U.S. patent application number 11/068535 was filed with the patent office on 2005-11-10 for compensation of human variability in pulse oximetry.
Invention is credited to Huiku, Matti.
Application Number | 20050250998 11/068535 |
Document ID | / |
Family ID | 27732606 |
Filed Date | 2005-11-10 |
United States Patent
Application |
20050250998 |
Kind Code |
A1 |
Huiku, Matti |
November 10, 2005 |
Compensation of human variability in pulse oximetry
Abstract
The invention relates to a method of calibrating a pulse
oximeter, in which the effects caused by tissue of a subject can be
taken into account. A detector output signal is measured when
living tissue of the subject is present between emitters and the
detector in a sensor. Nominal calibration and nominal calibration
characteristics are read from a memory, whereupon values for the
same nominal characteristics for the sensor on living tissue of the
subject are established using the detector output signal. Then,
changes in the nominal calibration characteristics induced by the
living tissue are calculated and a subject-specific calibration is
formed by correcting the nominal calibration with the changes.
Finally, the hemoglobin fractions are solved using the corrected
nominal calibration. The invention also relates to a pulse oximeter
having pre-stored data in a memory comprising data of initial
characterization measurements, data of nominal characteristics
describing calibration conditions under which a predetermined
calibration of the apparatus has been applied, and data of nominal
calibration and nominal calibration characteristics. An extinction
coefficient compensation block is operatively connected to the
first signal processing means and to the memory for reading data,
said block comprising first calculation means adapted to correct
the nominal characteristics of the sensor on living tissue of the
subject. A transformation compensation block is operatively
connected to the first signal processing means for receiving the DC
signals and to the memory for reading data, said block comprising
second calculation means adapted to correct the transformation
values based on the changes in the DC signals induced by tissue of
the subject. Alternatively, said data may be stored in the sensor
part of the pulse oximeter.
Inventors: |
Huiku, Matti; (Espoo,
FI) |
Correspondence
Address: |
MARSH, FISCHMANN & BREYFOGLE LLP
3151 SOUTH VAUGHN WAY
SUITE 411
AURORA
CO
80014
US
|
Family ID: |
27732606 |
Appl. No.: |
11/068535 |
Filed: |
February 28, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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11068535 |
Feb 28, 2005 |
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10077196 |
Feb 15, 2002 |
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6882874 |
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Current U.S.
Class: |
600/331 ;
600/323 |
Current CPC
Class: |
A61B 5/14551 20130101;
A61B 5/1495 20130101 |
Class at
Publication: |
600/331 ;
600/323 |
International
Class: |
A61B 005/00 |
Claims
1. A method for compensating for subject-specific variability in a
pulse oximeter intended for non-invasively determining in in-vivo
measurement the amount of at least two light-absorbing substances
in the blood of a subject and provided with emitters for emitting
radiation at a minimum of two different wavelengths and with a
detector for transforming the radiation received into an electrical
output signal, the method comprising the steps of measuring
detector output signal when living tissue of the subject is present
between the emitters and the detector in a sensor, wherein the
detector output signal depends on tissue, reading a nominal
calibration and nominal calibration characteristics from a memory,
establishing values for the same nominal characteristics for the
sensor on living tissue of the subject using the detector output
signal, calculating changes in the nominal calibration
characteristics induced by the living tissue, forming a
subject-specific calibration by correcting the nominal calibration
with the changes, solving the hemoglobin fractions using the
corrected nominal calibration.
2. The method as in claim 1, wherein the nominal calibration
characteristics include factors of external origin.
3. The method as in claim 1, wherein the nominal characteristics
include factors of internal origin.
4. The method as in claim 2, wherein the factors of external origin
are associated with calculation of numeric values of extinction
coefficients in the Lambert-Beer model.
5. The method according to claim 1, wherein determination of
wavelength shifts in relation to the nominal wavelengths are
incorporated into said establishing step.
6. The method according to claim 5, wherein the effect of
wavelength shift is derived from the transmitted signal through the
tissue.
7. The method according to claim 5, wherein effects of the
wavelength shifts are incorporated into calculation of
subject-specific extinction coefficients.
8. The method according to claim 3, wherein effects of the internal
factors are used in defining a subject-specific transformation that
is used to transform in-vivo measurement results to the
Lambert-Beer model.
9. The method of claim 8, wherein the internal factors affecting
the transformations of the modulation ratios from the in-vivo
values to the Lambert-Beer values are derived based on the
transmitted signal through the tissue.
10. The method according to claim 4, wherein tissue filter effect
is corrected according to the equation 30 E Eff = E kl 0 ( 1 + S (
Tissue SHIFT SLOPE = 1 - 1 ) ) where E.sub.kl.sup.0 is included in
the nominal calibration, S denotes the column array in Eq. 8 and
the matrix multiplications are performed element by element
({circle over (x)}) or element by row (.cndot.), respectively.
11. The method according to claim 4, wherein external temperature
and the LED drive power induced wavelength shift is corrected
according to the equation 31 E TEMP EFF = E kl 0 ( 1 + ( / 5 nm ) (
Temp SHIFT = 5 nm - 1 ) ) where .DELTA..lambda. is the array in Eq.
11 and 32 Temp SHIFT = 5 nm is as in equation 12.
12. The method according to claim 8, wherein the nominal
calibration characteristics include nominal values for the
Functional Light Transmission (FLT) of the apparatus, the
establishment step includes calculating new values for the
Functional Light Transmission (FLT) of the apparatus based on
tissue-induced changes, and the subject-specific transformation is
established on the basis of the nominal and new values
13. The method according to claim 8, wherein the nominal
calibration characteristics include nominal values for function
F.sub.kl of the apparatus, tissue-induced changes includes
calculating new values for the function F.sub.kl of the apparatus,
and the establishment step includes determining the
subject-specific transformation on the basis of the nominal and new
values, wherein the function F.sub.kl corresponds to the ratio 33 f
a ( a k - v k ) + v k f a ( a l - v l ) + v l , where .mu..sub.v
and .mu..sub.a are the absorption coefficients of venous and
arterial blood, respectively, as determined in the Lambert-Beer
domain, f.sub.a is the volume fraction of arterial blood, and the
superscripts k and l indicate the wavelength.
14. The method according to claim 13, wherein the nominal and new
values for the Function F.sub.kl are calculated on the basis of
measured fluctuation of the DC component of the radiation received
by the detector.
15. The method according to claim 1, wherein the at least two light
absorbing substances include oxyhemoglobin (HbO2) and reduced
hemoglobin (RHb).
16. The method according to claim 1, wherein all the method steps
are performed at each heart beat of the subject.
17. A pulse oximeter for non-invasively determining in in-vivo
measurement the amount of at least two light absorbing substances
in the blood of a subject, the pulse oximeter having an interface
to a sensor for receiving an output signal at at least two distinct
wavelengths and controlling operation of the sensor, the pulse
oximeter further comprising signal processing means for extracting
AC and DC signals for each wavelength, each wavelength signal
representing the absorption caused by the blood of the subject,
pre-stored data in a memory, comprising data of initial
characterization measurements, data of nominal characteristics
describing calibration conditions under which a predetermined
calibration of the apparatus has been applied, and data of nominal
calibration and nominal calibration characteristics, an extinction
coefficient compensation block operatively connected to the first
signal processing means, and to the memory for reading data, said
block comprising first calculation means adapted to correct the
nominal characteristics of the sensor on living tissue of the
subject, a transformation compensation block operatively connected
to the first signal processing means for receiving the DC signals
and to the memory for reading data, said block comprising second
calculation means adapted to correct the transformation values
based on the changes in the DC signals induced by tissue of the
subject, and a hemoglobin fraction calculation unit connected to
the extinction coefficient compensation block and the calibration
values compensation block, said unit comprising means for applying
the corrected nominal characteristics and the corrected nominal
calibration values in solving the hemoglobin fractions.
18. The pulse oximeter as in claim 17, wherein the pre-stored data
include nominal transformation values of the Lambert-Beer
model.
19. The pulse oximeter as in claim 17, wherein the pre-stored data
include nominal calibration values of the Lambert-Beer model.
20. The pulse oximeter as in claim 17, wherein the extinction
coefficient compensation block and the transformation compensation
block are time synchronized by synchronization means and the
nominal calibration is compensated on heart beat-to-beat basis.
21. A sensor for collecting measurement data for a pulse oximeter
intended for non-invasively determining in in-vivo measurement the
amount of at least two light absorbing substances in the blood of a
subject, the sensor comprising at least one light emitter for
emitting light at a minimum of two different wavelengths, a light
detector for receiving said light at each of said wavelengths and
producing an electrical output signal indicating the received light
at the said wavelengths, a pulse oximeter readable memory data
pre-stored in the memory, comprising data of initial
characterization measurements, data of nominal characteristics
describing calibration conditions under which a predetermined
calibration of the pulse oximeter has been establihed, and data of
nominal calibration values, wherein said data allows the pulse
oximeter connected to the sensor to determine tissue-induced
changes in the nominal characteristics when light is propagated
through said tissue.
22. A sensor as in claim 21, further comprising a temperature
sensitive element operatively connected to the light emitter for
producing electrical signals responsive to the temperatures of the
semiconductor junction.
23. The sensor according to claim 21, wherein the light emitters
are Light Emitting Diodes.
24. The sensor according to claim 21, wherein the light emitter is
a laser.
25. The sensor according to claim 21, wherein the light emitter is
light conduction means for conducting radiation from the emitting
component to the tissue site, at which the measurement is
performed.
26. The sensor according to claim 21, wherein the means for
receiving radiation is radiation conduction means for conducting
radiation from the tissue site to the detector component.
27. The sensor according to claim 21, wherein the data of nominal
characteristics includes the Functional Light Transmission (FLT) of
the pulse oximeter.
28. The sensor according to claim 21, wherein the data of nominal
calibration values includes function F.sub.kl of the pulse oximeter
in nominal conditions, wherein the function F.sub.kl corresponds to
the ratio 34 f a ( a k - v k ) + v k f a ( a l - v l ) + v l ,
where .mu..sub.v and .mu..sub.a are the absorption coefficients of
venous and arterial blood, respectively, as determined in the
Lambert-Beer domain, f.sub.a is the volume fraction of arterial
blood, and the superscripts k and l indicate the wavelength.
Description
FIELD OF THE INVENTION
[0001] The invention relates generally to pulse oximeters used to
detect blood oxygenation. More specifically, the invention relates
to a method for taking into account human variability in pulse
oximeters. The invention further relates to a sensor allowing
compensation for the inaccuracies caused by human variability, the
sensor being an integral part of the pulse oximeter.
BACKGROUND OF THE INVENTION
[0002] Pulse oximetry is at present the standard of care for
continuous monitoring of arterial oxygen saturation (SPO.sub.2).
Pulse oximeters provide instantaneous in-vivo measurements of
arterial oxygenation, and thereby an early warning of arterial
hypoxemia, for example.
[0003] A pulse oximeter comprises a computerized measuring unit and
a probe attached to the patient, typically to a finger or ear lobe.
