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`USOtl6721584B2
`
`(12) United States Patent
`(10) Patent No.:
`US 6,721,584 B2
`(45) Date of Patent:
`Baker, Jr. et al.
`Apr. 13, 2004
`
`(54)
`
`(i5)
`
`(73)
`
`METHOD AN!) AI’I’ARA'I‘US FOR
`ESTIMATING PHYSIOIDGICAL
`PARAMETERS USING MODEL-BASED
`ADAPTIVE FILTERING
`
`Inventors. Clark R. Baker, Jr., Castro Valley, CA
`(US); Thomas J. Yorkey, San Ramon.
`CA (US)
`
`Assignee: Nelleor Puritan Bennett Incorpumted,
`Pleasanton, CA (US)
`
`(*l
`
`Notice:
`
`Subject to any disclaimer, the term of this
`patent
`is extended or adjusted under 35
`U.S.C. 154(b) by 0 days.
`
`(31)
`
`(22
`
`(55)
`
`( 53)
`
`(60)
`
`(51)
`(52)
`(58)
`
`Appl. No: 09f8‘76,004
`
`Filed:
`
`Jun. 6, 2001
`Prior Publication Data
`
`US 2m2f0045800 Al Apr. 18, 2002
`
`Related U.S. Application Data
`
`Continuation of application No. 09f435,144. [lled on Nov. 5.
`1990, now abandoned: which is a continuation of application
`No.
`i'i‘n‘i’IJTA't“),
`liled on Aug. 20.
`[998. now Pat. No.
`(infill-1'2. which is a continuation of npplicalion No.
`08%60510, filed on Jun‘ 7, 19%, now Pat. No, 5,853,364
`Provisional application No‘ OUHXIU,195. filed on Jun. 14,
`1095.
`
`Int. Cl.7
`
`Field of Search
`
`A613 5/00
`
`
`.1 6(Kif3lii, 322,
`331, 330, 336
`600E323,
`
`(56)
`
`References Cited
`U.S‘ PA’I‘EN'I‘ DOCUMENTS
`
`5,-‘1981 Hamaguri
`4,266,554 A ‘
`tumor; New etaL
`4,621,643 A '
`4,900,126 A * mill-NU Conlon et al.
`1053:1122 Ir.
`l2r’1995 Corenmnn cl all.
`600E323
`5,490,505 A "‘
`31996 Diab et al.
`5,494,032 A "‘
`2:1990 Robinson et al.
`.. 600L323
`
`.. 000811
`5.032272 A *
`$1997 Diah et al.
`
`500L323
`606831
`600L334)
`
`* cited by examiner
`Printaer Examiner-Aide l“. Winakur
`(74) Attorney, Agent, or Firm—Townsend 3L Townsend 8:.
`Crew LLP
`
`(57)
`
`ABSTRACT
`
`Amethod and apparatus for reducing the ell'ecL-s of noise on
`a system for measuring physiological parameters, such as,
`for example, a pulse oximeter. The method and apparatus of
`the invention take into account the physical limitations on
`various physiological parameters being monitored when
`weighting and averaging a series of measurements. Varying
`weights are assigned different measurements. measurements
`are rejected, and the averaging period is adjusted according
`to the reliability of the measurements. Similarly, calculated
`values derived from analyzing the measurements are also
`assigned varying weights and averaged over adjustable
`periods. More specifically, a general class of filters such as,
`for example, Kalman filters, is employed in processing the
`measurements and calculated values. The filters use math-
`ematical models which describe how the physiological
`parameters change in time, and how these parameters relate
`to measurement in a noisy environment. The filters adap—
`tively modify a set of averaging weights to optimally
`estimate the physiological parameters,
`
`4 Claims, 12 Drawing Sheets
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`US. Patent
`
`Apr.13,2004
`
`Sheet 1 of 12
`
`US 6,721,584 B2
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`US. Patent
`
`Apr.13,2004
`
`Sheet 2 of 12
`
`US 6,721,584 B2
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`US. Patent
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`Apr.13,2004
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`Sheet 3 of 12
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`US 6,721,584 B2
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`US. Patent
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`Apr. 13, 2004
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`Sheet 4 of 12
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`US 6,721,584 B2
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`US. Patent
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`Apr.13,2004
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`US. Patent
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`Apr. 13, 2004
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`US. Patent
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`US 6,721,584 B2
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`1
`METHOD AND APPARATUS FOR
`ESTIMATING PHYSIOLOGICAL
`PARAMETERS USING MODEL-BASED
`ADAPTIVE FILTERING
`
`RELATED APPLICATION DATA
`
`The present application is a confirmation of application
`Ser. No. 09/435,144. filed Nov. 5, 1999, (now abandoned),
`which is a continuation 01‘ application Ser. No. 09f137,479,
`tiled Aug. 20, 1998, (now us. Pat. No. 6,083,172), which
`is a continuation of application Ser. No. eateries [0, filed
`Jun. 7, 1996 (now US. Pat. No. 5,853,364), which is a
`nonprovisiona] utility patent application based on provi-
`sional patent Application No. 60i00tt,l95, filed Jun. 14,
`1995.
