0 Rl-EIA'I‘IED
`APPUCA’I‘IONS
`
`[0001] This application claims priority to US. provisional
`application No. 601995.723. filed Sep. 28. 2003’. entitled
`“Method and Devices for Measurement of Matti-modal
`Physiological Signals." which is incorporated herein by ref-
`CI'CI'ICC.
`
`O22
`
`ofvolurne and pressure ofthe thoracic cavity orto a change in
`at least one ofvolume and pressure ot'an organ in the thoracic
`cavity.
`[0007] The system includes an auxiliary sensor configured
`to detect an ambient signal. The auxiliary sensor includes at
`least one o faccelemmeter. a pressure sensor. an optical detec-
`tor. a temperature sensor, and a piezoelectric device. The
`signal processing module is configured to remove an effect ol'
`the ambient signal from the electrical signal. The optical
`signal is a reflectance or a transmittance of the capillary bed.
`[0008]
`In another general aspect. a method includes illtuni -
`nating a capillary bed in an area of skin ofa patient. receiving
`an optical signal from the capillary bed resulting ii'om the
`illumination. converting the optical signal into an electrical
`signal. and processing the electrical signal usinga nonstation—
`ary frequency estimation method to obtaina processed signal
`related to at least one of a heart rate and a respiration rate of
`the patient. The optical signal characterinss a fluctuation in a
`level of blood in the capillary bed.
`[0009] Embodiments may include one or more of the fol—
`lowing. The method includes outputting infomiation deter-
`mined thorn the processed signal Processing the electrical
`signal using the nonstationary freqttency estimation method
`includes performing a Ililbcrt transform or processing the
`electrical signal using an instantaneous frequency estimation
`method. Processing the electrical signal using the instanta-
`neous frequency method includes band pass filtering the elec-
`trical signal. deten'nining an instantaneous frequency of the
`electrical signal. and using the instantaneous frequency to
`obtain the processed signal.
`[0010] The method further includes processing the electri-
`cal signat using a mode] to obtain a blood pressure signal
`related to a blood pressure of the patient. The optical signal
`characterizes a capillary refill time in the capillary bed. Pro-
`cessing the electrical signal includes processingtlte electrical
`signal in real time.
`[0011]
`In another aspect, a method for monitoring blood
`prossure includes illuminating a capillary bed in an area of
`skin Ufa patient. receiving an optical signal from the capillary
`bed resulting from the illumination, converting the optical
`signal into an electrical signal. and processing the electrical
`signal using a model characterizing a relationship ofthe fluc-
`tuation in the level of blood and the blood pressure of the
`patient to obtain a qualitin related to the blood pressure of the
`patient. The optical signal characterizes a fluctuation in a
`level of blood in the capillary bed ofthe patient.
`[0012] Embodiments may include one or more of the fol-
`lowing. The method includes outputting intbrmation deter—
`mined based on the quantity related to the blood pressure of
`the patient. The optical signal characterizes a capillary refill
`time. The method further includes engaging a device to
`restrict circulation in the capillary bed of the patient and
`disengaging the device prior to receiving the optical signal
`from the capillary bed. The disengaging ot' the device occurs
`gradually. The device is an active clamping device.
`[0013] The quantity related to the blood pressure of the
`patient is a quantity related to the continuous blood pressure
`ofthe patient. Applying the model includes applying a model
`including circuit elements or properties of the capillary bed.
`The method further includes calibrating the model on the
`basis ofa blood pressure ofthe patient determined by using a
`blood pressure cufi‘.
`
`STATEMENT REGARDING FEDERALLY
`SPONSORED RESEARCH
`
`[0002] The subject matter described in this application was
`partially funded by the (loveminent of the United States
`under Contract No. W9IZLKw04-Ps0239 awarded by the
`U.S. Department of the Army. The government has certain
`rights in the invention.
`
`FIELD OF THE INVENTION
`
`[00 03] The invention relates to measurement ofpltysiologi-
`cal signals.
`
`BACKGROUND
`
`Physiological signals are important for monitoring a
`[0004]
`subject’s physical and cognitive state. Otien. heart rate
`parameters are measured directly via electrocardiogram
`(ECG) measurements of a heart beat. Respiration rate data
`can be obtained from a respiration] chest strap. Physiological
`signals can also be extracted From infrared (IR) pliotoplethys-
`mographs (PPG). The signals of interest include heart rate.