The probe includes a light source for sending an optical signal
through the tissue and a photo detector for receiving the signal
after transmission through the tissue. On the basis of the
transmitted and received signals, light absorption by the tissue
can be determined. During each cardiac cycle, light absorption by
the tissue varies cyclically. During the diastolic phase,
absorption is caused by venous blood, tissue, bone, and pigments,
whereas during the systolic phase there is an increase in
absorption, which is caused by the influx of arterial blood into
the tissue. Pulse oximeters focus the measurement on this arterial
blood portion by determining the difference between the peak
absorption during the systolic phase and the constant absorption
during the diastolic phase. Pulse oximetry is thus based on the
assumption that the pulsatile component of the absorption is due to
arterial blood only.
[0004] Light transmission through an ideal absorbing sample is
determined by the known Lambert-Beer equation as follows:
I.sub.out=I.sub.ine.sup.-.epsilon.DC (1)
[0005] where I.sub.in is the light intensity entering the sample,
I.sub.out is the light intensity received from the sample, D is the
path length through the sample, .epsilon. is the extinction
coefficient of the analyte in the sample at a specific wavelength,
and C is the concentration of the analyte. When I.sub.in, D, and
.epsilon. are known, and I.sub.out is measured, the concentration C
can be calculated.
[0006] In pulse oximetry, in order to distinguish between two
species of hemoglobin, oxyhemoglobin (HbO.sub.2), and
deoxyhemoglobin (RHb), absorption must be measured at two different
wavelengths, i.e. the probe includes two different light emitting
diodes (LEDs). The wavelength values widely used are 660 nm (red)
and 940 nm (infrared), since the said two species of hemoglobin
have substantially different absorption values at these
wavelengths. Each LED is illuminated in turn at a frequency which
is typically several hundred Hz.
[0007] The accuracy of a pulse oximeter is affected by several
factors. This is discussed briefly in the following.
[0008] Firstly, the dyshemoglobins which do not participate in
oxygen transport, i.e. methemoglobin (MetHb) and carboxyhemoglobin
(COHb), absorb light at the wavelengths used in the measurement.
Pulse oximeters are set up to measure oxygen saturation on the
assumption that the patient's blood composition is the same as that
of a healthy, non-smoking individual. Therefore, if these species
of hemoglobin are present in higher concentrations than normal, a
pulse oximeter may display erroneous data.
[0009] Secondly, intravenous dyes used for diagnostic purposes may
cause considerable deviation in pulse oximeter readings. However,
the effect of these dyes is short-lived since the liver purifies
blood efficiently.
[0010] Thirdly, coatings like nail polish may in practice impair
the accuracy of a pulse oximeter, even though the absorption caused
by them is constant, not pulsatile, and thus in theory it should
not have an effect on the accuracy.
[0011] Fourthly, the optical signal may be degraded by both noise
and motion artifacts. One source of noise is the ambient light
received by the photodetector. Many solutions have been devised
with the aim of minimizing or eliminating the effect of the
movement of the patient on the signal, and the ability of a pulse
oximeter to function correctly in the presence of patient motion
depends on the design of the pulse oximeter. One way of canceling
out the motion artefact is to use an extra wavelength for this
purpose.
[0012] A further factor affecting the accuracy of a pulse oximeter
is the method used to calibrate the pulse oximeter. Usually the
calibration is based on extensive empirical studies in which an
average calibration curve is determined based on a high number of
persons. By means of this calibration curve, which relates the
oxygen saturation of blood to pulse oximeter signals, the average
difference between the theory and practice (i.e. in-vivo
measurements) is taken into account. The calibration curve
typically maps the measured in-vivo signal to a corresponding
SpO.sub.2 value.
[0013] Pulse oximeters, however, can also utilize the Lambert-Beer
model for calculating the concentrations of the different Hb
species. In this method of calibration, the measurement signals
must first be transformed into signals applicable to the
Lambert-Beer model for calculation. This transformation constitutes
the calibration of the pulse oximeter, since it is the step which
adapts the in-vivo signals to the Lambert-Beer theory, according to
which the pulse oximeter is designed to operate. Thus, the
calibration curves can also be in the form of transformations used
to adapt the actual in-vivo measurements to the Lambert-Beer
model.
[0014] Transformations are discussed for example in U.S. Pat. No.
6,104,938, which discloses a calibration method based on the
absorption properties of each hemoglobin component, i.e. on the
extinction coefficients of blood. In this method, the effective
extinction coefficients are determined for each light signal via a
mathematical transformation from the extinction coefficients
according to the Lambert-Beer theory.
[0015] Below, the solution according to the invention is discussed
with reference to a pulse oximeter utilizing the above-mentioned
transformations and four different wavelengths. As mentioned above,
U.S. Pat. No. 6,104,938 discloses a pulse oximeter utilizing the
transformations.
[0016] FIG. 1 is a block diagram of a pulse oximeter utilizing four
different wavelengths. Light from four different LEDs 10a, 10b,
10c, and 10d, each operating at a respective wavelength, passes
into patient tissue, such as a finger 11. The light propagated
through or reflected from the tissue is received by a photodetector
12, which converts the optical signal received into an electrical
signal and feeds it to an input amplifier 13. The amplified signal
is then supplied to a control unit 14, which carries out
calculation of the amount of the Hb-derivatives in the blood. The
control unit further controls the LED drive 15 to alternately
activate the LEDs. As mentioned above, each LED is typically
illuminated several hundred times per second.
[0017] When each LED is illuminated at such a high rate as compared
to the pulse rate of the patient, the control unit obtains a high
number of samples at each wavelength for each cardiac cycle of the
patient. The value of these samples (i.e. the amplitude of the
received signal) varies according to the cardiac cycle of the
patient, the variation being caused by the arterial blood, as
mentioned above. The control unit 14 therefore utilizes four
measurement signals, as shown in FIG. 2, each being received at one
of the wavelengths.
[0018] In order for variations in extrinsic factors, such as the
brightness of the LEDs, sensitivity of the detector, or thickness
of the finger, to have no effect on the measurement, each signal
received is normalized by extracting the AC component oscillating
at the cardiac rhythm of the patient, and then dividing the AC
component by the DC component of the light transmission or
reflection. The signal thus obtained is independent of the
above-mentioned extrinsic factors. Thus in this case the control
unit utilizes four normalized signals, which are in the following
denoted with 1 d A i = A C i D C i ,
[0019] where i is the wavelength in question (in this basic
embodiment of the multi-wavelength pulse oximeter i=1, 2, 3, 4),
AC.sub.i is the AC component at wavelength i, and DC.sub.i is the
DC component at wavelength i. The signals dA.sub.i are also
referred to below as modulation signals. The modulation signals
thus indicate how absorption is affected by the arterial blood of
the patient.
[0020] The above-described measurement arrangement corresponds to a
conventional four-wavelength pulse oximeter. The operation of the
pulse oximeter is discussed in more detail below.
[0021] The theory of pulse oximetry is generally presented as being
based on the Lambert-Beer Law. According to this theory, light
transmission through the tissue at each wavelength is exponentially
dependent on the absorbance of the tissue (Eq. 1). This theory is
generally accepted and established as a good model for pulse
oximetry.
[0022] Next to be discussed is the theory and formalism on which
the method of the invention is based.
[0023] According to the Lambert-Beer theory and for a system of two
analytes, the signals described above can be presented as
follows:
dA.sub.1=dA.times.(.epsilon..sub.1.sup.HbO.sup..sub.2.times.HbO.sub.2+.eps-
ilon..sub.1.sup.RHb.times.RHb)
dA.sub.2=dA.times.(.epsilon..sub.2.sup.HbO.sup..sub.2.times.HbO.sub.2+.eps-
ilon..sub.2.sup.RHb.times.RHb)
dA.sub.3=dA.times.(.epsilon..sub.3.sup.HbO.sup..sub.2.times.HbO.sub.2+.eps-
ilon..sub.3.sup.RHb.times.RHb)
dA.sub.4=dA.times.(.epsilon..sub.4.sup.HbO.sup..sub.2HbO.sub.2+.epsilon..s-
ub.4.sup.RHb.times.RHb)
RHb=1-HbO.sub.2
[0024] where dA is a common factor which depends on the absolute
values, i.e. inter alia on the total amount of hemoglobin,
.epsilon..sub.i.sup.HbO.sup..sub.2 is the extinction coefficient of
oxyhemoglobin at wavelength i (i=1-4), .epsilon..sub.i.sup.RHb is
the extinction coefficient of deoxyhemoglobin at wavelength i,
HbO.sub.2 is the concentration fraction of oxyhemoglobin, and RHb
is the concentration fraction of deoxyhemoglobin.
[0025] Using a matrix notation, the above dependencies can be
expressed for a system of n wavelengths and n analytes as follows:
2 ( d A 1 d A 2 d A n ) = C * ( 11 1 n 21 2 n n1 nn ) ( HbX 1 HbX 2
HbX n ) , ( 2 )
[0026] where dA.sub.i is the differential change in absorption
(i.e. the modulation signal) at wavelength .lambda.i, .epsilon.ij
is the extinction coefficient of the hemoglobin derivative
HbX.sub.i at wavelength .lambda.i, and the constant C accounts for
the change of units to fractional percentages of the concentrations
of the analytes HbX.sub.j.
[0027] FIG. 3 shows the extinction coefficients
(.epsilon..sup.HbO.sup..su- b.2 and .epsilon..sup.RHb) of
oxyhemoglobin (HbO.sub.2) and deoxyhemoglobin (RHb) as a function
of the wavelength. Point P shown in the figure is the isobestic
point of oxyhemoglobin (HbO.sub.2) and deoxyhemoglobin (RHb). The
point has the special property that the modulation signal at the
wavelength in question does not depend on the respective
proportions (relative concentrations) of the hemoglobin species.
Thus at the wavelength of point P the effect of the relative
concentrations of oxyhemoglobin and deoxyhemoglobin on the result
of the measurement is nil. It should be noted, however, that the
modulation signal is independent of the relative concentrations
only, not of the absolute concentrations. Thus, the absolute amount
of the hemoglobin species has an effect on the result of the
measurement.
[0028] As is known, there is a difference between the Lambert-Beer
theory and the practical measurements. The difference is due to the
fact that the Lambert-Beer theory does not take into account the
scattering and non-homogeneity of the tissue, whereas the actual
extinction coefficients are also dependent on the scattering of
light caused by the tissue and blood, and on the combined effect of
absorption and scattering. The larger the proportion of the
attenuation caused by absorption and scattering, the larger is the
correction needed between the actual and the theoretical
(non-scatter) domains. This correction between these two domains
can be represented by the transformation curves discussed above, by
means of which the actual in-vivo measurements are mapped to the
Lambert-Beer model.
[0029] The transformation can be expressed, for example, as
follows:
N.sub.kl.sup.L-B=g.sub.M.sup.-1(N.sub.kl.sup.in-vivo) (3)
[0030] where 3 N kl = A k A l
[0031] is the modulation ratio (the superscript indicating the
domain) and the subscripts k and l indicating the wavelengths in
question), and g is the transformation, for instance in the form of
a polynomial function, transforming the L-B N-values to the
corresponding N-values in the in-vivo domain. The g.sup.-1 in Eq. 3
is the inverse transformation, i.e. the inverse function, for
transforming the measured in-vivo values to the ideal, non-scatter,
values in the L-B domain
[0032] FIGS. 4a to 4f illustrate the average transformation curves
measured for a pulse oximeter, where the two wavelengths for
measuring the two species of hemoglobin are 660 nm and 900 nm and
the third wavelength is either 725 nm or 805 nm. FIGS. 4a to 4c
illustrate the transformation curves for a pulse oximeter with the
third wavelength being 725 nm, and FIGS. 4d to 4f illustrate the
transformation curves for a pulse oximeter with the third
wavelength being 805 nm. Each curve shows the Lambert-Beer
N.sub.k,l as a function of the in-vivo N.sub.kl at wavelengths k
and l.