`
`BACKGROUND OF THE INVENTION
`
`The present invention relates to a method and apparatus
`which uses model-based adaptive filtering techniques to
`estimate physiological parameters. More specifically,
`the
`invention employs Kalman filtering techniques in pulse
`oximetry to estimate the oxygen saturation of hemoglobin in
`arterial blood.
`
`l’ulse exintetcrs typically measure and display various
`blood flow characteristics including but not limited to the
`oxygen saturation of hemoglobin in arterial blood. Oxime-
`ters pass light through blood perfused tissue such as a finger
`or an ear, and photoelectrically sense the absorption of light
`in the tissue. The amount of light absorbed is then used to
`calculate the amount of the blood constituent (cg,
`oxyhemoglobin) being measured.
`The light passed through the tissue is selected to be of one
`or more wavelengths that are absorbed by the blood in an
`amount representative ofthe amount of the blood constituent
`present in the blood. The amount of light passed through the
`tissue varies in accordance with the changing amount of
`blood constituent in the tissue and the related light absorp-
`tion.
`
`When the measured blood parameter is the oxygen satu-
`ration of' hemoglobin, a convenient starting point assumes a
`saturation calculation based on Lambert-Beer’s law. The
`following notation will be used herein:
`
`Klrft-L;UU=XP('(-Ylia(fil+(1-5Jfi.(?\JJI(iJJ
`
`(l)
`
`where:
`
`hzwavelength;
`t-time;
`l-intensity of light detected;
`[,,=intensity of light transmitted;
`s=oxygen saturation;
`[50, [3,.=empirically derived absorption coellieients; and
`l(l)-a combination of concentration and path length from
`emitter to detector as a function of time.
`The traditional approach measures light absorption at two
`wavelengths. e.g., red and infrared (IR), and then calculates
`saturation by solving for the "ratio of ratios" as follows.
`1. First, the natural logarithm ol'(]) is taken ("log” will be
`used to represent the natural logarithm) for IR and Red
`
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`US 6,721,584 B2
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`The optical signal through the tissue can be degraded by
`both noise and motion artifact. One source of noim is
`ambient
`light which reaches the light detector. Another
`source of noise is electromagnetic coupling from other
`electronic instru ments. Motion of the patient aLso introduces
`noise and all‘ecls the signal. For example,
`the contact
`between the detector and the skin, or the emitter and the
`skin, can be temporarily disrupted when motion causes
`either to move away from the skin. In addition, since blood
`is a fluid, it responds dill‘erently than the surrounding tissue
`to inertial effects, thus resulting in momentary changes in
`volume at the point to which the oximeter probe is attached.
`Motion artifact can degrade a pulse oximetry signal relied
`upon by a physician. without
`the physician’s awareness.
`This is especially true if the monitoring of the patient is
`remote, the motion is too small to be observed, or the doctor
`is watching the instrument or other parts of the patient, and
`not the sensor site.
`In one oximeter system described in U.S. Pat. No. 5,025,
`'r'91, an accelerometer is used to detect motion. When
`motion is detected, readings influenced by motion are either
`eliminated or indicated as being corrupted.
