a2) United States Patent
`US 6,721,584 B2
`(10) Patent No.:
`Apr. 13, 2004
`(45) Date of Patent:
`Baker, Jr. et al.
`
`
`US006721584B2
`
`(54) METHOD AND APPARATUS FOR
`ESTIMATING PHYSIOLOGICAL
`PARAMETERS USING MODEL-BASED
`ADAPTIVE FILTERING
`
`(75)
`
`.
`.
`(*) Notice:
`
`References Cited
`aAITS
`RET ,
`US. BATENT DOCUMENTS
`4,266,554 A *
`5/1981 Hamaguri
`................ 600/323
`4,621,643 A * 11/1986 New et al. oc... O00/331
`Inventors: Clark R. Baker, Jr., Castro Valley, CA
`4,960,126 A * 10/1990 Conlonet al.
`.............. 600/336
`(US); Thomas J. Yorkey, San Ramon,
`RE35,122 E
`12/1995 Corenmanetal.
`CA (US)
`5,490,505 A *
`2/1996 Diab et al.
`.................. 600/323
`
`5,494,032 A *
`........... 600/323
`2/1996 Robinsonet al.
`5
`cf
`5/1909
`i
`«
`"i
`(73) Assignee: Nellcor Puritan Bennett Incorporated,
`..eecceccseee. 600/323
`. 5,632,272 A
`5/1997 Diab et al.
`Pleasanton, CA (US)
`* cited by examiner
`-
`a,
`oo,
`Primary Examiner—Eric F. Winakur
`Subject to any ee, the eae (74) Attorney, Agent, or Firm—Townsend & Townsend &
`patent
`is extended or adjusted under 35
`Crew LLP
`US.C. 154(b) by 0 days.
`‘
`ABSTRACT
`
`(56)
`
`(57)
`
`>
`=
`(21) Appl, No: 05/876,04
`(22)
`Filed:
`Jun. 6, 2001
`
`(05)
`
`Prior Publication Data
`US 2002/0045806 Al Apr. 18, 2002
`
`Related U.S. Application Data
`
`A method and apparatus for reducing the effects 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
`(63) Continuation of application No. 09/435,144,filed on Nov.5,
`:
`a
`i
`=
`1999, now abandoned, which is a continuation of application
`Periods. More specifically, a general class of filterssuch as,
`No. 09/137,479,
`filed on Aug. 20, 1998, now Pat. No.
`for example, ee ae s elas Ficeie .
`6,083,172, which is a continuation of application No.
`measurements and
`calculated values.
`The
`filters use math-
`08/660,510, filed on Jun. 7, 1996, now Pat. No. 5,853,364.
`ematical models which describe how the physiological
`Provisional application No. 60/000,195, filed on Jun. 14,
`parameters change in time, and how these parameters relate
`1995.
`10 Measurement in a noisy environment. Thefilters adap-
`(51) Unt, C17 oosececscsssssssssssnssessnsnseee AGIB 5/00
`tively modify a Set obaveraging Wrights. © ‘optimally
`(52). DiSSCE: .cacccnnnnemeannes WOES
`(58) Field of Search
`A6aib a estimate the physiological parameters.
`;
`‘ield
`of Search
`............00000...0........... 600/310, 322,
`600/323, 331, 330, 336
`4 Claims, 12 Drawing Sheets
`
`(60)
`
`oaTa acoersiion
`
`f°
`
`maul Loeaamren
`AND PASS FILTER
`ETA CORRECTION
`
`RA RED
`5s,
`
`:
`
`
`pee]
`ADAPTIVE
`1E
`2) (RE)
`
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`
`“osBEST RATE
`inet
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`
`
`2a {
`
`DISPLAY SAT
`
`
`
` r
`
`a“
`
`[rast reasJ
`[osruree y**
`
`
`
`0001
`
`U.S. Patent No.
`
`Apple Inc.
`APL1063
`8,923,941
`
`Apple Inc.