[0033] FIG. 5 is a flow diagram describing the general measurement
principle described in U.S. Pat. No. 6,104,938. In this method, the
above-mentioned N.sub.kl.sup.in-vivo values are first determined
from the dA.sub.i values measured (step 51). The average
transformations g.sub.kl are then used to convert the measured
in-vivo values to values N.sub.kl.sup.L-B, which can be used in the
ideal Lambert-Beer model (step 52). Other input values needed for
the Lambert-Beer model are also determined (step 53). In practice
these input values are the ideal (nominal) extinction coefficients
of the analytes to be measured, the extinction coefficients being
given for the center wavelengths used in the measurement. The
converted transformation values and the nominal input values (i.e.
nominal extinction coefficients) are then used according to the
Lambert-Beer model to calculate the concentrations of the desired
analytes (step 54). Thus in this approach the in-vivo values
N.sub.kl.sup.in-vivo measured from the tissue are converted to the
ideal in-vitro (cuvette) environment, where the ideal oximetry
model (i.e. the Lambert-Beer model) is applied to yield the desired
concentrations.
[0034] In the standard two wavelength pulse oximetry the prior art
technique is to map the modulation ratio N.sub.kl.sup.in-vivo
directly to the SpO2 percentage measured. In this simple case the
transformation is not necessary, though the transformation
technique together with the solution in the Lambert-Beer domain can
be utilized as well.
[0035] There are two basic ways to determine the average
transformation, a theoretical approach and an empirical approach.
In the empirical approach the measurements are made in the tissue
by taking blood samples and measuring the actual proportions of the
hemoglobin species and then determining the value of
N.sub.kl.sup.L-B on the basis of the measured proportions. The
transformation is then obtained as the relationship between the
values based on the blood samples and the values given by empirical
measurements as measured by the pulse oximeter. The theoretical
approach, in turn, is based on a known tissue model, which takes
into account the characteristics of the tissue as referred to
above, which are ignored in the Lambert-Beer model. A first value
is determined for in-vivo N.sub.kl by means of the tissue model and
a second value on the basis of the Lambert-Beer model. The tissue
parameters of the model are determined so that the known
2-wavelength calibration (so-called R-curve) is reproduced. Then
using these tissue parameters and the wavelength dependence of the
tissue model, the relation of the in-vivo N.sub.kl and the
Lambert-Beer N.sub.kl is extrapolated to other wavelengths in order
to obtain the transformations at these new wavelengths. Thus in the
theoretical approach no new empirical measurements are made.
[0036] In practice the transformation can be a quadratic equation
yielding a correction of the order of 20 percent to the measured
N.sub.kl.sup.in-vivo value, for example. As discussed below, the
transformation data (i.e. the transformation curves) are preferably
stored in numeric form in the pulse oximeter or the sensor. The
number of transformation curves stored in the pulse oximeter can
vary, depending on the number of wavelengths used, for example.
Typically there is a transformation curve for each wavelength
pair.
[0037] As mentioned above, the accuracy of a pulse oximeter
utilizing an average transformation is not necessarily sufficient,
especially if analytes which are weak absorbers are to be measured
or if two analytes absorb similarly, whereby it is difficult to
distinguish the said analytes from each other.
[0038] Further, each patient (i.e. subject of the measurement) has
a calibration curve of his or her own, which deviates from the
average calibration curve calculated on the basis of a high number
of patients. This is due to the fact that for each patient the
characteristics of the tissue through which light is transmitted
deviate from those of an average patient.
[0039] This causes one drawback of the current pulse oximeters;
they are incapable of taking this human variability into account.
Human variability here refers to any and all factors causing
patient-specific variation in the calibration curve, including
time-dependent changes in the calibration curve of a single
patient. As discussed in the above-mentioned U.S. Patent,
subject-dependent variation can also be seen as an effect of a
third substance, such as a third hemoglobin species in the blood.
However, the variation can also be interpreted as a
subject-dependent change in the calibration curve of the pulse
oximeter.
[0040] Without compensation for human variability, the accuracy of
current pulse oximeters is about .+-.2% SpO2. However, in
multi-wavelength applications in general, and especially if weak
absorbers, such as COHb, are to be measured, the human variability
represents a much more serious problem. Therefore, techniques of
compensation for these inaccuracies are called for.
[0041] It is an objective of the invention to bring about a
solution by means of which the effects caused by the tissue of the
subject can be taken into account when a pulse oximeter is
calibrated. In other words, it is an objective of the present
invention to create a pulse oximeter which can take into account
the differences caused by an individual subject as compared to the
average calibration or transformation curve which the current pulse
oximeter relies on.
[0042] A further objective of the invention is to bring about a
general-purpose solution for the compensation of inaccuracies
caused by human variability in pulse oximetry, a solution which is
not limited to the particular general calibration method employed
in the pulse oximeter, but which can be applied to any pulse
oximeter regardless of its current built-in calibration method.
SUMMARY OF THE INVENTION
[0043] These and other objectives of the invention are accomplished
in accordance with the principles of the present invention by
providing a mechanism by means of which the subject-specific
deviation in the tissue-induced effects on the accuracy of the
pulse oximeter can be taken into account. Thus, the accuracy of the
pulse oximeter is improved by taking into account the
subject-specific light transmission through the tissue, and
changing the values input to the ideal model, i.e. the nominal
transformation and the nominal extinction coefficients, on the
basis of the measurement to compensate for the subject-specific
changes.
[0044] In the method of the invention, the effect of tissue is
taken into account and the inaccuracies caused by subject-specific
variation in that effect are compensated for. This is implemented
by defining a nominal calibration for the apparatus and making
initial characterization measurements in order to define the
characteristics which describe the conditions under which the
nominal calibration has been defined. Reference data indicating the
characteristics are stored for subsequent in-vivo measurements in
which light transmission through the actual tissue of the patients
is measured. (Initial characterization measurements here refer to
the measurements performed before the apparatus is taken into use.
The term is used to refer to A the characterization measurements
without tissue, i.e. mainly characterization of the optical
components of the sensor, and B the characterization measurements
of the tissues in volunteered or hospitalized subjects for which
the nominal calibration of the oximeter is established.) Individual
subject-specific calibration is then defined based on the nominal
calibration, and the reference data created in connection with the
initial characterization measurements in the subject group in the
nominal calibration, the in-vivo measurements in an individual
patient and the in-vivo characterization measurements, defining the
tissue characteristics of the individual patient in the in-vivo
measurement. (In-vivo characterization measurements here refer to
the characterizations performed when the apparatus is in actual
use.) The in-vivo characterization also includes a step in which
the information from the optical properties of the particular
sensor, used in the in-vivo measurement of the individual patient,
is read into the oximeter. Thus, the inaccuracies are eliminated by
means of comparing the optical properties of the sensors and the
characteristics of the tissues in the calibration measurements and
the in-vivo measurements in the individual patient. Thus the
initial characterization measurements are used to create the
reference data so that light transmission measured subsequently
through the tissue of a subject can be used to correct the nominal
calibration for that particular subject.
[0045] Thus in one aspect the invention provides a method for
compensating for subject-specific variability in an apparatus
intended for non-invasively determining the amount of at least two
light absorbing substances in the blood of a subject and being
provided with emitter means for emitting radiation at a minimum of
two different wavelengths and with detector means for receiving the
radiation emitted, the method comprising the steps of
[0046] carrying out initial characterization measurements, said
measurements to include the measuring of radiation received by the
detector,
[0047] based on the initial characterization measurements,
establishing nominal characteristics describing conditions under
which the nominal calibration is established,
[0048] calibrating the apparatus using a nominal calibration,
[0049] storing reference data indicating the nominal
characteristics and nominal calibration,
[0050] performing in-vivo characterization measurements on a living
tissue, said measurements to include the measuring of radiation
emitted through the tissue and received by the detector means is
measured,
[0051] performing simultaneously with the in-vivo characterization
measurement measurements, wherein the pulsative light absorption is
measured,
[0052] based on the in-vivo measurements, establishing
characteristics describing conditions under which the in-vivo
measurement is done,
[0053] based on the in-vivo characteristics and the reference data
stored, determining tissue-induced changes in the nominal
characteristics, and
[0054] compensating for subject-specific variation in the in-vivo
measurements by correcting the nominal calibration on the basis of
the tissue-induced changes.
[0055] In a preferred embodiment of the invention the method is
divided in two steps so that the first step compensates for the
inaccuracies caused by tissue-induced and sensor-induced wavelength
shift and the second step compensates for the inaccuracies caused
by internal effects occurring in the tissue. The first step is then
used to correct the extinction coefficients of the blood analytes
to be measured, and the second step is used to correct the average
transformation stored in the pulse oximeter.
[0056] In a further preferred embodiment of the invention the
effect of the temperature is also compensated for in connection
with the first step.
[0057] The method is not limited to pulse oximeters explicitly
using the transformations, but can be applied to any pulse
oximeter. However, the method is preferably applied to a pulse
oximeter based on a transformation, since in a preferred embodiment
the method is implemented by carrying out changes separately in the
transformation and in the extinction coefficients.
[0058] In another aspect, the invention provides an apparatus for
non-invasively determining the amount of at least two light
absorbing substances in the blood of a subject, the apparatus
comprising
[0059] emitter means for emitting radiation at a minimum of two
different wavelengths,
[0060] detector means for receiving said radiation at each of said
wavelengths and producing at least two electrical output
signals,
[0061] first signal processing means for processing said output
signals and producing a modulation signal for each wavelength,
whereby each modulation signal represents the pulsating absorption
caused by the arterialized blood of the subject,
[0062] second signal processing means for applying a predetermined
calibration on said modulation signals, whereby transformed
modulation signals applicable in the Lambert-Beer model are
obtained,
[0063] memory means for storing and reading reference data
indicating nominal characteristics under which said predetermined
calibration has been applied,
[0064] first compensation means, operatively connected to the
memory means, for determining tissue-induced changes in the nominal
characteristics,
[0065] second compensation means, operatively connected to the
first compensation means, for defining a subject-specific
calibration by correcting the predetermined calibration on the
basis of the tissue-induced changes, and
[0066] calculation means, responsive to the second compensation
means, for determining said amounts, and
[0067] display means.
[0068] In a still further aspect, the invention provides a sensor
for collecting measurement data for a pulse oximeter intended for
non-invasively determining the amount of at least two light
absorbing substances in the blood of a subject, the sensor
comprising
[0069] emitter means for emitting radiation at a minimum of two
different wavelengths,
[0070] detector means for receiving said radiation at each of said
wavelengths and for producing at least two electrical output
signals,
[0071] storage means including nominal calibration and reference
data indicating nominal characteristics describing calibration
conditions of the pulse oximeter, said data allowing apparatus
connected to the sensor to determine tissue-induced changes in the
nominal characteristics when radiation is emitted through said
tissue.