`respiration rate, continuous blood pressure, and intrathoracic
`pressure. With respect to blood pressure. there is technology
`related to collecting data at two locations on the body and
`using pulse transit time and other parameters as the basis of
`the pressure estimate.
`
`SUMMARY
`
`In a general aspect. a system includes an optical
`[0005]
`sensor and a signal processing module. The optical sensor is
`configured to be posit toned on an area ol'skin ot‘a patient. The
`optical sensor includes a light source for illuminating a cap-
`illary bed in the area of skin and a photodetector. The photo-
`detector is configured to receive an optical signal from the
`capillary bed resulting from the illumination and to convert
`the optical signal into an electrical signal. the optical signal
`characterizing a fluctuation in a level ofblood in the capillary
`bed. The signal processing module is configured to process
`the electric signal using a nonstationary li'equency estimation
`method to obtain a processed signal related to at least one of
`a heart rate and a respiration rate ofthe patient.
`[0006] Embodiments may include one or more of the fol—
`lowing. The system includes an output for providing infor-
`mation determined from the processed signal. The nonsta-
`tionary frequency estimation method includes a Hilbert
`transfonn method or an instantaneous frequency estimation
`method. The processed signal includes at least one ot‘instan-
`taneons heart rate. intersbeat interval. heart rate variability.
`high—low heart rate ratios, respiration rate. inter-breath inter-
`val, and respiration rate variability. The fluctuation in the level
`ofblood in the capillary bed relates to a change in at least one
`
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`US 2009i’0105556 A1
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`Apr. 23, 2009
`
`located such that it cam obtain a measurement via the skin that
`
`[0014] Embodiments may include one or more of the fol-
`lowing advantages.
`[0015] A system or method as described above can be used
`for both military and civilian applications. Combat casualty
`care requires close monitoring ofvital signs from the moment
`that a medic first attends to a wounded soldier in the battle:
`field and thence through the many transfer stages to the point
`of full hospital care. generally removed from the combat
`scene. Similar needs are evident in the civilian community
`where critical care is administered by first responders at the
`scene ofaccidcnts. by emergency room staff, and by intensive
`care unit staff. It is often desirable to obtain maximum infor-
`mation using as little equipment as possible. The system and
`method described herein support this need. They reduce the
`burden of equipment logistics. the burden of extra wires and
`sensors on and around the patient, and the complexity and
`cost of rising multiple devices.
`[0016]
`For both military and civilian applications. a dispos-
`able. wearable device in keeping with the system and method
`described herein can be adapted to stay with a patient and to
`report vital signs throughout the care and transport processes.
`Further. the system can be configured to provide medical
`personnel with real-time visibility of vital signs as well as
`recording ofthis infonnation for playback by attending medi—
`cal staff at a later time. The disposability of the device allows
`it to be fabricated with low cost parts and eliminates the need
`for sanitization and asset tracking logistics irt large scale
`clinical or military uses.
`[0017]
`Such system and methods additionally support
`applications in fitness monitoring. where theirease ofuse and
`robustness make them a compelling alternative to chest strap
`monitors for the rnonitoringol'cardiac and respiratoryparam-
`ctet's during exercise. An ear-worn device can also integrate a
`speaker unit for mobile electronic devices such as mobile
`phones or music players.
`[0018] An advantage of applying a nonstationary fre-
`quency estimation method (e.g.. analysis involving monitor-
`ing the frequency changes of the signal over time. such as
`monitoring changes in the instantaneous principal frequency
`over time) is that it is possible to avoid a tradeoff inherent in
`many stationary estimation methods between frequency reso-
`lution and duration of data signals being analyzed. For
`example. ifthe signal is assumed to be stationary within each
`ofa series of data windows. the frequency resolution is gen-
`erally inversely proportional to the duration of the window.
`As the window duration increases. the assumption of a sta—
`tionary signal is increasingly violated andfor nonstationary
`events (cg. transients) are more difficult to detect. At least
`sortie nonstationary frequency analysis methods. which may
`be based. without limitation. on a Hilbert transform approach.