`APL1063
`U.S. Patent No. 8,923,941
`
`0001
`
`

`

`U.S. Patent
`
`Apr.13, 2004
`
`Sheet 1 of 12
`
`US 6,721,584 B2
`
`paratiéi
`ACQUISITION
`
`{!@
`
`1"
`
`50,
`IR & RED
`
`
`WATURAL
`LOGARITHM
`
`BAND Pass
`FILTER
`
`IR
`NORMALIZE
`
`vo
`
`38
`
`POWER
`SPECTRUM
`
`PATTERN
`MATCH RATE
`
`1l |{ || | | | ] | |
`
`| l (
`
`18 |
`
`7l |
`
`| | |
`
`i]
`
`KALMAN
`6co >
`
`NORMALIZE
`
`WHITEN
`
`
`
`ADAPTIVE
`COMB FILTER
`| HARMONIC
`42
`FILTER
`
`BEST RATE|UNWHITEN|
`
`RATE
`36
`TRIGGERS
`OR ECG
`1oS
`
`| |4
`
`SS
`
`| | | | |
`
`S
`
`OUTLIER
`REJECTION
`
`CLS ON INIT
`KALMAN SAT
`
`BEST SAT
`
`POST PROGESS m
`
`Y
`DISPLAY R
`
`RATE
`
`46
`
`27T~|
`
`28
`
`POST PROCESS
`
`DISPLAY SAT
`
`FIG. IA.
`
`0002
`
`0002
`
`

`

`U.S. Patent
`
`Apr.13, 2004
`
`Sheet 2 of 12
`
`US 6,721,584 B2
`
`[~!
`
`l2
`
`4 1
`
`DATA AcQuisiTion
`
`NATURAL LOGARITHM
`
`BAND PASS FILTER
`
`5
`ETA CORRECTION
`
`_IR_& RED
`
`
`
`__|R
`NORMALIZE
`
`RATE
`
`Ta
`CLS ON INIT
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`KALMAN SAT
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`nm
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`4
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`| |
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`~ BEST SAT
`Be aici _ _ 26)
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`POST PROCESS
`og~{
`DISPLAY SAT
`
`FIG.
`
`IB.
`
`POST PROCESS
`DISPLAY RATE
`
`“4
`T°
`
`0003
`
`0003
`
`

`

`U.S. Patent
`
`Apr. 13, 2004
`
`Sheet 3 of 12
`
`US 6,721,584 B2
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`U.S. Patent
`
`Apr.13, 2004
`
`Sheet 4 of 12
`
`US 6,721,584 B2
`
`DECODER
`
`FIG. 3.
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`0005
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`U.S. Patent
`
`Apr.13, 2004
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`US 6,721,584 B2
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`Apr.13, 2004
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`U.S. Patent
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`Apr. 13, 2004
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`US 6,721,584 B2
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`U.S. Patent
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`Apr.13, 2004
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`U.S. Patent
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`Apr.13, 2004
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`US 6,721,584 B2
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`Apr.13, 2004
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`US 6,721,584 B2
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`US 6,721,584 B2
`
`1
`METHOD AND APPARATUS FOR
`ESTIMATING PHYSIOLOGICAL
`PARAMETERS USING MODEL-BASED
`ADAPTIVE FILTERING
`
`RELATED APPLICATION DATA
`
`The present application is a continuation of application
`Ser. No. 09/435,144, filed Nov. 5, 1999, (now abandoned),
`which is a continuation ofapplication Ser. No. 09/137,479,
`filed Aug. 20, 1998, (now U.S. Pat. No. 6,083,172), which
`is a continuation of application Ser. No. 08/660,510, filed
`Jun. 7, 1996 (now U.S. Pat. No. 5,853,364), which is a
`nonprovisional utility patent application based on provi-
`sional patent Application No. 60/000,195, 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.
`
`Pulse oximeters 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 asa finger
`or an ear, and photoelectrically sense the absorption oflight
`in the tissue. The amount oflight absorbed is then used to
`calculate the amount of the blood constituent
`(e.g.,
`oxyhemoglobin) being measured.