[0072] Preferred embodiments of the invention are discussed in more
detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0073] In the following, the invention and its preferred
embodiments are described more closely by referring to the appended
drawings, wherein:
[0074] FIG. 1 illustrates the basic embodiment of a pulse oximeter
according to the present invention;
[0075] FIG. 2 illustrates the signals utilized in the pulse
oximeter of FIG. 1;
[0076] FIG. 3 shows the extinction coefficients of two different
species of hemoglobin as a function of wavelength;
[0077] FIG. 4a to 4f illustrate the average transformation curves
for two different pulse oximeters;
[0078] FIG. 5 is a flow diagram illustrating the prior art
calibration method;
[0079] FIG. 6 illustrates an example of the transmission curve of
human tissue, the curve being employed in the compensation of the
inaccuracies caused by tissue-induced wavelength shift;
[0080] FIG. 7 is a flow diagram illustrating the general principle
according to the present invention;
[0081] FIG. 8 is a flow diagram illustrating steps of initial
characterization measurement phase;
[0082] FIG. 9 depicts stages of the phase of establishing nominal
characteristics;
[0083] FIG. 10 is a block diagram depicting different steps of the
first stage in FIG. 9;
[0084] FIG. 11 is a block diagram depicting different steps of the
second stage in FIG. 9;
[0085] FIG. 12 shows compensation of human variability in the
in-vivo measurement;
[0086] FIG. 13 depicts received intensity as a function of
wavelength;
[0087] FIG. 14 depicts frequency baseline fluctuations of a
plethysmographic wave signal;
[0088] FIG. 15 illustrates an embodiment of a sensor according to
the invention;
[0089] FIG. 16 illustrates main frame blocks of a pulse oximeter,
and
[0090] FIG. 17 illustrates main blocks of a sensor.
DESCRIPTION OF THE INVENTION
[0091] Guidelines for Implementing the Invention
[0092] The method of the present invention is implemented in the
control unit of the pulse oximeter on the basis of the four
modulation signals described above, i.e. the novelty of the system
resides within the control unit itself. However, to be able to
perform the self-calibration in conjunction with each patient, the
control unit requires some pre-calculated data, which is stored in
the memory of the pulse oximeter. Instead of being stored in
conjunction with the control unit, this data, or at least part of
it, can also be stored in the sensor part of the pulse oximeter.
The sensor part, including at least the LEDs and the photo
detector, is connected to the signal processing part, which
includes the control unit. Consequently, depending on the overall
configuration, the novelty can also reside partly in the
sensor.
[0093] Human tissue can influence the accuracy of a pulse oximeter
by two different mechanisms. First a direct wavelength shift is
caused in the LED emission due to the filtering effect of the
tissue. Namely, on one side of the LED center wavelength the
absorption may be larger than on the other, whereby the center
wavelength of the transmitted or reflected light is effectively
shifted towards the region with smaller absorption. The second
mechanism is a subtle one. It arises from the fact that the
arterial blood is in interaction with the surrounding tissue, which
can either increase or decrease the effective path length through
the arterial blood layer. The first mechanism is in this context
termed the external mechanism, since it affects factors external to
the tissue (wavelength). The second mechanism is called the
internal mechanism, as it is caused by internal factors in the
tissue itself.
[0094] Therefore, adding two compensating processes to the prior
art mechanism preferably compensates for the subject-specific
variations in the influence of tissue. In other words, prior art
nominal calibration is corrected with two compensating process. The
first process attends to the subject-specific variation in the
external mechanism, and the second process attends to the
subject-specific variation in the internal mechanism. The first
process preferably controls the extinction coefficients to be input
to the Lambert-Beer model, while the second process preferably
controls the value of the transformation used to transform the
modulation ratios N.sub.kl.sup.in-vivo to the Lambert-Beer model
N.sub.kl.sup.L-B. The linear equations with the unknown analyte
concentrations are then solved in the Lambert-Beer model, as in the
prior art method. The degree of these compensations is determined
by DC light transmission through the tissue (the measured DC
signal), measured both in the initial characterization and in-vivo
characterization conditions.
[0095] a) Compensating for Tissue Filter Effect to Nominal
Extinction Coefficient Values
[0096] Nominal extinction coefficients determined without tissue
must be corrected by measuring DC light transmission through tissue
and determining the optical characteristics of the particular
sensor without tissue. Based on the without-tissue sensor
characteristics new nominal extinction coefficients without tissue
for the particular in-vivo sensor are calculated. Then in the
actual in-vivo measurement, subject-specific extinction
coefficients, i.e. individual extinction coefficients of a patient,
are calculated for each patient and for each time moment at which a
change of tissue properties in-vivo has been observed. Finally,
based on the without-tissue optical properties and the in-vivo
tissue properties, the final extinction coefficient values are
found continuously in real-time, whereupon said values are input to
the Lambert-Beer model.
[0097] We next discuss the mechanisms by which tissue changes the
extinction coefficients (external mechanism) and the transformation
functions (internal mechanism). We introduce a parameter called the
Functional Light Transmission (FLT).sub.i at a wavelength i, since
it is used below in order to make all DC.sub.i values (measured at
varying LED emission powers at the four discrete wavelengths i)
comparable to each other. Using DC values comparable to each other
is in practice a prerequisite for unveiling the real effect of the
tissue on the measurements and the characteristics of the tissue.
In order to obtain the comparable units, the DC light transmission
for each LED channel (wavelength) is first measured at a certain
emitter drive current, and the measured DC value is then reduced in
the preamplifier to a detector current, which is normalized to an
emitter current value of 1 mA. When measured without the tissue in
the probe, this result is called the Current Transfer Ratio (CTR)
of the probe. CTR characterizes the sensor design and the
efficiency of the light transmission from the emitters to the
detector. It is usually of the order of a few microAmps (of
detector current) per one milliAmp (of LED current). Now the tissue
(e.g. a finger) is inserted into the probe and the CTR is again
measured. This result is now called the Functional Current Transfer
Ratio (FCTR) because it is the CTR measured under conditions of the
function of the pulse oximeter, i.e. when the tissue is in place in
the probe. The FLT.sub.i is then calculated for each emitter
(wavelength) as follows:
FLT(emitter# k)=FCTR(1 mA-emitter-current)/CTR(1
mA-emitter-current)
[0098] Next the CTR and the FCTR concepts will be linked to the
Lambert-Beer absorption model and to the actual measured
intensities in the pulse oximeter. The CTR obviously describes how
the external probe design factors, such as the color and geometry
of the probe, affect the light transmission to the detector. On the
other hand, the FLT can be associated with the true transmission
through the tissue in units which are normalized to the emitter
efficiency. Therefore, Eq. 1 can be written in a slightly different
form, as it is often written in transport theory:
I=I.sub.0 exp(-.alpha.*d)=I.sub.0
exp(-.alpha..sub.int*d)exp(-.alpha..sub.- est*d) (4),
[0099] where d is the tissue thickness and a is an effective
absorption coefficient. The above equation has been divided into
two components. The attenuation factor with .alpha..sub.int
accounts for all internal absorption effects, such as blood and can
be associated to the FLT-value as the FLT equals one when no tissue
(no internal attenuation) is in the probe, and the factor with
.alpha..sub.ext accounts for all external attenuations, such as
geometrical factors and multiple surface reflections without light
penetration into the tissue, and can be associated with the CTR of
the probe. (The term d' denotes the `phantom` absorption thickness
parameter for the external effects.) The term .alpha..sub.ext is
mainly a SpO2 probe design issue which does not influence the
measurement accuracy as such, and thus it need not to be
compensated for by any means. The FLT at wavelength k can now be
defined as: 4 FLT k = I I 0 exp ( - ext * d ' ) = exp ( - int * d )
= FCTR k / CTR k . ( 5 )
[0100] The FLT thus describes light attenuation caused by the
tissue, and it can be related to the DC light transmission in the
pulse oximeter.
[0101] In the following the compensations are discussed in more
detail. The compensation of subject-variability causing wavelength
shift type interference (i.e. external mechanism) is discussed
first.
[0102] In the Lambert-Beer model (see Eq. 2) the effective
extinctions .sub..epsilon.ij.sup.effective for broadband emitters,
such as LEDs, can be calculated as follows: 5 ij effective = 1 W j
( ) * LED i ( ( T ) ) * DET ( ) * tissue ( ) , ( 6 )
[0103] where the integration is over the LED emission spectrum
LED.sub.i(.lambda.(T)), DET(.lambda.) represents the spectral
sensitivity of the detector, tissue(.lambda.) is the spectral
transmission of light through the tissue, .epsilon.j(.lambda.) is
the spectral extinction of the analyte in question, T is the
temperature, and W=.right brkt-bot.LED*DET*tissue*.delta..lambda.
represents a normalization factor.
[0104] In a preferred embodiment of the invention, the radiation
emitting means are Light Emitting Diodes (LED), but lasers emitting
at one single wavelength are also possible. For lasers the
effective extinction values are the extinction values at the laser
wavelength, which can depend, however, on the temperature of the
emitter component. In the case of a laser, Eg. 6 is thus not needed
to calculate the effective extinction value. In the preferred
embodiment of the invention the emitter and detector means are
located at the tissue site at which the radiation is transmitted
through the tissue, but the radiation can also be conducted to and
from the tissue site in a light conducting fiber or in equivalent
conduction means. In this case Eg. 6 shall also include a term for
the spectral transmission of the radiation conductor. A sensor
utilizing light conducting fibers can be as shown in FIG. 5 of the
above-mentioned U.S. Pat. No. 6,104,938.
[0105] The extinction coefficients can thus be calculated according
to this equation by determining all the above factors, which depend
on the actual wavelength values, i.e. the optical properties of the
sensor components and the tissue term. However, as the task of
determining the exact spectral value of Eg. 6 is not possible in
connection with a real-time pulse oximeter measurement using only a
few discrete wavelength bands, in practice the result of Eg. 6 has
to be approximated. The compensation is based on determining
nominal extinction coefficients and approximating their wavelength
dependence in advance at the factory and using this information in
the real measurement situation to approximate the final
subject-specific extinction coefficients.
[0106] The compensation algorithm will now be presented for a
4-wavelength pulse oximeter according to FIG. 1, having four LEDs
at nominal wavelengths of 627 nm, 645 nm, 670 nm, and 870 nm. The
extinction matrix for RHb (first column), HbO.sub.2, HbCO, and
metHb (last column) and for the above four wavelengths (627 nm on
top) is then nominally in L/(mmol*cm). 6 E kl 0 = ( 1.132 0.1799
0.2734 3.575 0.9182 0.1124 0.1337 2.411 0.7353 0.0885 0.0550 0.5796
0.2071 0.2772 0.010 0.5754 ) ( 7 )
[0107] This equation (7) describes the nominal extinction matrix
for the particular sensor used in the in-vivo measurement. Thus the
changes of the optical properties of the sensor components with
respect to the sensor components in the nominal calibration can
thus be directly incorporated into this extinction matrix, a new
nominal extinction matrix for the particular sensor.
[0108] The above extinction coefficients have been calculated
applying Eg. 6 at nominal LED drive temperature without the tissue
filtering term tissue(.lambda.). It then represents a nominal
extinction matrix for a SpO2 sensor before its attachment on the
tissue site. This extinction matrix is then altered on the basis of
the measured filtering effect caused by tissue, when the sensor is
attached on the site.
