JP2023043576A - Biological information measurement device and biological information measurement program - Google Patents

Abstract

To easily determine that a waveform pattern is not an inappropriate waveform pattern, compared with when determining an appropriate waveform pattern using an existing algorithm.SOLUTION: A biological information measurement device 10 includes a CPU 20A. The CPU 20A acquires a first signal and a second signal detected from a living body, multiplies the value of the first signal or the value of the second signal by a coefficient to compensate one of the value of the first signal and the value of the second signal, so that the difference between the amount of change in the first signal and the amount of change in the second signal become smaller with changes in the arterial blood volume of the living body, calculates a waveform pattern representing the change in blood oxygen concentration in the living body, represented as the difference between the value of the first signal and the value of the second signal, one of which is compensated by a coefficient, and determines that the waveform pattern is appropriate if the value representing the degree of correlation between the first signal and the second signal is less than a threshold value.SELECTED DRAWING: Figure 12

Description

The present invention relates to a biological information measuring device and a biological information measuring program.

For example, Patent Literature 1 describes a method for measuring circulation time of oxygen delivery in which a change in oxygen saturation is calculated based on an absorbance signal of arterial blood extracted from a living body using a sensor. In this oxygen delivery circulation time measurement method, the intake oxygen amount to the living body is changed, and the time point of the change is used as a reference point, and the time from the reference point until the oxygen saturation of arterial blood changes is measured.

Further, Patent Literature 2 describes a biological information measuring device capable of measuring changes in blood oxygen concentration. This biological information measuring apparatus generates a first signal representing a change in the amount of light of a first wavelength detected from a living body, and a second signal representing a change in the amount of light of a second wavelength detected from the living body. and at least one of the first signal and the second signal so as to reduce the difference between the amount of change in the first signal and the amount of change in the second signal due to changes in the arterial blood volume of the living body. A correction unit for correction, and a calculation unit for calculating a change in blood oxygen concentration in a living body based on the first signal and the second signal, at least one of which is corrected by the correction unit.

Japanese Patent Application Laid-Open No. 2006-231012 JP 2019-166144 A

By the way, the simplest method for changing the blood oxygen concentration of a subject is to have the subject hold his or her breath. However, because of the reluctance to hold the breath, there are cases where the breath is held while the lungs contain a lot of air. In this case, the waveform pattern representing the change in the blood oxygen concentration becomes an inappropriate pattern in which the inflection point of the waveform pattern caused by the change in the blood oxygen concentration cannot be specified.

With the existing technology, a complicated algorithm is required to determine whether the waveform pattern is appropriate, and the load on the determination process is heavy. Therefore, a simple determination method is desired.

The present disclosure provides a biological information measurement device and a biological information measurement program that can easily determine that the waveform pattern is not inappropriate compared to determining an appropriate waveform pattern using an existing algorithm. intended to

In order to achieve the above object, a biological information measuring apparatus according to a first aspect includes a processor, wherein the processor generates a first signal representing a change in the amount of light of a first wavelength detected from a living body; obtaining a second signal representing a change in the amount of light of a second wavelength detected from a living body, and determining the amount of arterial blood in the living body with respect to the value of the first signal or the value of the second signal; The value of the first signal and the value of the second signal are multiplied by a coefficient so that the difference between the amount of change in the first signal and the amount of change in the second signal due to the change in of the blood oxygen concentration in the living body, expressed as the difference between the first signal value and the second signal value, one of which is corrected by the coefficient A waveform pattern representing a change is calculated, and the waveform pattern is determined to be appropriate when a value representing the degree of correlation between the first signal and the second signal is less than a threshold.

Further, the biological information measuring device according to the second aspect is the biological information measuring device according to the first aspect, wherein from the waveform pattern determined as appropriate by the processor, the blood is The point of inflection of the middle oxygen concentration is detected, and the time from the time when the intake oxygen amount of the living body changes to the detected point of inflection of the blood oxygen concentration is specified.