`tracking of a nonstationary model. nonstationary principal
`frequency analysis. or other time-frequency methods. miti-
`gate the effects of such a time-frequency tradeoll‘. Further-
`more. use 0 fsuch nonstalionary techniques. as opposed to use
`of time domain peak picking and/or threshold based tech-
`niques. can provide robustness ofalgorithm against artifacts.
`and provide sensitivity to periodicity without being burdened
`by a window that can reduce the time resolution.
`[0019] Other features and advantages are apparent from the
`following description and front the appended claims.
`
`FIG. 2 is a graph ol‘a PPG detector signal taken over
`[0021]
`a 25 second period by an earlobe PPG device.
`[0022]
`FIG. 3 is a flow diagram of signal processing of a
`detector signal from a PPG device to obtain heart rate and
`respiration rate parameters.
`[0023]
`FIG . 4 is a graph ofa result oi'bandupass filtering the
`data shown in FIG. 2 between 0.5 Hz and 5.5 Hz to extract a
`cardiac signal.
`[0024]
`FIG. Sis a graph ofa result ofband-pass filtering the
`data shown in FIG. 2 between 0.17 Hz and 0.5 Hz to extract a
`respiration signal.
`[0025]
`FIG. 6 is a graphofan inter-boat interval obtained by
`applying an instantaneous frequency method to the cardiac
`signal shown in FIG. 4.
`[0026]
`FIG. 7 is a graph ol‘a spectral analysis of the inter-
`beat interval data shown in FIG. 6.
`[0027]
`FIG. 8 is. a graph of the respiration rate obtained by
`applying an instantaneous frequency method to the respira-
`tion signal shown in FIG. 5.
`[0028]
`FIG. 9 is a diagram ofl’PG measurements related to
`physiological states used to determine intrathoracic pressure.
`[0029]
`FIG. 10 is a graph ofthe output ol'a matched filter-
`ing process using the PPG detector signal shown in FIG. 2 and
`a pulse pilot signal.
`[0030]
`FIG. 11 is a block diagram of a least mean squares
`[1.MS) adaptive filter.
`[0031]
`FIG. 12 is a schematic diagram of an active clamp-
`ing mechanism used to stimulate capillary refill.
`[0032]
`FIG. 13 is a diagram ot‘a system model relating a
`PPG signal to blood pressure.
`[0033]
`FIG. 14 is a graph oftrends in various physiological
`parameters before and during a stress event.
`[0034]
`1"IG. 15 is a block diagram of a portable electronics
`unit.
`
`FIG. 16 is a flow diagram of methods to estimate a
`[0035]
`heart rate and a respiration rate.
`[0036]
`FIG. 17 is a flow diagram of a processing delay in
`the estimation ot'a heart rate.
`[0037]
`FIG. 18 is a [low diagram ofa processing delay in a
`first method for the estimation of a respiration rate.
`[0038]
`FIG. 19 is a flow diagram ofa processing delay in a
`second method for the estimation oi'a respiration rate.
`[0039]
`FIG. 20 is a flow diagram ofa processing delay in a
`third method for the estimation of a respiration rate.
`
`Dl'i'l'MLF-D Dl-ISCRIPTION
`
`[0040] Referring to FIG. 1. examples of an infrared phoa
`toplethysmograph (PPG ) device 100 are used to obtainplrysi-
`ological signals related to one or more of heart rate. reSpira-
`tion rate. blood pressure. and intralhoracic pressure. Such
`signals may be relevant for monitoring a person‘s state.
`including one or more of the person’s physical state, long—
`terrn health. psychological state. andlor cognitive state. More
`generally. the physiological signals may provide information
`about the activity of the person‘s sympathetic and parasym—
`pathetic nervous system. The PPG device 100 illustrated in
`FIG. 1 is attached to an earlobe 102 of a person. for example.
`using a clamping or adhesive approach. However. in other
`embodiments. PPG device 100 is used on other areas of the
`skin ol'a person, including but not limited to a portion of a
`forehead. a neck. an arm. a foreamt. a finger. a leg. a back. an
`abdomen. or a stomach. In general. a requirement for the
`positioning of PPG device 100 is that the PPG sensor be
`
`BRIEF DESCRIPTION 01" DRAWINGS
`
`[0021]] HG. l is a schematic diagram ofa photoplethysnlo-
`graph (PPG} sensor system.