[0109] It is now assumed that the spectral tissue transmission is
as presented in FIG. 6, which shows spectral characteristics of
tissue in the same units for each wavelength, i.e. FLT as a
function of the wavelength, based on an empirical measurement. In a
continuous real-time SpO2 measurement, the transmission is known at
4 distinct wavelength values (the FLT values derived from the DC
values in the pulse oximeter) marked in the figure. At each
wavelength the slope of the tissue transmission curve can be
determined or approximated using the four transmission values. The
slope then determines the change in the tissue transmission in a
band of a predetermined width (100 nm in this example) around the
center of the LED band. We denote the slopes between 627 to 645 nm
and 645 to 670 nm by A and B, respectively. This definition of the
slopes is expressed as: 7 A = FLT ( 2 ) - FLT ( 1 ) ( 2 - 1 ) * (
FLT ( 1 ) + FLT ( 2 ) ) / 2 * 100 and B = FLT ( 3 ) - FLT ( 2 ) ( 3
- 2 ) * ( FLT ( 2 ) + FLT ( 3 ) ) / 2 * 100 ,
[0110] where FLT(.lambda.i) is the measured FLT value determined at
wavelength .lambda.i. The estimation of these slopes can be
improved by calculating the curvature at the center LED (645 nm).
This curvature (change of the slope/nm) is 8 curv = B - A ( 3 - 1 )
/ 2 .
[0111] Finally the expressions are obtained for the slopes s at the
three red wavelengths using A and B as parameters: 9 ( s 1 s 2 s 3
s 4 ) = ( A - curv * ( 2 - 1 ) / 2 ( A + B ) / 2 B + curv * ( 3 - 2
) / 2 - 0.5 ) , ( 8 )
[0112] where the slope at the IR wavelength has been estimated to
be constant as it cannot be determined by the other LEDs. If we had
had another LED, at about 800-1000 nm range, for example, it could
have been used for the estimation of the IR slope. Because the
extinction curves are very flat at 870 nm and the transmission is
usually rather high, the tissue prefilter cannot alter the
effective extinction coefficient from its nominal value
significantly. The approximation of a constant transmission slope
is thus considered sufficient.
[0113] In principle these slopes could be inserted in Eg.6 in order
to integrate the new true values for the extinction coefficients.
However, this is impractical to do in real-time, so a simpler
algorithm is presented below. We first calculate off-line the
relative changes of extinction coefficients for each analyte of the
system using Eg.6 and assuming that the value of the slope equals a
predetermined value, which is one in this example. This calculation
(assuming the slope is 1) results in a shift matrix of Eq. 9: 10
Tissue SHIFT SLOPE = 1 = ( 0.975 0.942 0.940 0.999 0.984 0.966
0.920 0.916 0.963 0.986 0.896 0.789 1.01 1.03 0.903 1.05 ) , ( 9
)
[0114] where the effective extinction of HbO.sub.2 at 645 nm is
0.966 times the original value, and the effective extinction of
HbCO at 670 nm is 0.896 times the original value, for example. The
proportional change of the extinction coefficient is thus the
matrix value minus one, i.e. (Tissue.sup.SLOPE-1-1). Thus, the
matrix of Eq. 9 defines the relative changes caused by the tissue,
assuming that the slope of the tissue transmission curve equals
one. During in-vivo measurement, the slope is continuously
estimated using the DC values. The ratio of the slopes then
indicates the relative change of a coefficient. In other words, if
the relative slope is s, the relative change is
s*(Tissue.sup.SLOPE-1-1). The relative changes are different for
different analytes since the extinction coefficients of the
different analytes behave differently as a function of
wavelength.
[0115] In real-time the effective extinction coefficients can thus
be calculated as follows: 11 E Eff = E kl 0 ( 1 + S ( Tissue SHIFT
SLOPE = 1 - 1 ) ) , ( 10 )
[0116] where S denotes the column array in Eq. 8 and the matrix
multiplications are performed element by element ({circle over
(x)}) or element by row (.cndot.), respectively.
[0117] b) Compensating for Temperature Effect to Extinction
Coefficient Values
[0118] Changes that the external temperature and the LED drive
power induce to the nominal extinction coefficients must also be
corrected. If the LEDs are not driven at the nominal drive
currents, their effective wavelength may also be shifted by the
temperature change at the LED p-n junction. The wavelength shift
induced by temperature is typically about 0.1-0.2 nm/.degree. C.
which is significant if the drive currents are high, as is usually
the case at wavelengths shorter than 660 nm. Thus, the extinction
matrix of Eq. 10 must also be compensated for in varying LED drive
conditions.
[0119] There are many ways to find out the temperature of the LED
p-n junction. One alternative is to add a temperature sensor on the
LED substrate and use the reading of the sensor for the
compensation of all LED emission wavelengths. Though the junction
temperature follows the substrate temperature according to some
empirical heat conduction model, the method may be unreliable
because the LED chip contact to the substrate and the internal heat
conductivity may vary considerably. A better way is therefore to
determine the junction temperature directly from the forward
voltage drop of the LED junction. The junction has typical diode
characteristics, which can be determined off-line for each LED
separately after assembling the LEDs on the substrate. It is even
possible to measure, with an optical spectrometer the shift of the
emission as a function of the LED forward voltage. Relating the
wavelength shift to the forward voltage assumes that the forward
voltage is measured during the operation of the pulse oximeter. The
circuit board of the pulse oximeter should thus preferably have
means for performing the forward voltage measurement. But if it
does not, the LED emission shift can be calibrated against the
temperature sensor at the substrate. The LED manufacturer
specifications for the temperature shift can then be used to
calculate the corresponding wavelength shift. Still another
practical compensation for the emitter temperature changes is to
map empirically the relationship of the emitter drive current to
the observed wavelength shift and to use this information to adjust
the in-vivo extinction coefficients for the sensor.
[0120] A method for temperature compensation of the LED emission is
now presented, assuming that the LED forward voltage is measured on
the circuit board. The wavelength shifts can then be calculated as
follows 12 ( 1 2 3 4 ) = ( k 1 k 2 k 3 k 4 ) ( V 1 V 2 V 3 V 4 ) ,
( 11 )
[0121] where the shift coefficients k.sub.i are values determined
empirically in advance and .DELTA.V.sub.i are the measured changes
of the forward voltage drops. For the 627-645-670-870 nm LEDs of
the sensor, the k-values are 0.06 nm/mV, 0.06 nm/mV, 0.09 nm/mV,
and 0.1 nm/mV, respectively.
[0122] As in the compensation discussed above relating to tissue
filtering, it is practical to first calculate the change in the
extinction coefficients off-line for a certain fixed wavelength
shift. In this example the relative changes of the extinction
coefficients are calculated, as in Eq. 9, for a 5 nm wavelength
shift for each of the four hemoglobin derivatives. The following
shift matrix is then obtained: 13 Temp SHIFT = 5 nm = ( 0.919 0.820
0.798 0.974 0.963 0.926 0.823 0.794 0.941 0.983 0.855 0.725 1.0
1.01 0.963 1.02 ) . ( 12 )
[0123] During in-vivo measurement, which will be later applied to a
patient in a hospital or like, the relative changes are then
calculated based on the measured wavelength shift. The ratio of the
wavelength shifts then indicates the relative change of a
coefficient caused by temperature. In other words, if the relative
change calculated for a wavelength shift of Y1 is r, the relative
change for the measured (in-vivo) wavelength shift of Y2 is
r.times.(Y2/Y1). The relative changes are different for different
analytes, since the extinction coefficients of the different
analytes behave differently as a function of wavelength.
[0124] The temperature compensated extinction coefficients are
thus: 14 E TEMP EFF = E kl 0 ( 1 + ( / 5 nm ) ( Temp SHIFT = 5 nm -
1 ) ) , ( 13 )
[0125] where .DELTA..lambda. is the array in Eq. 11. As mentioned
earlier, .DELTA..lambda. can also be estimated by reading the
temperature indicated by the temperature sensor on the LED
substrate or by measuring the LED drive current and using the
mapping of the current to the wavelength shift.
[0126] The compensation of the variability causing wavelength shift
type interference can now be summed up as follows: 15 E Eff = E kl
0 ( 1 + S ( Tissue SHIFT SLOPE = 1 - 1 ) ) ( 1 + ( / 5 nm ) ( Temp
SHIFT = 5 nm - 1 ) ) . ( 14 )
[0127] c) Compensating for Tissue Effect to Transformation
Functions
[0128] The second compensation (the internal mechanism) controls
the value of the transformation used to transform the modulation
ratios N.sub.kl.sup.in-vivo to the Lambert-Beer model
N.sub.kl.sup.L-B. Therefore nominal transformation values are first
calculated based on DC signals obtained from the sensor when the
tissue properties are averaged over a large group of people. Then,
in the actual in-vivo measurement subject-specific transformation
values, i.e. individual tissue characteristics affecting the
transformation values of a patient are calculated. Finally, based
on the in-vivo measurement, the nominal transformation values are
corrected, whereupon the corrected values are input to the
transformations used to transform the modulation ratios to the
Lambert-Beer model.
[0129] A practical implementation of the second compensating step
is now discussed by introducing a new variable called "path length
multiplier", since this will provide an easy way of understanding
the technique in accordance with the invention.
[0130] As mentioned above, the purpose of the invention is to
improve the accuracy of a pulse oximeter in situations in which the
blood volume, the red blood cell density or the hematocrit, the
total hemoglobin (g/dl), the division between the arterial and
venous blood compartment volumes, and the arterial-venous
saturation difference vary and produce human variability, which
worsens the accuracy of the SpO2 measurement. It is also the
purpose of the invention to compensate for the effect of skin
pigmentation (dark skin), which in part can be considered to belong
to the tissue prefilter category of compensations, but which also
influences via modifying the path length multiplier. This
modification is especially important for SpO2 ear sensors, which
are attached to a very thin and pigmented tissue part (of about the
same thickness corresponding to the diffusion constant in human
tissue).
[0131] The interdependence of the above-described transformation
and the path length multiplier is first illustrated by considering
the photon path lengths through a single layer of artery blood and
examining how the scattering and absorption affect it. It is
postulated here that multiple scattering effectively increases the
photon path length through the artery and that the absorption of
the surrounding tissue effectively decreases it. In this way the
artery and tissue are in interaction with each other. To derive a
mathematical formulation of this, relationship, the known
Kubelka-Munk two-flux model can be used. This model defines an
absorption probability K as follows: 16 K = l z * a , ( 15 )
[0132] where .SIGMA.a is the macroscopic absorption cross-section
of the media and dl is the true average photon path length through
the scattering and absorbing medium of infinitesimal layer
thickness dz. The term <dl/dz>=K/.SIGMA.a is a path length
multiplier (plm) which enhances the arterial blood absorption from
that of the Lambert-Beer non-scatter value because of the multiple
scattering in the surrounding medium.