Further, the biological information measuring device according to the third aspect is the biological information measuring device according to the first aspect or the second aspect, wherein when the value representing the degree of correlation is equal to or greater than the threshold value, the waveform pattern is determined to be inappropriate, and a warning is issued to prompt remeasurement.

Further, a biological information measuring device according to a fourth aspect is the biological information measuring device according to the third aspect, wherein the warning includes a message prompting the user to exhale sufficiently, hold his/her breath, and perform the measurement again.

Further, a biological information measuring device according to a fifth aspect is the biological information measuring device according to any one of the first to fourth aspects, wherein the value representing the degree of correlation changes the inspiratory oxygen amount of the living body. It is calculated from the first signal and the second signal at a predetermined time after being set.

Further, in the biological information measuring device according to the sixth aspect, in the biological information measuring device according to the fifth aspect, the predetermined time is a period of two or more beats of the living body.

Further, a biological information measuring device according to a seventh aspect is the biological information measuring device according to any one of the first to sixth aspects, wherein the coefficient is the It is represented by the amplitude ratio of the amplitude of the first signal and the amplitude of the second signal, and the correction is the value of the first signal after changing the inspiratory oxygen amount of the living body or This is done by multiplying the value of the second signal.

Further, a biological information measuring device according to an eighth aspect is the biological information measuring device according to the first aspect, wherein the processor obtains the coefficient from the value of the first signal and the value of the second signal. Calculated from the slope of the regression line.

Further, the biological information measuring device according to the ninth aspect is the biological information measuring device according to the eighth aspect, wherein the regression line is the value of the first signal and the value of the second signal in a period of two or more beats of the living body. Obtained from the value of the signal.

Further, a biological information measuring device according to a tenth aspect is the biological information measuring device according to the eighth aspect or the ninth aspect, wherein the processor determines that the value representing the degree of correlation between the first signal and the second signal is If it is equal to or greater than a predetermined value, the coefficient is calculated.

Further, a biological information measuring device according to an eleventh aspect is the biological information measuring device according to any one of the eighth to tenth aspects, wherein the processor divides the regression line into systole and diastole. , the coefficient is calculated when the difference between the slopes of the regression lines is within a predetermined range.

Further, the biological information measuring device according to the twelfth aspect is the biological information measuring device according to any one of the eighth aspect to the eleventh aspect, wherein the processor is an index indicating the tension state of the living body LF/ If the HF ratio is less than or equal to the threshold, the factor is calculated.

Furthermore, in order to achieve the above object, a biological information measurement program according to a thirteenth aspect includes a first signal representing a change in the amount of light of a first wavelength detected from a living body, and a first signal detected from the living body. and a second signal representing changes in the amount of light of light with two wavelengths, and with respect to the value of the first signal or the value of the second signal, the Either the value of the first signal or the value of the second signal is multiplied by a coefficient such that the difference between the amount of change in the first signal and the amount of change in the second signal is reduced. is corrected, and a waveform pattern representing a change in the blood oxygen concentration in the living body, represented as a difference between the value of the first signal and the value of the second signal, one of which is corrected by the coefficient If the calculated value representing the degree of correlation between the first signal and the second signal is less than a threshold value, the computer determines that the waveform pattern is appropriate.

According to the first aspect and the thirteenth aspect, there is an effect that it is possible to easily determine that the waveform pattern is not an inappropriate waveform pattern, compared to the case of determining an appropriate waveform pattern using an existing algorithm. .

According to the second aspect, there is an effect that it is possible to specify the time to the inflection point of the blood oxygen concentration with high accuracy as compared with the case where the difference between the corrected signals is not used.

According to the third aspect, there is an effect that remeasurement can be prompted when the waveform pattern is inappropriate.

According to the fourth aspect, there is an effect that it is possible to encourage the person to hold his/her breath after fully exhaling.