`
`023
`
`FITBIT, Ex. 1016
`
`

`

`US 2009i’0105556 Al
`
`Apr. 23. 2009
`
`is related to blood flow or pressure. forcxample to measure a
`level of blood in a capillary bed 104. for example. a blood
`volume. a rate of blood flow. or a rate of change of blood
`volume. Note also that the approach is not limited to use of a
`single PPG device on an individual. In some embodiments,
`multiple PPG devices are used. for example. on the torso
`andlor at different extremities. and signals obtained at the
`different PPG devices are processed independently or in com-
`bination to determine underlying characteristics of the indi-
`viduals state.
`
`{3}
`
`In some embodiments. such as that shownin FIG. 1.
`[0041]
`an infrared light source 106 illuminates the earlobe 102. The
`blood level in capillary bed 104 affects the amount of light
`108 that is backseattered or reflected by earlobe 102. Light
`108 backscattcrod by earlobe 102 is received by an optical
`transducer such as a photodetector 110 and converted into a
`detector signal 112. Since the blood flow in capillary bed 104
`is controlled by the heart beat ofthe person and thus the blood
`level in the capillary bed varies with time. the backseattcrcd
`light 108 and hence the detector signal 112 are also tittie-
`varying. In another embodiment. the PPG sensor operates in
`transmission mode and the light transmitted through the cap-
`illary bed is received by the photodetector,
`[0042] The detector signal 112 is sent to a signal processing
`unit 114 which processes the detector signal. which contains
`information about the person’s pulse. to extract desired physi-
`ological data. in various embodiments including one or more
`ol'instantaneous heart rate. inter-beat interval. heart rate vari-
`ability. high-low heart rate ratio. respiration rate. inter-breath
`interval. respiration rate variability. blood pressure. and
`intrathoracic pressure. A single I‘PG device 100. referred to
`below as an integrated Matti—Modal Physiological Sensor
`(IMMPS). is capable of producing multiple (or all) of such
`types of physiological data.
`[0043]
`In some embodiments. the [’I’G device 100 provides
`real-time visibility of physiological parameters and vital
`signs. which can be transmitted to other equipment for real—
`time processing or for playback or off—line processing at a
`later time. In some embodiments. the PPG device includes
`user output devices. such as a set of light emitting diodes
`[I..F.Ds) (cg... a red LEI) ] 16. a yellow LEI.) 118. and a green
`LED 120) or an audio device for producing alert sounds.
`which provide (tn-device status on PPG device 100. As an
`example for use of such output devices. when a selected
`physiological parameter is in a normal range. green LED 120
`is turned on: when the physiological parameter is in a slightly
`abnormal range. yellow LED 118 is tumed on; when the
`physiological parameter is ina dangerous range. red LED 1 16
`is turned on. In seine embodiments. the audio output device is
`used to provide other audio output. such as the output for an
`electronic device such as a mobile phone or a music player. In
`some embodiments. a wireless link 122 to an external moni—
`toring system 124. such as a bedside system or a wearable
`system. provides sensor data to the external system enabling
`a nttmeric readout 126 of various physiological parameters.
`In some embodiments. the PPG device. or at least some
`wearable portion ofthe device. is disposable. In such dispos—
`able entbodiments. the bedside system can be designed to be
`sterilized and reused: in another embodiment. the bedside
`system itself is also disposable. In some embodiments. the
`bedside system includes or conmtunicates with a centralized
`monitoring system that monitors PPG devices of multiple
`patients.
`
`the pliotodetcctor based
`In sortie embodiments.
`[0044]
`detector signal is augmented with other signals. for example.
`accelerometer or pressure sensor signals. For example. aux—
`iliary sensors 130 are connected to signal processing unit 114
`via a wired connection 132. In other embodiments. auxiliary
`sensors 130 are connected to signal processing unit 114 via a
`wireless connection. Auxiliary sensors 130. such as tempera-
`ture sensors. accelerometers. pressure transducers. optical
`detectors. or piezoelectric films or matrices can provide aux-
`iliary signals 132 related to ambient sources ofnoise to signal
`processing unit 114. Signal processing Limit 114 incorporates
`auxiliary signals 132 into the signal processing. for example.