[0133] The idea of the path length multiplier is applied to the
Lambert-Beer formulation of 2-.lambda. pulse oximetry. The ratio of
the change in absorption at the two probe wavelengths is defined
as: 17 A k A l = N kl in - vivo = a k * d k a l * d l , ( 16 )
[0134] where .mu.a.sup.i is the arterial (non-scatter) absorption
coefficient at wavelength i and di is the effective true optical
path length. The transformation is defined by substituting equation
15 with dl=d.sub.i in equation 16: 18 N kl in - vivo = a k * ( K a
) k * dz a l * ( K a ) l * dz = ( K a ) k ( K a ) l * N kl ideal ,
( 17 )
[0135] where the ideal Lambert-Beer model is used for
N.sub.kl.sup.ideal.ident..mu.a.sup.k/.mu.a.sup.l, and where the
layer thickness dz is the same for all wavelengths (k, l). Equation
17 now represents the transformation (gkl).sup.-1 from
N.sub.kl.sup.in-vivo, i.e. from the measured value, to
N.sub.kl.sup.L-B, which is the ratio of differential absorptions
that would be measured if the measurement system were the ideal
cuvette system of the Lambert-Beer model. For the transformation
the equation below is obtained: 19 g kl = ( K a ) k ( K a ) l = plm
k plm l . ( 18 )
[0136] Thus the transforming quantity is a ratio of path length
multipliers measured at two different wavelengths (k and l). The
dependence of the function g.sub.kl thus refers to the absorption
density of the scattering tissue in the surrounding of the
infinitesimal arterial layer dz including the layer itself. This
essentially means that the transformation does not require
knowledge of the analyte composition in the arterial blood, but
refers rather to the macroscopic light absorption, i.e.
transmission through the tissue part under the sensor. That is in
the language of pulse oximetry the DC component of the light
transmission. This is utilized in the compensation of the
invention.
[0137] Modifying Eq.1 and leaving the attenuation of the probe
design factors (i.e. CTR values) out of consideration, the
relationship of the DC light transmission through the tissue and
the path length multiplier can be presented as follows:
I.sub.out-I.sub.ine.sup.-.epsilon.DC=I.sub.ine.sup.-.epsilon.plm D1
C (19),
[0138] where D is the actual path length through the sample, D1 is
the shortest path length through the sample (i.e. the thickness of
the sample), and .epsilon. is the ideal extinction coefficient of
the analyte. Here the I.sub.out/I.sub.in can be associated with the
FLT at the wavelength in question. Plm thus describes the internal
attenuation factors in the tissue and, in particular, the
enhancement of the absorbancy relative to the ideal cuvette
absorption.
[0139] In nominal conditions, the path length multiplier has a
certain nominal value plm.sup.0 (where the superscript `0` refers
to the nominal value). This nominal value can be determined in the
factory at the manufacturing stage of the pulse oximeter. When the
DC component is measured again in connection with in-vivo
measurement, the change in the plm from the nominal value can be
used to determine the change in the average transformation.
[0140] The term .alpha..sub.int in Eg.4 can be expressed with the
help of the path length multiplier in the Lambert-Beer model as
.alpha..sub.int=plm*.SIGMA..sub..alpha.,
[0141] where .SIGMA.a accounts for all internal absorption sources
and is defined in the non-scatter Lambert-Beer domain. The FLT at
wavelength k can then be written as follows:
FLT.sub.k=exp(-.alpha..sub.int*d)=exp(-plm*.SIGMA..sub..alpha.*d)
(20).
[0142] We then ratio the logarithms of the FLTs at two wavelengths
k and l, which results in Eq. 21: 20 log ( FLT k ) log ( FLT l ) =
plm k * a k plm l * a l = g kl * f a * a k + f v * v k f a * a l +
f v * v l = g kl * f a ( a k - v k ) + v k f a ( a l - v l ) + v l
, ( 21 )
[0143] where g.sub.kl=plm.sub.k/plm.sub.i is the transformation
between the Lambert-Beer and in-vivo modulation ratios according to
Eq. 18, and in which the internal absorbing tissue compartments are
venous and arterial blood with volume fractions f.sub.v and f.sub.a
and with absorption coefficients .mu..sub.v and .mu..sub.a
determined in the Lambert-Beer domain, respectively. In the last
expression we have used for the venous volume fraction the
relationship f.sub.v=1-f.sub.a. As the arterial volume fraction is
always smaller than the venous volume fraction and as the
arterial-venous absorption difference is always smaller than the
venous absorption, the dominating factor in the last term is
.mu..sub.v.sup.k/.mu..sub.v.sup.l, i.e. the venous saturation
SvO2.
[0144] Thus the changes in the FLT and SvO2 from their nominal
values provide the compensation needed for estimating the correct
transformation function g.sub.kl. We can then finally write for the
relative change of the transformation function g.sub.kl: 21 g kl g
kl 0 = log ( FLT k ) log ( FLT l ) / log ( FLT k ) 0 log ( FLT l )
0 F kl ( SvO2 , SaO2 , f a ) / F kl ( SvO2 , SaO2 , f a ) 0 , ( 22
)
[0145] where the function F.sub.kl represents the ratio term 22 f a
( a k - v k ) + v k f a ( a l - v l ) + v l
[0146] in Eq. 21 and the superscript 0 represents the nominal
values of the nominal calibration function g.sub.kl.sup.0, which is
on average true for a large patient population. In fact, the
log(FLT) and the F.sub.kl compensation terms account for quite
different human variability factors in the tissue: whereas F.sub.kl
mainly tracks the changes of the arterial venous saturation
difference, in particular SvO2, the log(FLT) reflects the changes
in the total absorption of the tissue, i.e. in the total blood
volume and the total hemoglobin or hematocrit, which are not seen
in F.sub.kl at all. In practice, the largest corrections to the
transformation function are due to the log(FLT) and F.sub.kl is
less important.
[0147] The FLT in Eq. 22 is easily obtained at the two wavelengths
k and l, as has been described earlier in Eg.5. The function
F.sub.kl represents the ratio of the absorption coefficients (in
the Lambert-Beer non-scatter model) of the whole tissue at these
same two wavelengths, i.e. it represents the internal color of the
tissue. This internal absorption ratio can be measured by examining
the low frequency baseline fluctuations of the plethysmographic
wave signal.
[0148] FIG. 14 depicts frequency baseline fluctuations of the
plethysmographic wave signal. These fluctuations are caused by the
low frequency changes (usually of respiration origin) in the blood
volume or in the blood volume distribution of the tissue.
Similarly, since the arterial color (=R-ratio) is defined as the
ratio of the arterial absorption coefficients, function F.sub.kl
can be calculated as: 23 F kl = f a ( a k - v k ) + v k f a ( a l -
v l ) + v l = g kl - 1 ( AC / DC ) k ( AC / DC ) l = g kl - 1 ( N
kl baseline ) , ( 23 )
[0149] where AC is the amplitude (or the instantaneous slope) of
the low frequency baseline fluctuation, instead of the heart pulse
amplitude of the plethysmographic wave, and DC is the DC light
transmission at that particular wavelength. Because the effective
tissue color is mainly determined by the venous blood, function
F.sub.kl can be approximated as the arterial modulation ratio
calculated for the venous saturation, which is usually about
SaO2-10% i.e. F.sub.kl=R(SvO2=SaO2-10%).
[0150] If the venous saturation is determined by venous blood
samples and the arterial saturation by the arterial blood samples,
the function F.sub.kl can be calculated using the real blood values
(with the assumption that the corresponding blood compartment
volumes are f.sub.a=0.25 and f.sub.v=0.75).
PREFERRED EMBODIMENT OF THE INVENTION
[0151] FIG. 7 is a flow diagram illustrating the general principle
of the present invention. The method can be divided into two groups
of phases. The first group 71-73 comprises measures relating to the
setting up that is carried out prior to actual use of a pulse
oximeter for measuring analyte concentrations of a patient whereas
the second group 74-75 comprises method phases performed in the
actual use. Previously in the present application the phases of the
first group were also called off-line phases.
[0152] In the setting up phase, initial characterization
measurements are first made, preferably at the calibration stage of
the pulse oximeter with a nominal wavelength pulse oximeter sensor
(phase 71). Based on the measurements, nominal characteristics are
established describing the conditions under which the pulse
oximeter has been calibrated (phases 72). As a result of these
phases, reference data are stored (phase 73), which describe the
calibration conditions of the pulse oximeter. In connection with
subsequent in-vivo measurements, the same characteristics are again
estimated and tissue-induced changes in the characteristics are
determined based on the measured characteristics and the reference
data stored (phase 74). In addition to the tissue induced changes,
the changes relating to the different sensor than in the
calibration stage are incorporated with the tissue changes. In
in-vivo measurement, the N.sub.kl.sup.in-vivo values are determined
from the dA.sub.i values measured. On the basis of the changes
determined, the subject-specific calibration is then determined
(phase 75) for the in-vivo measurements to be performed by the
pulse oximeter on the subject.
[0153] It is to be noted here that phases 71-73 are performed
either when the pulse oximeter has been calibrated in a known
manner or at the manufacturing stage of the pulse oximeter sensor
when the sensor characteristics are determined. After these phases,
the nominal transformation and the nominal extinction coefficients
are known to the pulse oximeter.
[0154] Next, referring to FIG. 8-11 contents of phases 71 and 72
will be explained in more detail.
[0155] FIG. 8 depicts the first steps carried out in the initial
characterization phase of the setting up. It should be noted that
in this phase no living tissue is needed. These steps are performed
prior to the actual measurements, for example in the factory at the
manufacturing stage of the pulse oximeter sensor. Thus, referring
to FIG. 8, the steps are as follows.
[0156] Step 81.
[0157] The spectral characteristics of the emitter/detector system
are measured. In other words, the LEDs are characterized for their
light emission (the emission as a function of wavelength) and the
detector for its spectral sensitivity. This step thus includes
determination of the characteristics of the curve shown in FIG. 13,
i.e. the received intensity as a function of wavelength (at least
around the wavelengths used). The light transmission from the light
emitter to the light detector is measured without living tissue,
i.e. the CTR is determined in the sensor in a fixed setup mimicking
the actual use of the sensor. For clip-type sensors this is usually
the Probe Off position of the sensor. The step also includes
determination of the center wavelength of each LED.
[0158] Step 82
[0159] Using the spectral characteristics obtained at the previous
step, the effective extinction coefficients for the nominal
extinction matrix are determined without the tissue term. Thus in
this step Eg.6 is used without the tissue term (tissue(.lambda.))
to form the nominal extinction matrix E.sub.kl.sup.0 according to
Eg.7.
[0160] Step 83
[0161] The tissue correction to the nominal extinction matrix is
estimated for artificial tissue, in which transmission slopes are
1. In other words, the relative changes in the effective extinction
coefficients due to artificial tissue filter effect are determined.
Here Eg.6 is used assuming that the slope of tissue(s) equals a
predetermined fixed value at each wavelength. In other words, the
shift matrix of Eq. 9 is determined.
[0162] Step 84
[0163] Temperature corrections to the nominal extinction matrix are
determined, i.e. the relative changes in the effective extinction
coefficients due to wavelength shift caused by changes in
temperature are determined. In other words, the matrix of Eq. 12 is
determined, which indicates the relative changes for a wavelength
shift of a predetermined value. Said value could be 5 nm, for
example.