According to the fifth aspect, there is an effect that the value representing the degree of correlation can be calculated with higher accuracy than in the case where the predetermined time after changing the intake oxygen amount is not considered.

According to the sixth aspect, there is an effect that the value representing the degree of correlation can be calculated with higher accuracy than in the case of one beat after changing the inspired oxygen amount.

According to the seventh aspect, there is an effect that a highly accurate coefficient can be obtained as compared with the case where the coefficient is obtained after changing the intake oxygen amount of the living body.

According to the eighth aspect, there is an effect that the coefficient can be obtained easily compared to the case where the slope of the regression line is not considered.

According to the ninth aspect, there is an effect that it is possible to obtain a highly accurate regression line as compared with the case where the period is one beat of the living body.

According to the tenth aspect, there is an effect that a stable coefficient can be obtained as compared with the case where the value representing the degree of correlation is less than the predetermined value.

According to the eleventh aspect, there is an effect that a stable coefficient can be obtained as compared with the case where the difference in slope of the regression line is outside the predetermined range.

According to the twelfth aspect, there is an effect that a stable coefficient can be obtained as compared with the case where the LF/HF ratio is larger than the threshold.

It is a schematic diagram which shows the blood flow information which concerns on embodiment, and the measurement example of the oxygen saturation in blood. 7 is a graph showing an example of changes in the amount of received light due to reflected light from a living body according to the embodiment; FIG. 4 is a schematic diagram for explaining a Doppler shift that occurs when laser light is applied to a blood vessel according to the embodiment; FIG. 4 is a schematic diagram for explaining speckles that occur when a blood vessel is irradiated with laser light according to the embodiment; 4 is a graph showing an example of spectrum distribution for each frequency in unit time according to the embodiment; It is a graph which shows an example of the change of the blood flow volume per unit time which concerns on embodiment. It is a graph which shows an example of the change of the light absorption amount of the light absorbed by the living body which concerns on embodiment. 4 is a graph showing an example of absorbance characteristics of hemoglobin according to the embodiment; FIG. 4 is a schematic diagram for explaining the principle of measuring a respiratory waveform according to the embodiment; FIG. 2 is a schematic diagram for explaining the principle of measuring cardiac output according to the embodiment; 4 is a graph for explaining an example of an LFCT measurement method according to the embodiment; 1 is a block diagram showing an example of an electrical configuration of a biological information measuring device according to an embodiment; FIG. It is a figure which shows an example of arrangement|positioning of the light emitting element and the light receiving element in the biological information measuring apparatus which concerns on embodiment. It is a figure which shows another example of arrangement|positioning of the light emitting element and the light receiving element in the biological information measuring apparatus which concerns on embodiment. 5 is a graph showing an example of data sampling timing in the light receiving element according to the embodiment. It is a block diagram showing an example of functional composition of a biological information measuring device concerning a 1st embodiment. (A) is a scatter diagram showing an example of the correlation between the IR light output voltage and the red light output voltage when there is a change in blood oxygen concentration. (B) is a scatter diagram showing an example of the correlation between the IR light output voltage and the red light output voltage when there is no change in blood oxygen concentration. It is a flow chart which shows an example of the flow of processing of a biological information measuring program concerning a 1st embodiment. 5 is a graph showing exemplary amplitudes of an IR light signal and a red light signal according to an embodiment; 4 is a graph showing an example of a relationship between a coefficient and a pulse wave difference according to the embodiment; 5 is a graph showing an example of time-series data of an IR light signal and time-series data of a red light signal according to the embodiment; 7 is a graph showing an example of corrected time-series data of an IR light signal and time-series data of a red light signal according to the embodiment; 7 is a graph showing an example of a monitoring result based on a pulse wave difference according to the embodiment; 4 is a graph showing an example of LFCT identified from the pulse wave difference according to the embodiment; (A) is a graph showing time-series data of an IR light signal and a red light signal according to a first inappropriate pattern; (B) is a graph showing a first inappropriate pattern of the pulse wave difference β(t). (C) is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the first inappropriate pattern. (A) is a graph showing time-series data of an IR light signal and a red light signal according to a second inappropriate pattern; (B) is a graph showing a second inappropriate pattern of the pulse wave difference β(t). (C) is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the second inappropriate pattern. (A) is a graph showing time-series data of an IR light signal and a red light signal according to a third inappropriate pattern; (B) is a graph showing a third inappropriate pattern of the pulse wave difference β(t). (C) is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the third inappropriate pattern. (A) is a graph showing time-series data of an IR light signal and a red light signal according to a fourth inappropriate pattern; (B) is a graph showing a fourth inappropriate pattern of the pulse wave difference β(t). (C) is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the fourth inappropriate pattern. FIG. 11 is a scatter diagram showing an example of the correlation between the IR light output voltage and the red light output voltage according to the second embodiment; FIG. 4 is a diagram showing an example of a preparation period, a breath-holding period, and a follow-up period in LFCT measurement. (A) and (B) are diagrams for explaining the correlation between an IR light signal and a red light signal. It is a figure where it uses for description of correlation with an IR light signal and a red light signal. (A) is a graph showing time-series data of an IR light signal and a red light signal during systole and diastole. (B) is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage during systole and diastole. (A) is a scatter diagram showing an example of regression lines for each of systole and diastole when there is no change in blood oxygen concentration. (B) is a scatter diagram showing an example of regression lines for each of systole and diastole when there is a change in blood oxygen concentration.