`In a typical
`oximeter, measurements taken at the peaks and valleys of the
`blood pulse signal are used to calculate the desired charac~
`teristic. Motion can cause a false peak,
`resulting in a
`measurement having an inaccurate valuc and one which is
`recorded at the wrong time. In US. Pat. No. 4,802,486,
`assigned to Nellcor, the assignee of the present invention,
`the entire disclosure of which is incorporated herein by
`reference, an EKG signal is monitored and correlated to the
`oximeter reading to provide synchronization to limit
`the
`effect of noise and motion artifact pulses on the oximeter
`readings. This reduces the chances of the oximeter locking
`onto a periodic motion signal. Still other systems, such as the
`one described in U.S. Pat. No. 5,0?8,136, assigned to
`Nellcor, the entire disclosure of which is incorporated herein
`by reference, use signal processing in an attempt to limit the
`either of noise and motion artifact. The ‘136 patent,
`for
`instance, uses linear interpolation and rate of change tech-
`niques to analyze the oximeter signal.
`Each of the above-described techniques for compensating
`for motion artifact has its own limitations and drawbacks. It
`is therefore desirable that a pulse oximetry system be
`designed which more effectively and accurately reports
`blood—oxygen levels during periods of motion.
`SUMMARY (Jli TI IE INVENTION
`
`40
`
`According to the present invention, a method and appa-
`ratus are provided for reducing the effecu; of motion artifact
`anti noise on a system for measuring physiological
`parameters, such as, for example, a pulse osimcter. The
`method and apparatus of the invention take into account the
`physical
`limitations on various physiological parameters
`being monitored when Weighting and averaging a series of
`samples or measurements. Varying weigth are assigned
`different measurements. Optionally, measurements are
`rejected if unduly corrupt. The averaging period is also
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`adjusted according to the reliability of the measurements.
`More specifically, a general class of filters is employed in
`processing the measurements. The filters use mathematical
`models which describe how the physiological parameters
`change in time. and how these parameters relate to mea-
`surement
`in a noisy environment. The filters adaptively
`modify a set of averaging weights and averaging times to
`optimally estimate the physiological parameters.
`In a specific embodiment, the method and apparatus of the
`present invention are applied to a pulse oximetcr which is
`uSed to measure the oxygen saturation of hemoglobin in
`arterial blood. The system takes the natural logarithm of the
`optical oximctry data and then bandpass filters the data to get
`absorption-like data. The bandpass filter strongly attenuates
`data below 0.5 Hz and above it) Hz in an attempt to remove
`as much out-of—band noise as possible. This llltcred data is
`then processed through two algorithms: a rate calculator and
`a saturation calculator.
`
`The system calculates the heart rate of the patient one of
`three ways using the oximetry data. An adaptive comb filter
`(ACF) is employed to track the slowly varying heart rate.
`The tracking of the heart rate by the AC1; is quite robust
`through noisy environments, however, the ACF is not a good
`heart rate tinder. As a result, the system periodically calcu-
`lates the power spectrum of one of the wavelengths and uses
`it to find and/or verify the heart rate. In cases of arrhythmia
`or suddenly changing heart rates,
`the system employs a
`pattern matching technique that recognizes sequences of
`crests and troughs in the data and calculates an average heart
`rate period over a set number of samples.
`The system then employs the calculated hean rate to
`digitally comb filter the data so that only the energy at
`integer multiples of the heart rate are allowed through the
`filter. The comb filter frequency varies as the heart rate
`varies, attenuating motion energy not at the heart rate or
`multiples thereof. To remove noise energy at integer mu]-
`tiples of the heart rate, the system adaptively signal averages
`full cycles of past plethysmographs,
`i.e., pleths, using a
`Kalman filter to limit the rate of change in the pleth shape
`or size.
`
`The system then calculates two saturations, one with the
`pteth cycle data which has been comb filtered as described
`above, and one with raw data from the output of the band
`pass filter. Both saturations are calculated using time based
`signals and using an adaptive Kalman filter which continu—
`ously weights all data according to an estimate of the current
`noise, and limits the rate of change of saturation to a defined
`limit (currently 1.3 saturation points per second). Data
`pointsthat result in a saturation calculation (prior to weight-
`ing and averaging} which is obviously not physiologically
`possible (c.g., negative saturation, or a saturation greater
`than 100%) are deemed invalid and are not used and are
`rejected in an “outlier rejection” step in both saturation
`calculations. The system then arbitrates between the two
`saturation values based on rules deseribed below to deter-
`mine the best saturation. For example, the arbitration may be
`based on such factors as the noise level or the age of the
`saturation value. The best saturation may also be a weighted
`average of the diflerent saturation values.