`Thelight passed through thetissue is selected to be of one
`or more wavelengths that are absorbed by the blood in an
`amount representative of the amountof the blood constituent
`present in the blood. ‘The amount oflight 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 convenientstarting point assumes a
`saturation calculation based on Lambert-Beer’s law. The
`following notation will be used herein:
`
`1A0=foAdexp(-(sBo(A)+U-s)B(A)MO)
`
`()
`
`where:
`
`dh=wavelength;
`t=time;
`
`I=intensity of light detected;
`[,=intensity of light transmitted;
`s=oxygen saturation;
`(i. },=empirically derived absorption coefficients; and
`I(t)=a combination of concentration and path length from
`emilter to detector as a function oftime.
`
`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 of (1) is taken (“log” will be
`used to represent the natural logarithm) for IR and Red
`
`fogl=logl-(sh,.+(1-s)B,
`
`(2)
`
`15
`
`30
`
`35
`
`40
`
`45
`
`50
`
`55
`
`60
`
`65
`
`0014
`
`(3)
`
`(4)
`
`(5)
`
`2. (2) is then differentiated with respect to time
`
`
`dl
`dlog/
`7 =—-(sf + (1 — BF
`
`3. Red (3) is divided by IR (3)
`
`dloghAg)/dt _ SBolAg) + (L-—SB8AAR)
`dlogi(Ayg)/dt sBo(Aig) + (1 —5)8-Agp)
`
`4. Solving for s
`
`
`dlogl(Arg)
`dlogl(Ag)
`BrlAig)
`dt
`BAR) —
`di
`
`dlogl(Ag)
`we (Bo(Aug) — BlApR) —
`dt
`dloghAje)
`ar
`ae (BolA Rg) — BAR)
`
`Note in discrete time
`
`
`dlogl(aA, 1)
`a
`
` logl(A, ty) —logf(A, 4)
`
`Using log A-log B=log A/B,
`
`
`dlogi(A, 0) aint pi(ts, A)
`dt
`Mey, AY!
`
`So, (4) can be rewritten as
`
`
`dlog/(Ap)
`Tt. +2)
`adr
`=:
`I(t, Ap)!
`
`H(t, Arr)
`dlogl(Ayg) -
`———-_
`)
`(tz, Ay)
`dt
`
`lo
`
`where R represents the “ratio of ratios.”
`Solving (4) for s using (5) gives
`
`BelAp) — RBpApR)ee
`RUB.) — BAR — BolAg) + BAR)
`
`From (5) note that R can be calculated using two points
`(c.g., plethysmograph maximum and minimum), or a family
`of points. One method using a family of points uses a
`modified version of (5). Using the relationship
`
`
`di /dt
`dlogl
`“ad 7
`
`now (5) becomes
`
`Ita, Ap) —f(t, Ap)
`dlogi(Ag)
`Titi, AR)
`=
`adr
`dlogl(Aig) ~ Mita, Arp) — th, Ape)
`di
`Z(t. Aye)
`
`_ [tz Ag) — Mn, Ag, Aik)
`~ (Ht, Ape) — Mtn, ie, AR)
`= R
`
`(6)
`
`(7)
`
`0014
`
`

`

`US 6,721,584 B2
`
`Nowdefine
`Then
`
`describes a cluster of points whose slope of y versus x will
`give R,
`
`X()=[(Aye l(tAre) WhAg)
`
`v=o AgMh Ag) WhAne)
`
`y(tj=Rx(0)
`
`(8)
`
`The optical signal through the tissue can be degraded by
`both noise and motion artifact. One source of noise is
`ambient
`light which reaches the light detector. Another
`source of noise is electromagnetic coupling from other
`electronic instruments. Motion of the patient also introduces
`noise and affects 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
`cither to move away from the skin. In addition, since blood
`is a fluid, it responds differently than the surroundingtissue
`to inertial effects, thus resulting in momentary changes in
`volumeat the point to which the oximeter probeis 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 sensorsite.