Hereinafter, an example of a mode for implementing the technology of the present disclosure will be described in detail with reference to the drawings. Components and processes having the same actions, actions, and functions are given the same reference numerals throughout the drawings, and overlapping descriptions may be omitted as appropriate. Each drawing is only schematically shown to the extent that the technology of the present disclosure can be fully understood. Therefore, the technology of the present disclosure is not limited only to the illustrated examples. Further, in this embodiment, descriptions of configurations that are not directly related to the present invention and well-known configurations may be omitted.

[First embodiment]
First, with reference to FIG. 1, a method of measuring blood flow information and oxygen saturation in blood, which is an example of biological information, particularly concerning blood, among biological information will be described.

FIG. 1 is a schematic diagram showing a measurement example of blood flow information and blood oxygen saturation according to the present embodiment.
As shown in FIG. 1, the blood flow information and the oxygen saturation in the blood are obtained by irradiating light from the light emitting element 1 toward the subject's body (living body 8) and receiving light with the light receiving element 3. It is measured using the intensity of light reflected or transmitted by the arteries 4, veins 5, capillaries 6, etc., that is, the amount of reflected light or transmitted light received.

(Measurement of blood flow information)
FIG. 2 is a graph showing an example of changes in the amount of received light due to reflected light from the living body 8 according to this embodiment.
2, the horizontal axis of the graph 80 represents the passage of time, and the vertical axis represents the amount of light received by the light receiving element 3. As shown in FIG.

As shown in FIG. 2, the amount of light received by the light-receiving element 3 changes with the passage of time. It is believed that there is.

As the first optical phenomenon, a change in light absorption due to a change in the amount of blood present in the blood vessel being measured due to pulsation can be considered. Blood contains blood cells such as red blood cells, and moves in blood vessels such as capillaries 6. Therefore, the number of blood cells moving in the blood vessels changes as the amount of blood changes. may affect the amount of light received.

As the second optical phenomenon, the influence of Doppler shift can be considered.

FIG. 3 is a schematic diagram for explaining the Doppler shift that occurs when a laser beam is applied to a blood vessel according to this embodiment.