[0164] Step 85
[0165] If the LED forward voltage method is used, the LED forward
voltages are characterized at a typical drive current for small
ambient temperature changes. In other words, the relationship
between the forward voltage shift and the temperature shift is
established for each emitter. Thus, the temperature coefficients
k.sub.l in Eq. 11 and the nominal forward voltage drops at nominal
temperature are determined for each LED.
[0166] Step 86
[0167] All data obtained in the previous steps are saved in a
memory unit in the sensor or in the control unit, or the
corresponding information is otherwise made available to the pulse
oximeter, for example, by using codes, such as sensor
identification numbers, which indicate the values of the
information.
[0168] As is obvious from the above, steps 81 to 86 include
performing initial characterization measurements for the
compensation, said measurements to include measuring the light
transmission of the apparatus, establishing nominal DC transmission
characteristics of the apparatus on the basis of the measurements,
and for subsequent in-vivo measurements storing reference data that
indicate the transmission characteristics established.
[0169] Thus, in these first initial characterization measurements,
the value of Eg.6 is determined using nominal values, and the
nominal extinction matrix is formed. The apparatus is also provided
with the data needed in the subsequent in-vivo compensation steps
for calculating the changes in the factors included in Eg.6. In the
in-vivo steps the said changes are determined and a new extinction
matrix is formed, whereby the new extinction values are such that
the external effects are compensated for.
[0170] To sum up, after the setting up steps the pulse oximeter
stores the matrices according to equations 7, 9, and 12 and the
values of the shift coefficients k.sub.i. In addition to this, the
oximeter stores the CTR values and the center wavelengths
corresponding to these values.
[0171] FIG. 9 depicts setting up stages that are carried out while
establishing calibration and nominal characteristics, which
describe calibration conditions (phase 72 in FIG. 7). These setting
up stages consist of three measurement and calculation stages that
are carried out simultaneously. Stage 91 includes characterization
of calibration conditions when external factors influence the
nominal extinction matrix that was formed previously. Stage 92
includes characterization of calibration conditions when internal
factors influence transformation curves. Stage 93 includes
establishment of nominal calibration either for multiwavelength
pulse oximeter of two-wave pulse oximeter. In performing the steps
the information stored during initial characterisation measurements
(phase 71 in FIG. 7) is utilized. In addition, it is worth noting
that when doing the measurements in said three steps a sensor is
attached to the pulse oximeter and there is living tissue between
the emitters and the detector. As to living tissues, a large group
of people is used in order to obtain enough statistical data. At
least the nominal calibration and the nominal internal factors are
stored. Optionally external factors are stored.
[0172] FIG. 10 is a block diagram depicting different steps of
stage 91 (FIG. 9). The content of the steps are as follows:
[0173] Step 101
[0174] The forward voltage drops (.DELTA.V) are measured for each
LED and for each subject in a group of people. The voltage drops
are compared with the nominal drops obtained in step 85 of FIG. 8
or the temperature is compared with the nominal temperature. The
change in the forward voltage relative to the sensor nominal values
are calculated, the nominal values having been stored in the sensor
memory unit or in the control unit memory. If the pulse oximeter
does not have forward voltage measuring means, the temperature of
each LED is estimated by reading the temperature indicated by a
sensor on the LED substrate, and either the manufacturer
specifications or empirical data for corresponding wavelength
changes or a look-up table mapping the emitter drive current to the
center wavelength shift is used.
[0175] Step 102
[0176] Then the change in the extinction matrix for the temperature
compensation is calculated. This is done for each person of the
group. Calculation is done according to Eq. 11-13, i.e. using the
wavelength shifts determined according to Eq. 11 and calculating
the matrix of Eq. 13 using the matrices of Eg.7 and 12 stored in
step 84 of FIG. 8.
[0177] Step 103
[0178] For each person of the group the tissue transmission induced
changes to the nominal extinction matrix are calculated. Thus, the
DC light transmission for each LED channel (wavelength) is
measured, and the value measured is normalized to an emitter
current value of 1 mA. The result is the FCTR of the sensor. An
estimate for the FLT is then calculated for each emitter
(wavelength). In this connection, the FLT values are used for
calculating the slopes (A and B). In other words, all DC values are
normalized in relation to the 1 mA emitter current in order to make
all values comparable to one another. Equations 8-10 are used for
calculating the change of the nominal extinction matrix.
[0179] Step 104
[0180] This step is an alternative to the next step. Namely it
might be practical simply to average the results obtained in the
previous steps for each person of the group and then just combine
the average values (temperature and tissue effects) into one
extinction matrix.
[0181] Step 105
[0182] If step 104 is omitted, then the effective Lambet-Beer
extinction matrix for each person of the group is calculated. The
effective Lambert-Beer extinction coefficients are determined using
Eq. 14. Thus, each of those matrices characterizes the calibrations
conditions for one particular person of the group.
[0183] Step 106
[0184] Averaging of the individual Lambet-Beer matrices element by
element results in a group average extinction matrix that
characterize the extinction coefficients during the nominal
calibration process.
[0185] Optionally all the data is stored. However, it may be more
practical that in the subsequent in-vivo pulse oximeter
measurements the steps 101-106 are continuously repeated to update
extinction matrix coefficients using only the information stored in
the setting-up phase of FIG. 8. When the data describing the
external factors--mainly the characteristics relating to the
sensor--is stored, it can be used for checking up the sensor
condition and alert the user if considerable, abnormal deviation
from the average characteristics occur during in-vivo measurements.
The stored data can thus be used to issue a Probe Fault Condition
alarm.
[0186] Next, referring to FIG. 11 the content of the stage 92 (FIG.
9) is explained. As stated above, that stage is performed
simultaneously with stages 91 and 93.
[0187] Step 111
[0188] Characterization of the calibration conditions with internal
factors influencing transformation curves starts by first fetching
the FLTs calculated in step 103 of FIG. 10 and then by calculating
the ratio of the logarithms for each wavelength pair and for each
person of the group. In other words, the nominal light transmission
through a finger or ear lobe or its approximation at the distinct
nominal wavelengths of the sensor, is determined for each person of
a group (the group may be a population of patients or volunteers or
even for only one single volunteer), on whom the nominal
calibration was performed. This gives for each person of the group
an individual curve 24 log ( FLT k ) 0 log ( FLT l ) 0
[0189] as a function of N.sub.kl.sup.in-vivo at these wavelengths
(k, l), i.e. essentially as a function of the correct SpO2.
[0190] Step 112
[0191] A regression curve is calculated from the individual curves
obtained in step 111. The logarithm ratios fitted to the regression
curve and the corresponding N.sub.KL values are stored in the table
1, 5.sup.th column. The table is presented below. This information
is stored in the sensor memory unit (or in the control unit).
[0192] Step 113
[0193] F-factors, i.e. function F.sup.0 can be determined in two
alternative ways:
[0194] a) In the above measurement using the baseline fluctuations,
N.sub.kl.sup.baseline is calculated and transformed to the
Lambert-Beer model by the nominal transformation
(g.sup.0.sub.kl).sup.-1 for the group of persons (patients or
volunteers or even for only one single volunteer, on whom the
nominal calibration was performed). This is tabulated in table 1
below as a function of N.sub.kl.sup.in-vivo at these wavelengths
(k, l). These data are stored in the sensor memory unit (or in the
control unit).
[0195] b) In the above measurement venous and arterial blood
samples are taken from a position close to the sensor site and
analyzed for RHb, HbO.sub.2, HbCO, and metHb. The absorption
coefficients .mu..sub.a and .mu..sub.v are then calculated using
the measured analyte fractions. The arterial volume fraction
f.sub.a is then estimated. Usually it is sufficient to approximate
that f.sub.a is equal to 0.25. The functions F.sub.kl.sup.0 are
calculated in the Lambert-Beer Model using the venous and arterial
volume fractions and 25 a , v = RHb * a , v RHb + HbO2 * a , v HbO2
* HbCO * a , v HbCO + metHb * a , v metHb .
[0196] As a result of steps 111-113, we have set up a look-up table
for each wavelength pair, in which the following nominal
information is tabulated:
1TABLE 1 k l N.sub.kl.sup.in-vivo g.sub.kl.sup.0
Log(FLT.sub.k).sup.0/log(FLT.sub.l).sup.0 g.sub.kl.sup.-1 .times.
(N.sub.kl.sup.baseline) F.sup.0kl
[0197] and where also the nominal transformation g.sup.0 is
presented and
N.sub.kl.sup.baseline.congruent.g.sub.kl.sup.0.times.(F.sub.kl).
Only one of the two last columns is necessary, depending on the way
the values of function F are determined. It is also to be noted
that the ratio is not necessary in column 5, but that it is enough
to store the FLT values from which the ratio of their logarithms
can be calculated.
[0198] As is obvious from the above, steps 111-113 again include
measuring the DC light transmission of the apparatus, establishing
nominal DC transmission characteristics for the apparatus on the
basis of the measurements, and storing reference data for
subsequent in-vivo measurements, the data indicating the
transmission characteristics established.
[0199] After the above setting up steps, which can be performed at
the manufacturing stage of the pulse oximeter, the pulse oximeter
is ready for use in a hospital environment or like in connection
with in-vivo measurements.
[0200] Now, next we consider a case where the pulse oximeter is
used to detect blood oxygenation of a patient in a hospital or
like, i.e. in-vivo measurement is to be performed. The patient puts
his finger of ear lobe between the emitters and the detector
whereupon light transmission through the finger or ear lobe is
measured. The sensor of the pulse oximeter produces data that is
processed as shown in FIG. 12. It is worth noting that during the
measurement pulsative signals are collected through the whole
measuring period. Accordingly, the full round of steps that will be
described below are performed cyclically many times, one round per
each pulsation of the heart, for example.
[0201] FIG. 12 shows four steps of the data processing in the
in-vivo measurement. Firstly, changes that patient tissue causes to
the nominal extinction matrix are calculated, step 121. Secondly,
changes that patient tissue causes to the nominal transformation
curves are calculated, step 122. Thirdly, the nominal values, which
were calculated in the setting up phase, are compared with the
individual patient values calculated. Equation 22 is used to get
the individual transformation function for the patient (step 123).
Finally, the individual transformation functions obtained from step
123 and the individual extinction matrix obtained from step 121 are
applied to Lambert-Beer model that gives analytic concentrations of
the patient.
[0202] The steps are now explained in more detail.
[0203] Step 121 is actually repetition of steps 101-102 of FIG. 10.
Thus, the forward voltage drops (.DELTA.V) are measured for each
LED. The voltage drops are compared with the nominal drops stored
in the memory. The change in the forward voltages relative to the
sensor nominal values is calculated. Then the change in the nominal
extinction matrix for the temperature compensation is calculated.
Calculation is done according to Eq. 11-13, i.e. using the
wavelength shifts determined according to Eq. 11 and calculating
the matrix of Eq. 13 using the matrices of Eg.7 and 12. Calculation
in step 121 therefore results in patient specific extinction
matrix.