`In one oximeter system described in U.S. Pat. No. 5,025,
`791, an accelerometer is used to detect motion. When
`motionis 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 value and one which is
`recorded at the wrong time. In U.S. 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. 5,078,136, assigned to
`Nellcor, the entire disclosure of which is incorporated herein
`by reference, use signal processing in an attempt to limit the
`effect 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 OF THE INVENTION
`
`According to the present invention, a method and appa-
`ratus are provided for reducing the effects of motion artifact
`and noise on a system for measuring physiological
`parameters, such as, for example, a pulse oximeter. The
`method and apparatus ofthe invention take into account the
`physical
`limitations on various physiological parameters
`being monitored when weighting and averaging a series of
`samples or measurements. Varying weights are assigned
`different measurements. Optionally, measurements are
`rejected if unduly corrupt. The averaging period is also
`
`30
`
`35
`
`40
`
`45
`
`50
`
`55
`
`60
`
`65
`
`4
`adjusted according to the reliability of the measurements.
`More specifically, a general class offilters 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 ofthe
`present invention are applied to a pulse oximeter which is
`used to measure the oxygen saturation of hemoglobin in
`arterial blood. The system takes the natural logarithm of the
`optical oximetry data and then bandpassfilters the data to get
`absorption-like data. The bandpassfilter strongly attenuates
`data below 0.5 Hz and above 10 Hzin an attempt to remove
`as much out-of-band noise as possible. This filtered 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 ACFis quite robust
`through noisy environments, however, the ACFis not a good
`heart rate finder. 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 andtroughsin the data and calculates an average heart
`rate period over a set number of samples.
`The system then employs the calculated heart 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 mul-
`tuples of the heart rate, the system adaptively signal averages
`full cycles of past plethysmographs, 1.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
`pleth cycle data which has been combfiltered 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 (e.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 described 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 ofthe different saturation values.
`According to a specific embodiment of the invention, a
`method for reducing noise effects in a system for measuring
`a physiological parameter is provided. A plurality of mea-
`surements is generated correspondingto at least one wave-
`length of electromagnetic energy transmitted through living
`tissue. Selected measurements are compared with at least
`one expected measurement characteristic. A variable weight
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`5
`is assigned to each of the selected measurements based on
`the comparison, thereby generating a plurality ofdifferently
`weighted measurements for cach 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 second numberof
`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 berealized by reference to the
`remaining portions ofthe specification and the drawings.
`
`BRIEF DESCRIPTION OF THE DRAWINGS
`
`FIGS. la and 16 are block diagrams illustrating the data
`flow in a pulse oximetry system designed according to two
`specific embodiments of the invention;
`FIG. 2 shows the frequency response of an infinite
`impulse response (IIR) filter employed by a specific embodi-
`ment of the invention;
`FIG. 3 showsa sensor/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 outputsof the
`Kalman cardiac gated averaging filter;
`FIG, 6 is a graph illustrating the improvement in satura-
`tion calculation gained by enhancing the pulse shape with
`the Kalman cardiac gated averaging filter;
`FIG. 7 is a graph illustrating the weighting and aging of
`pulses by one embodiment of a Kalman cardiac gated
`averagingfilter;
`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 combfilter;
`FIG. 10 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 OF THE PREFERRED
`EMBODIMENT
`
`0
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`30
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`45
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`FIG. la showsthe flow ofdata according to one embodi-
`ment of the present invention. A separate platform collects
`the oximetrydata (step 10) and passesit to processors 50 and
`52 of the present invention. A preferred platform is described
`in U.S. Pat. No. 5,348,004 assigned to Nellcor, the entire
`disclosure of whichis incorporated herein by reference. The
`data is first pre-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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`60
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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 Kalmanfilter (step 16) are
`discussed below.
`the processing
`According to a preferred embodiment,
`technique employs the following pre-processing. The natu-
`ral logarithm of the IR and Red wavelength data is taken
`(step 12), and then the data is band pass filtered with an
`infinite impulse response (IIR) filter that has a high pass
`cutoff frequency at 0.5 Hz, i.e., 30 beats per minute, and a
`low passrolloff from 10 to 20 Hz (step 14). FIG. 2 showsthe
`frequency response of an IIR 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.