As shown in FIG. 3, for example, when coherent light 40 with a frequency ω ₀ such as laser light is irradiated from the light emitting element 1 to a region including a capillary 6, which is an example of a blood vessel, blood cells moving in the capillary 6 Scattered scattered light 42 will undergo a Doppler shift with a difference frequency Δω ₀ determined by the moving velocity of blood cells. On the other hand, the frequency of scattered light 42 scattered by a tissue (stationary tissue) such as skin that does not contain moving bodies such as blood cells maintains the same frequency ω ₀ as the frequency of the irradiated laser light. Therefore, the frequency ω ₀ +Δω ₀ of the laser light scattered by the blood vessels such as the capillaries 6 and the frequency ω ₀ of the laser light scattered by the stationary tissue interfere with each other, and the beat signal having the difference frequency Δω ₀ is generated by the light receiving element 3. , and the amount of light received by the light receiving element 3 changes with the lapse of time. The difference frequency Δω ₀ of the beat signal observed by the light-receiving element 3 depends on the moving speed of blood cells, but is within a range with an upper limit of about several tens of kHz.

Also, as the third optical phenomenon, the influence of speckle can be considered.

FIG. 4 is a schematic diagram for explaining speckles that occur when laser light is applied to a blood vessel according to the present embodiment.

As shown in FIG. 4, when coherent light 40 such as laser light is irradiated from light emitting element 1 to blood cells 7 such as red blood cells moving in the direction of arrow 44 in a blood vessel, the laser light collides with blood cells 7. scatters in different directions. Scattered light interferes randomly due to different phases. This produces a random speckled light intensity distribution. A light intensity distribution pattern formed in this way is called a "speckle pattern".

As already explained, since the blood cells 7 move through the blood vessel, the light scattering state of the blood cells 7 changes, and the speckle pattern changes over time. Therefore, the amount of light received by the light receiving element 3 changes over time.

Next, an example of how to obtain blood flow information will be described. When the amount of light received by the light receiving element 3 over time shown in FIG. 2 is obtained, the data included in the range of a predetermined unit time T ₀ is cut out, and the data is subjected to, for example, a Fast Fourier Transform. : FFT), a spectral distribution for each frequency ω is obtained.

FIG. 5 is a graph showing an example of spectral distribution for each frequency ω in unit time T ₀ according to this embodiment. In FIG. 5, the horizontal axis of graph 82 represents frequency ω, and the vertical axis represents spectrum intensity.

Here, the blood volume is proportional to the value obtained by normalizing the area of the power spectrum represented by the shaded area 84 surrounded by the horizontal and vertical axes of the graph 82 by the total amount of light. In addition, since the blood flow velocity is proportional to the frequency average value of the power spectrum represented by the graph 82, the value obtained by integrating the product of the frequency ω and the power spectrum at the frequency ω with respect to the frequency ω is divided by the area of the shaded area 84. proportional to

Since the blood flow is expressed as the product of the blood volume and the blood flow velocity, it can be obtained from the above formula for calculating the blood volume and the blood flow velocity. Blood flow, blood flow velocity, and blood volume are examples of blood flow information, and blood flow information is not limited to these.

FIG. 6 is a graph showing an example of changes in blood flow per unit time T ₀ according to this embodiment. In FIG. 6, the horizontal axis of graph 86 represents time, and the vertical axis represents blood flow.

As shown in FIG. 6, the blood flow fluctuates over time, and the tendency of the fluctuation is classified into two types. For example, the variation width 90 of the blood flow volume in the interval _T2 is larger than the variation width 88 of the blood flow volume in the interval _T1 in FIG. This is because the change in the blood flow in the section _T1 is mainly due to the movement of the pulse, whereas the change in the blood flow in the section _T2 is caused by, for example, congestion or nerve activity. It is considered that this is because it shows the accompanying change in blood flow.

(Measurement of oxygen saturation)
Next, the measurement of blood oxygen saturation will be described. Blood oxygen saturation is an example of blood oxygen concentration, and is an index showing how much hemoglobin in blood binds to oxygen. symptoms are more likely to occur.