`to increase the signal-to-noiseratio ofthe desired physiologi-
`cal data.
`
`Heru't and Respiration Rate Signals
`
`[0045] Referring to FIG. 2, in an embodiment of the PPG
`device that uses a photodetector signal obtained from at an
`earlobe location. a detector signal 200 obtained front the PPG
`device 100 has a high-frequency pulse signal 202 whose local
`peaks in the time domain have a one-to-one correspondence
`with cardiac beats. Significant low-frequency amplitude vari-
`ability in the detector signal is due in large part a respiration
`signal 204. which modulates the baseline of the pulse signal.
`[0046] The time varying heart and rcspir'dt ion components
`in detector signal 200 can be modeled as
`stn=ilfltncos [(nm'rtrmu’ff T)]+:IRt't]co.s [01,.“th ?)]+
`NH).
`
`I”
`
`where Ana} is the atnplitude modulation ol'pulse signal 202.
`uiH(t) is the frequency modulation ofpulse signal 202. $1.0) is
`the phase modulation of pulse signal 202. ARU) is the ampli—
`tude modulation of respiration signal 204. mRtt) is the fre—
`quency modulation of respiration signal 204. $1.0) is the
`phase modulation of respiration signal 204. and Ntt) is the
`time varying noise. which includes baseline drift and broad—
`band noise in the overall signal band.
`[0047] Given the measured detectorsignal 200 (5(0). signal
`processing is performed to estimate the slowly varying corn-
`ponents of the heart rate 01,40 and the respiration rate wptt).
`It is known that w.,(t)u(o,mul beat per second 1 Hz for heart
`rate and toR{t)v-u)m~l 2 breaths per minute~0.2 Hz for respi-
`ration rate.
`
`[0048] Amplitude, phase. and frequency modulation cause
`spectral spread that broadens the pure tones implied by these
`frequencies. Amplitude and phase modulation and rapid fluc-
`tuations of the frequency modulation are confounding come
`ponents of detector signal 200. The slowly varying compo-
`nents ot~ u:(t) are the desired components 1hr obtaining heart
`and respiration rate information.
`is com-
`[0049]
`For both heart and respiration rate. wtt).
`posed of three pans: constant frequency (no, which is the
`nominal heart or respiration rato:a Zero-1111331 . slowly varying
`frequency component S2, having a time scale ofminutcs: and
`a zero-mean. rapidly varying frequency component thaving
`a time scale ofseconds. In this case. the composite heart rate
`or respiration rate is written as
`(ott}-:(oo+fl,tt)+§2,t r).
`
`(2]
`
`The signal of interest is the combination of the constant and
`the slowly varying component (for both hoan rate and respi-
`ration rate) and is written as
`
`(human-+94”.
`
`024
`
`FITBIT, Ex. 1016
`
`

`

`US 2009/0105556 A1
`
`Apr. 23, 2009
`
`root = not + you)
`
`= no mm”.
`
`then true beat peaks may be missed. For general processing it
`is convenient to select a single threshold. but if the data are
`sufficiently variable then selecting a single threshold is not
`possible.
`[0055] Other approaches to processing the cardiac signal
`use nonstationary frequency estimation methods. For
`instance. instantaneous frequency computation component
`306. 318 are used to determine the time-varying principal
`frequencies present in bandpass filtered signals. Approaches
`to instantaneous frequency computation include Hilbert
`'l‘t'dnsflirm methods. which are particularly elfective because
`the band-pass filtered cardiac signal shown in 1?[(i. 4 has a
`strong sinusoidal content and because systematic changes in
`the 13] over long periods oftime are often ofinterest. Model-
`based approaches. such as Kalman filtering approaches
`described later in this document can also be used, with state
`variables (or alternatively time-varying system parameters)
`that are estimated corresponding to the instantaneous fut.-
`quencies of interest. Yet other approaches to tracking instan-
`taneous frequency can be based on adaptive modeling ofthe
`underlying quasi-periodic heart signal.