`la,
`two saturation values are calculated in parallel by
`saturation calculator 50. 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 harmonicfilter (step
`17) before it
`is averaged in step 16. Harmonic filter 17
`digitally filters the IR and red waveforms such that only
`energy at
`integer multiples of the heart rate is allowed
`throughthefilter. The response of harmonic filter 17 varies
`with the heart rate whichis supplied by pulse rate calculator
`52 to attenuate motion and noise energy notal 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 and/or 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 downweights 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 samestatistically. As a result, samples exhibiting
`large amounts of noise are down weighted,
`thus
`de-emphasizing outliers. Whitening step 20 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 27), 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 howit 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. The use of this technique occurs on initialization of
`saturation calculator 50 only.
`The pulse or heart rate is calculated in pulse rate calcu-
`lator 32 in the following manner. After the pre-processing
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`7
`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 30) as described above, and the
`fundamental frequency ofthe pulse rate is tracked using an
`adaptive combfilter (ACF) 32 as discussed below. ACF 32
`supplies its pulse rate directly to harmonic filter 17 as
`described above. ACF 32 also provides the trigger
`for
`Kalman CGA 16 after the data is unwhitened by integration
`(step 34) and the triggers for Kalman CGA are generated
`(step 36). Alternatively, the triggers for Kalman CGA 16
`may be derived from, for example, an ECG waveform. ACF
`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 ofthe pulse rate.
`The normalized data is also supplied to a pattern matching
`algorithm 40 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. A best rate algorithm 42 then arbitrates between the
`pulse rates calculated by ACF 32 (as updated by frequency
`power spectrum 38) and pattern matching algorithm 40
`using confidence levels associated with each, 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 ofthe present invention. The system operates
`the same as the system of FIG. la exceptthat after the data
`is band pass filtered by IIR filter 14,
`it undergoes an
`additional processing step in ela correction processor 15
`before it is sent 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 effects of motion and other
`noise artifact. The operation of eta correction processor 15
`is based on an analysis ofthe signal intensity received for the
`different 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. First, although
`the magnitude ofthe effect of motion on the signal intensity
`for each wavelength will be different,
`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
`
`15
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`35
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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, If 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) described below uses a Kalmanfilter. The method
`first transforms the pre-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 coefficients, 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 for maxima or minimalike a pulse searching algo-
`rithm (although maxima or minima could be used and
`Kalman filtered if desired). Using instantaneousratios (i.¢.,
`a time based algorithm) rather than maxima/minima ratios
`(i.e., 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
`LEDsin the particular sensor attached to the patient. In a
`preferred embodiment,
`the sensor includes a means for
`generating a signal which correspondstoat 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
`recalibrate an oximeter to match the transmission charac-
`teristics of a particular probe. In a preferred embodiment, the
`means for generating the signal is an electrical impedance
`element such as, for example, a resistor, the value of which
`corresponds to the wavelengths of the LEDs. A preferred
`embodiment of a sensor/oximeter combination is shown in
`FIG. 3. Oximetry system 60 includes a sensor 61 and an
`oximeter monitor 62. Sensor 61 includes LEDs 63 and 64
`typically having wavelength emission characteristics in the
`infrared and red ranges of the spectrum, respectively. Pho-
`todiode sensor 65 receives the light transmitted by LEDs 63
`and 64. Resistor 66 (or a similar electrical
`impedance
`reference) is chosen to correspond to a specific wavelength
`or combination of wavelengths as specified by a table
`relating impedance values to wavelengths. Once decoding
`means 67 determines the impedance value of resistor 66,
`appropriate extinction coefficients are generated which cor-
`respond to the transmission characteristics of the particular
`sensor 61. Thus, the oximeter may be used with a variety of
`sensors having LEDs which emit varying wavelengths of
`light without recalibration.
`Sensor 61 may be detachably coupled to oximeter monitor
`62 via connector 68. An example of such a sensor/oximeter
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`9
`combinati