`According to a specific embodiment of the invention, a
`method for reducing noise effects in a system [or measuring
`a physiological parameter is provided. A plurality of mea—
`surements is generated corresponding to at least one wave-
`length ot‘electromagnetic energy transmitted through living
`tissue. Selected measurements are compared with at least
`one expected measurement characteristic. A variable weight
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`US 6,721,584 B2
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`5
`is assigned to each of the selected measurements based on
`the comparison, thereby generating a plurality ol‘difl'erently
`weighted measurements for each wavelength. A first number
`of weighted measurements is averaged to obtain a filtered
`measurement.
`the first number varying according to the
`manner in which weights are assigned to a plurality of
`successive weighted measurements. A plurality of filtered
`measurements are thus generated for each wavelength. The
`filtered measurements for each wavelength are then com-
`bined and calculations resulting therefrom are adaptively
`filtered using variable weights based on comparing the
`calculations to an expected calculation. A socond number of
`the weighted calculations are averaged to obtain a filtered
`calculation,
`the second number varying according to the
`manner in which weights are assigned to a plurality of
`successive weighted calculations.
`A further understanding of the nature and advantages of
`the present invention may be realized by reference to the
`remaining portions of the specification and the drawings.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIGS. in and 1b are block diagrams illustrating the data
`flow in a pulse oximetry system designed according to two
`specific embodiments of the invention;
`FIG. 2 shoves the frequency response of an infinite
`impulse response (IIR) filter employed by a specific embodi-
`ment of the invention;
`FIG. 3 shows a scnsor/oximeter combination for use with
`the present invention in which the transmission character—
`istics of the sensor are identified by a calibration resistor;
`FIG. 4 is a graph comparing the performance of a classic
`least squares algorithm to that of the Kalman algorithm;
`FIG. 5 is a graph comparing the inputs and outputs of the
`Kalman cardiac gated averaging filter;
`FIG. 6 is a graph illustrating the improvement in satura~
`tion calculation gained by enhancing the puLsc shape with
`the Kalman cardiac gated averaging tiller;
`FIG. 7 is a graph illustrating the weighting and aging of
`pulses by one embodiment of a Kalman cardiac gated
`averaging filter;
`FIG. 8 is a graph illustrating the improvement in satura-
`tion calculation gained by employing both the Kalman
`cardiac gated averaging filter and the Kalman saturation
`algorithm;
`FIG. 9 is a frequency domain graph depicting the response
`of a comb filter;
`FIG. It! is a graph showing the validity measure for data
`pulses in relation to the relative strengths of several signal
`harmonics; and
`FIG. 11 is a graph showing the pulse rate reported by the
`adaptive comb filter employed by the present invention as
`compared to the pulse rate reported by a prior art system.
`DESCRIPTION Oli THE PREFERRED
`EMBODIMENT
`
`FIG. 1a shows the flow of data according to one embodi-
`ment of the present invention. A separate platform collects
`the oximctry data (step 10) and passes it to processors 50 and
`52 of the present invention. Apreferred platform is described
`in US. Pat. No. 5,348,004 assigned to Ncllcor, the entire
`disclosure ol‘which is incorporated herein by reference. The
`data is first pro-processed (steps 12 and 14), and is then
`passed to a saturation calculation algorithm (box 50). The
`algorithm described herein employs an improved Kalman
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`It will be understood that other
`filter method (step 24).
`saturation calculation techniques may be employed. The
`pulse rate calculation method {box 52) and a cardiac gated
`averaging technique also using a Kalman filter {step 16) are
`discussed below.
`the processing
`According to a preferred embodiment.
`technique employs the following pro-processing. The natu~
`ral logarithm of the IR and Red wavelength data is taken
`(step 12)1 and then the data is band pass filtered with an
`infinite impulse response (HR) filter that has a high pass
`cutoff frequency at 0.5 Hz, Le, 30 beats per minute. and a
`low pass rollolT from 10 to 20 Hz (step 14). FIG. 2 shows the
`frequency response of an HR filter employed by a specific
`embodiment of the invention.
`After the oximetry data has been filtered, it is processed
`by a saturation calculation algorithm (box 50). According to
`a preferred embodiment of the invention depicted in FIG.