`[0056]
`In some embodiments. an approach to determining
`the instantaneous frequency relies on analytic signals. which
`are signals that have no negative frequency components.
`Based on the properties ofthe Fourier transform. a signal with
`no negative frequencies is a complex signal
`in the time
`domain. Given a real signal. x,(t}. the corresponding analytic
`signal. xn(t)=er,.(t)+jx,(t). has the same positive frequency
`spectrum as x,(t] but has zero negative frequency values.
`Thus. the imaginary signal x,(t) mttst be determined. The
`utility of computing the analytic signal becomes apparent
`when it is written as follows:
`
`O25
`
`The phase modulation noted is Eq. ( l ). Mt). is assumed to be
`small. since large phase modulation can be represented as
`frequency modulation and this is already captured in $2, or Q,
`[0050] Applying tiq. (3) to liq. {1). a new equation for the
`measured raw signal 200 is obtained:
`
`rut-"Afltncos [(iJHUlI-t-Qtdfifl]+.-lglticos [
`tht‘uapmtrflt-Ntn.
`
`14)
`
`where for both heart and respiration rate. small components
`([141) are defined as
`we ,' “’Qlt'rtrttmn
`
`(5)
`
`and by construction l¢E[tJI<<] and aim is zero mean.
`[005]] Using the Law of Cosines and the fact that I¢E(t]
`|<<l. the following equation for raw signal 200 is obtained:
`
`stri=.t,.t:1[ cos [tir,,tt)tf:¢.,,vtttsin [:Irmrtrf}+_.r..ra{
`cos [roRmIJ—dwttisln [roflmdht‘r't ti.
`
`(6)
`
`[0052] The formulation in Eq. {6) of PFC detector signal
`200 suggests a number of methods to estimate the desired
`slowly varying heart and respiration rate signals riJH(t) and
`thigh). respectively. Such estimation techniques can include
`instantaneous frequency determination via analytic signals.
`tuoving averages. band pass filtering. synchronous detection.
`correlation detection. narrowband processes (e.g.. demodu—
`lation). matched filtering, wavelet filtering. short—time fre—
`quency analysis (cg, short-time fast Fourier transform.
`Wigner-Ville transform). state estimation (cg... Kalman fil-
`tering, unscented filtering), Doppler processing, or a combi-
`nation of the above methods. A nutuber of these techniques
`can be impleruented to account for the nonstationary nature of
`the detector signal, which relates to the time variation of the
`frequency modulation signals. tam.
`[0053] Referring to FIG. 3. one example of a procedure for
`obtaining hean rate parameters andfor respiration rate param-
`eters involves receiving the detector signal 200. such as that
`shown in FIG. 2. from a PPG photodetector (IR Plethysmo-
`graph 300). To emphasize the pulse signal in order to extract
`cardiac parameters from the detector signal, the respiratory
`modulation is removed by bandpass filtering [302} the deter;
`tor signal between approximately 0.5 Hz and approximately
`5.5 119:. Band-pass filtering of the detector signal shown in
`FIG. 2 results in a cardiac signal shown in FIG. 4. which has
`significantly less amplitude variability due to respiratory con-
`tamination. The band-pass filtered signal pulse signal can be
`expressed as
`streamed cos [autos—nurse ttfintrtrrhfltn.
`
`m
`
`where Mt) is the content of the noise. N(t). within the heart
`rate bandwidth. Likewise. referring again to FIG. 3. to extract
`parameters related to respiration from a PPG detector signal.
`the pulse signal is removed by band-pass filtering (304) the
`detector signal between approximately 0.17112 (equivalent to
`10 breaths per minute) and 0.5 Hz (30 breaths per minute),
`Band—pass filtering the detector signal of FIG. 2 produces a
`respiration signal shown in FIG. 5. which retains primarily
`low frequency reSpir-ation components ofthe original signal.
`[0054]
`Focusing now on the cardiac signal. one approach to
`detecting heart beats in the cardiac simtal is by threshold—
`based peak picking. which can be used to determine the time
`of specific events such as heart beats. In smite implementa-
`tions. peak picking can be sensitive to the threshold selected.
`For example. if the threshold is set too low. then false beats
`can be counted. and the inter