[0204] Accordingly, step 122 is actually repetition of step 111 of
FIG. 11. The step starts by first fetching the FLTs stored in the
memory during the setting up process of the oximeter and then by
calculating the ratio of the logarithms for each wavelength pair
and for the patient. In other words, the nominal light transmission
through the finger or ear lobe is determined for the patient. This
gives for the patient a curve 26 log ( FLT k ) log ( FLT l )
[0205] as a function of N.sub.kl.sup.in-vivo at these wavelengths
(k, l). Then in Table 1 in the row where the measured modulation
ratio at the wavelengths k and l equals N.sub.kl.sup.in-vivo, the
nominal 27 log ( FLT k ) 0 log ( FLT l ) 0
[0206] is read. The correction factor 28 log ( FLT k ) log ( FLT l
) / log ( FLT k ) 0 log ( FLT l ) 0
[0207] is then calculated.
[0208] Then in step 123, comparison between the nominal values
stored in Table 1 and the individual patient specific values
obtained from step 112 is done and equation (22) is used to get the
patient specific transformation functions. This is carried out so
that in Table 1 in the row, where the measured modulation ratio at
the wavelengths k and l equals N.sub.kl.sup.in-vivo, the nominal
F.sub.kl.sup.0 is read from either of the last two columns. Then
the correction factor F.sub.kl/Fk.sub.kl.sup.0 is determined in one
of the following two ways:
[0209] a) Column g.sup.-1.times.(N.sup.baseline): By using the
baseline fluctuations of the measured plethysmographic wave and by
using Eq. 23 with g.sup.-1, the function F.sub.kl is determined.
The correction factor F.sub.kl/F.sub.kl.sup.0 is then calculated.
For determining N.sup.baseline and its changes, the amplitudes of
the signal can be used, as is normally done for a modulation ratio
N.
[0210] b) Column F.sub.kl: The blood analytes RHb, HbO.sub.2, HbCO,
and metHb are solved in the Lambert-Beer model using the nominal
transformation g.sup.0. This is the first approximation for the
analytes. The absorption coefficients .mu..sub.a and .mu..sub.v are
calculated using the measured analyte fractions in the arterial
absorption and approximating the absorption coefficient in the
venous blood by using the measured dyshemoglobin fractions and
setting HbO2.sup.vena=HbO2-10% and RHb.sup.vena=RHb+10%, where
f.sub.a=0.25 is assumed. The functions F.sub.kl are calculated in
the Lambert-Beer Model using the equation: 29 a , v = RHb * a , v
RHb + HbO2 * a , v HbO2 * HbCO * a , v HbCO + metHb * a , v metHb
.
[0211] A new transformation g.sub.kl is calculated using Eq. 22,
and the new transformation is used for solving the analyte
concentrations in the Lambert-Beer model. Optionally, a more
accurate estimate of F.sub.kl can be obtained by iteration of new
analyte fractions for the new corrected transformation.
[0212] Finally, in step 124 the patient specific transformation
functions and the patient specific extinction matrix are used to
solve the analyte concentrations by applying them to the
Lambert-Beer model.
[0213] During the above steps 121-124 in-vivo measurements are
performed, wherein the DC component of the radiation emitted
through the tissue and received by the detector is measured,
tissue-induced changes in the transmission characteristics are
determined based on the in-vivo DC component and the transmission
characteristics stored, and on the basis of the tissue-induced
changes the subject-specific variation in the in-vivo measurement
is compensated for.
[0214] The above setting up and in-vivo steps compensate for the
non-ideal characteristics of the broadband emitters or for external
effects on the light source emission spectra. They do also
compensate for the variation in the absorption and scattering
interplay in the tissue, i.e. the internal effects, which equally
influence a single line laser emitter and a broadband LED emitter.
Lasers also show shifts in the emission line wavelength as a
function of the temperature. Therefore, the lasers are compensated
for the temperature and internal tissue effects, but not for the
pre-filter tissue-induced spectral shifts.
[0215] The pre-calculated data utilized by the pulse oximeter can
be stored in the sensor part of the pulse oximeter, whereby the
same sensor can be attached to different pulse oximeter
housings.
[0216] FIG. 15 illustrates the general structure of a sensor
according to the invention, the detailed configuration of the
sensor being dependent on which information is stored in the sensor
and which in the signal processing part, and also on the amount of
the calculation appropriate in the signal processing part.
[0217] Nevertheless, a sensor according to the invention includes
the light sources (10a-10c) and the photo detector, the light
sources being adapted to emit at two or more wavelengths. In
addition, the sensor includes a data storage unit M2 for storing
the data on the basis of which the signal processing part can
perform the above-described calibration. The information necessary
for the above compensations is shown in the figure. For the first
compensation process the pulse oximeter needs the k-values, the
above-mentioned three matrices, i.e. the nominal extinction matrix
(Eg.7), the shift matrices (Eqs. 9 and 12), and the CTR/wavelength
pairs. For the second compensation process, in turn, the pulse
oximeter needs the information stored in Table 1 and the
CTR/wavelength pairs. As mentioned above, at least part of this
data determined prior to the use of the device for in-vivo
measurements can also be stored in the control unit part of the
pulse oximeter. The apparatus further preferably includes means 150
for measuring the forward voltage of the p-n junction of each LED,
as discussed above.
[0218] FIG. 16 illustrates the general structure of a main frame.
The main frame refers here to hardware loaded with software, which
are enclosed in the housing ox a pulse oximeter. The main frame
comprises thee units: control and measurement unit 161,
compensation unit 162 and hemoglobin fraction calculation unit
163.
[0219] Control and measurement unit 161 includes light source
controller 1611 that forms and controls supply current to the
emitters of the light source. The emitters locate in the separate
sensor (see FIG. 15). Detector signal receiver 1612 receives the
signal from the photo detector, filters and amplifies said signal.
The amplified analog signal is converted to digital domain in A/D
converter 1614 whereupon AC and DC components are separated from
the digital signal. Further, the control and measurement unit 161
also includes memory read/write means 1616 for storing data in and
reading data from memory 1617 in the main frame and memory in the
sensor. Control and measurement unit 161 per se is known in the art
except the module 1613, in which the sensor CTR and the functional
light transmission are measured.
[0220] The inventive part of the main frame resides mainly in
compensation unit 162 that includes software to perform the method
steps of the invention. The unit 162 comprises of extinction
compensation block 1621, path length multiplier compensation block
1622, and block 1623 for calculating subject specific
compensation.
[0221] Extinction compensation block 1621 includes software adapted
to create a nominal extinction and to correct said matrix by
compensating it for tissue pre-filter effects and temperature
effect. In other words, tissue pre-filter compensator 1601
calculates an amount of tissue pre-filtering compensation that is
needed and temperature compensator 1602 calculates an amount of the
needed temperature compensation. Thus, the blocks 1601 and 1602 are
adapted to perform the steps described under subtitles (a) and
(b).
[0222] Path length multiplier compensation block 1622 is adapted to
control the value of the transformation used to transform the
modulation ratios N.sub.kl.sup.in-vivo to the Lambert-Beer model
N.sub.kl.sup.L-B. Thus, the block performs operations described
previously under subtitle (c). Log (FLT) compensator 1603 fetches
the FLTs, that have been calculated in block 1612, and then
calculates the ratio of the logarithms for each wavelength pair and
for each person of a group. Then this block calculates the relative
change of the nominal transformation function. F-compensator 1605
14 calculates function F.sub.kl that can be approximated as the
arterial modulation ratio calculated for the venous saturation.
[0223] The last block of the compensation unit 1621 is a block that
calculates subject-specific compensation based on the results of a
in-vivo measurement and the data stored in memory 1617.
[0224] The results obtained from block 1623 are fed to hemoglobin
fraction calculation unit 163 that is per se known from the prior
art.
[0225] FIG. 17 depicts some units of the sensor. The sensor
comprises memory 171 for storing data, particularly the reference
data indicating calibration conditions, i.e. data produced by the
compensation unit 162. Optionally reference data may also be stored
in memory 1617 residing in the main frame of a pulse oximeter. For
calculating a correction to the nominal extinction matrix, which is
required pursuant to a wavelength shift caused by a temperature
change in the emitter chip, the sensor is provided with junction
sensitive element 172. The element monitors the temperatures of the
pn-junctions of the LEDs and produces signals to be fed to the main
frame. Further, the sensor includes light emitters 173 and 174.
[0226] As can be seen from the above, the method of the invention
is based on the DC transmission of light. By means of the DC
measurements in the setting up phase, reference data is first
created. During subsequent in-vivo DC measurements, the reference
data is then utilized to filter out human variability from the
in-vivo measurement.
[0227] Although the method in accordance with the invention has
been discussed in connection with a four wavelength pulse oximeter,
it can also be employed in a basic two wavelength pulse oximeter.
However, the method is more beneficial in a multi-wavelength pulse
oximeter where the number of analytes to be measured is greater
than two.
[0228] In the case of a two-wavelength pulse oximeter, the simplest
way to apply the compensation is first to formulate the calibration
of the two-wavelength oximeter as a first step using only one
transformation function g.sup.-1 (e.g. at wavelengths 660 nm and
900 nm) and a second step using a two-times-two extinction matrix
.epsilon. for these wavelengths and for the two analytes RHb and
HbO2. The compensation procedures are then identical to the ones
presented in the above multi-wavelength method. If the calibration
of the two-wavelength pulse oximeter is done in the normal way
using a direct mapping of the in-vivo measured R-ratio
(=N.sub.660-900) to the SpO2 percentage, the compensation steps
could for example, be as follows: The wavelength shifts from the
nominal LED center wavelength values to a change in the SpO2 value
can first be coded. The wavelength shifts are determined for the
temperature component as described in the above multi-wavelength
method and for the tissue component by mapping at the two
wavelengths the change in the FLT ratio from its nominal value in
the calibration conditions to a change in the SpO2 value from the
nominal calibration SpO2. The tissue wavelength shift cannot be
estimated as accurately as in the multi-wavelength oximeter, but
sufficient compensation to the tissue prefilter can still be
obtained and the accuracy of the pulse oximeter can be improved.
The last compensation step also includes the compensation for the
internal tissue variability, which is summed with the prefilter
effect.
[0229] A distinguishing feature of the invention is that
compensation is made several times during the in-vivo measurement.
In other words, the pulse oximeter measurement is compensated
heartbeat-by-heartbeat, i.e. the oxygenation measurement is
accurate for each patient and for each time moment in the
individual patient. This is not known from the prior art. Further,
the same apparatus is used both in the calibration phase, the
initial characterization with tissue and the in-vivo
characterization with tissue. The method compensates continuously
and dynamically the tissue induced changes and keeps the PO
measurement accurate at all times. The apparatus and method is also
used in the manner that the same apparatus is suitable both the
in-vivo characterization and the tissue characterization during the
calibration. The prior art technology uses different techniques in
the initial characterization and in-vivo characterization.
[0230] Although the invention has been described above with
reference to the examples shown in the appended drawings, it is
obvious that the invention is not limited to these, but may be
modified by those skilled in the art without departing from the
scope and spirit of the invention. For example, instead of
transformation, any other quantity by which the pulse oximeter can
correct the average calibration known to it can be used to
eliminate human variability.
[0231] The invention has also been described with reference to
pulse oximeters for analytes which are in the blood of a subject.
The invention, however, can also be applied at different wavelength
ranges, e.g. at around 1.5 .mu.m for glucose, at which similar
compensation means are called for. The other substances in the
tissue modify the effective extinction of the glucose because they
alter the path length multiplier at this wavelength. Similarly, the
tissue prefilter and temperature effects are taken into
account.
* * * * *