`In,
`two saturation values are calculated in parallel by
`saturation calculator 5|]. One saturation value is calculated
`using a harmonic filter 17 and a Kalman-filter—based cardiac
`gated averaging (CGA)
`technique (step 16) {described
`below) to obtain a more reliable data stream. Kalman CGA
`16 is gated by triggers based on the pulse rate which are
`supplied by pulse rate calculator 52.
`In a specific
`embodiment. the data is put through a harmonic filter (step
`17) before it
`is averaged in step [6. llarmonic llltcr 17'
`digitally filters the [R and red waveforms such that only
`energy at
`integer multiples of the heart rate is allowed
`through the filter. The response of harmonic filter 17 varies
`with the heart rate which is supplied by pulse rate calculator
`52 to attenuate motion and noise energy not at the heart rate.
`In one embodiment, only one of the IR and red waveforms
`is filtered by harmonic filter 17. In this embodiment, the
`subsequent filtering by Kalman CGA 16 andror the satura-
`tion calculation algorithm described below applies the same
`weighting and averaging to both the IR and red data streams
`on the basis of the single filtered data stream.
`Both saturation values are calculated in the following
`manner. The data pulses (either directly from the band pass
`filter or from steps 16 and 17) are normalized (step 18) and
`then "whitened" (step 20). Normalizing downwcights large
`pulse amplitudes so that each pulse has roughly the same
`average amplitude. Normalizing step 18 assumes that from
`one sample to the next, noise energy should look substan—
`tially the same statistically. As a result, samples exhibiting
`large amounts of noise are down weighted,
`thus
`Lie-emphasizing outliers. Whitening step 2|] involves taking
`the derivative of the normalized data, thereby emphasizing
`the higher harmonics of the pleth so that its energy is more
`evenly distributed between them. Data points resulting in an
`impossible saturation calculation are rejected (step 22) and
`the resulting data are used to calculate the saturation values
`using a Kalman filter technique described below (step 24).
`The best saturation value is then chosen (step 26) according
`to confidence levels associated with each, and, after some
`post processing (step 2?). the saturation value is output to
`the display (step 28). Post processing 27. which will be
`discussed in greater detail below, uses available metrics. with
`regard to the saturation value to determine its reliability and
`determine whether and how it is to be displayed. In specific
`preferred embodiments of the present invention, the initial
`saturation value calculated by each calculation path in
`saturation calculator 50 may be calculated by the well
`known classic least squares (CLS) technique as indicated by
`step 21. 'I'he use of this technique occurs on initialization of
`saturation calculator 50 only.
`The pulse or heart rate is calculated in pulse rate calcu-
`lator 52 in the following manner. After the pro-processing
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`described above. data from one channel. e.g., the IR channel.
`are normalized (step 29) by the downweighting of data
`corresponding to large pulse amplitudes so that each pulse
`has roughly the same average amplitude. The data are then
`sent
`to two different algorithms for calculation of the
`patient's pulse rate. According to one algorithm, the deriva-
`tive of the data is taken (step 31]) as described above, and the
`fundamental frequency of the pulse rate is tracked using an
`adaptive comb filter (AC1")32 as discussed below. AC1" 32
`supplies its pulse rate directly to harmonic filter 17 as
`described above. AC F 32 also provides the trigger
`for
`Kalman CGA 16 after the data is unwhitencd by integration
`(step 34) and the triggers for Kalman CGA are generated
`(step 36). Alternatively, the triggers for Kali-nan CGA 16
`may be derived from, for example, an ECG waveform. AC]:
`32 is a robust pulse rate tracker, but not a good pulse rate
`finder. Therefore,
`the frequency power spectrum of the
`normalized data is calculated periodically (step 38) to deter-
`mine whether ACF 32 is tracking the fundamental rather
`than a super- or subharmonic of the pulse rate.
`The annualized data is alsn supplied to a pattern matching
`algorithm 41] which recognizes sequences of crests and
`troughs in the data and calculates an average period of the
`pleth over a set number of samples. This algorithm is
`preferably used primarily to track the pulse rate for an _
`arrhythmic pulse rate during periods where no motion is
`detected. Abest rate algorithm 42 then arbitrates between the
`pulse rates calculated by ACF 32 (as updated by frequency
`power spectrum 38} and pattern matching algorithm 41]
`using confidence levels associated with eachI which are
`based on various metrics. After post processing (step 44), the
`pulse rate is output
`to the display (step 46}. As with
`saturation calculator 50, post processing 44 uses available
`metrics to determine the reliability of the pulse rate and to
`determine whether and how it is to be displayed.
`FIG. 1b shows the flow of data according to a second
`embodiment of the present invention. The system operates
`the same as the system of FIG. lrr except that after the data
`is band pass filtered by HR filter 14,
`it undergoes an
`additional processing step in eta correction processor 15
`before it is 56111 to either saturation calculation algorithm 50
`or pulse rate calculation algorithm 52. Like other aspects of
`the present invention already described, eta correction pro-
`cessor 15 serves to reduce the ell'ects of motion and other
`noise artifact. The operation of eta correction processor 15
`is based on an analysis ofthc signal intensity received for the
`ditIerent wavelengths, without separately measuring the
`motion signal for each wavelength, without providing feed-
`back to cancel the motion signal, and without attempting to
`mathematically eliminate the motion signal individually for
`each wavelength. Instead, processor 15 mathematically rec—
`ognizes the presence of the motion signal and recognizes a
`few key characteristics of the motion signal. t-‘irst, although
`the magnitude of the etfcct of motion on the signal intensity
`for each wavelength will be dilIcrent,
`the change in the
`logarithm of the motion component will be approximately
`the same (for signals obtained at approximately the same
`time). This allows the motion component to be cancelled out
`in a ratiometric equation. Second,
`it
`is assumed that the
`blood pulse signal is not affected by motion. This second
`assumption is more of an approximation, since the blood
`pulse signal is somewhat affected by motion, which can
`actually change the blood volume characteristics at any
`point in the patient. [Eta correction processor 15 recognizes
`that the intensity signal for each of the wavelengths includes
`a time-varying motion term, and that
`this time-varying
`motion term is proportional for each of the wavelengths. In
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`addition, each wavelength signal occurs close enough in
`time with one another that
`the motion should not vary
`noticeably, and can be assumed to be the same for each
`signal. The output from eta correction processor 15 is an IR.
`or red signal which has significantly less motion noise than
`the signals fed into processor 15. [f the data include infor-
`mation from a third wavelength, the output of processor 15
`is both an IR signal and a red signal depleted of motion
`noise. A more detailed description of the operation of eta
`correction processor 15 is described in a commonly
`assigned, copending U.S. patent application Ser. No. 08/490,
`315 for METHOD AND APPARATUS FOR REMOVING
`ARTIFACT AND NOISE FROM PULSE OXIMETRY, filed
`Jun. 14, 1995. the entire disclosure of which is incorporated
`herein by reference.
`The method for calculation of blood oxygen saturation
`(step 24) deseribcd below uses a Kalman filter. The method
`first transforms the pro-processed data into quantities cor-
`responding to the oxyhemoglobin and total hemoglobin
`concentrations using appropriate extinction coefficients. The
`instantaneous ratio of these two transformed quantities gives
`the saturation It will be understood from the equation
`immediately following equation (4) above that the instan—
`taneous saturation value may be calculated directly by using
`the extinction cocflicients, or from the ratio of ratios as
`shown in the equation immediately following equation (5).
`According to a preferred embodiment, the method does not
`search [or maxima or minima like a pulse searching algo-
`rithm {although maxima or minima could be used and
`Kalman filtered if desired). Using instantaneous ratios (i.e.,
`a time haSed algorithm) rather than maximafminima ratios
`(in, an event based algorithm) keeps the code from being
`event driven and having to qualify data as it arrives, Thus,
`the preferred method of the present invention is simpler to
`implement
`than a pulse—searching event—based saturation
`calculation algorithm.
`The extinction coefficients are determined with reference
`to the wavelength or wavelengths being transmitted by the
`LL-‘Ds in the particular sensor attached to the patient. In a
`preferred embodiment,
`the sensor includes a means for
`generating a signal which corresponds to at least one of the
`wavelengths being transmitted by the sensor’s LEDs. The
`oximeter monitor receives the signal and determines the
`proper extinction coefficients based on the wavelength or
`wavelengths indicated by the signal. This avoids the need to
`rccalibrate an oximctcr to match the transmission charac-
`teristics of a particular probe. In a preferred embo