FIG. 7 is a graph showing an example of changes in the amount of light absorbed by the living body 8 according to this embodiment. In FIG. 7, the horizontal axis of the graph 92 represents time, and the vertical axis represents the amount of light absorption.

As shown in FIG. 7, the amount of light absorbed by the living body 8 tends to fluctuate over time.

Furthermore, looking at the details of the variation in light absorption in the living body 8, it is assumed that the amount of light absorption varies mainly due to the artery 4, and that the amount of light absorption does not vary in other tissues, including veins 5 and stationary tissue, compared to the artery 4. It is known to be an amount of variation that can be considered. This is because the arterial blood pumped from the heart moves in the blood vessel with pulse waves, so the artery 4 expands and contracts over time along the cross-sectional direction of the artery 4, and the thickness of the artery 4 changes. . In FIG. 7, the range indicated by the arrow 94 indicates the amount of change in the amount of light absorption corresponding to the change in the thickness of the artery 4. As shown in FIG.

In FIG. 7, if the amount of light received at time t _a is I _a and the amount of light received at time t _b is I _b , the amount of change ΔA in the amount of light absorption due to the change in the thickness of the artery 4 can be expressed by equation (1). be done.

(Number 1)
ΔA=ln(I _b /I _a ) (1)

FIG. 8 is a graph showing an example of absorbance characteristics of hemoglobin according to this embodiment.
In FIG. 8, the vertical axis represents absorbance and the horizontal axis represents wavelength.

As shown in FIG. 8, hemoglobin bound to oxygen flowing through the artery 4 (oxyhemoglobin) is particularly likely to absorb light in the infrared (IR) region having a wavelength around 880 nm, and is not bound to oxygen. It is known that hemoglobin (reduced hemoglobin) is particularly apt to absorb light in the red region having a wavelength around 665 nm. Furthermore, it is known that the oxygen saturation has a proportional relationship with the ratio of the amount of change ΔA in the amount of light absorption at different wavelengths.

Therefore, compared to other wavelength combinations, infrared light (IR light) and red light are more likely to cause a difference in the amount of light absorption between oxygenated hemoglobin and reduced hemoglobin. By calculating the ratio between the amount of change ΔA _IR and the amount of change ΔA _Red in the amount of absorption when the living body 8 is irradiated with red light, the oxygen saturation S is calculated by the equation (2). In addition, k in (2) Formula is a proportionality constant.

(Number 2)
S=k(ΔA _Red / _ΔAIR ) (2)

That is, when calculating the oxygen saturation in blood, a plurality of light-emitting elements 1 that irradiate light of different wavelengths, specifically, a light-emitting element 1 that irradiates IR light and a light-emitting element 1 that irradiates red light. may overlap part of the light emission period, but it is desirable that the light emission periods do not overlap. Then, the reflected light or transmitted light from each light emitting element 1 is received by the light receiving element 3, and the amount of light received at each light receiving time is obtained by formulas (1) and (2), or by modifying these formulas. Oxygen saturation is measured by calculating a known formula.

As a known formula obtained by modifying the above formula (1), for example, formula (1) may be developed to express the amount of change ΔA in the amount of light absorption as in formula (3).

(Number 3)
ΔA= _lnIb - _lnIa (3)

Also, equation (1) can be transformed into equation (4).

(Number 4)
ΔA=ln( _Ib / _Ia )=ln(1+( _Ib - _Ia )/ _Ia ) (4)

Since (I _b -I _a )<<I _a , ln(I _b /I _a )≈(I _b -I _a )/I _a is usually established. Equation (5) may be used as the amount of change ΔA in the amount of light absorption.

(Number 5)
ΔA≈(I _b −I _a )/I _a (5)

When it is necessary to distinguish between the light-emitting element 1 that emits IR light and the light-emitting element 1 that emits red light, the light-emitting element 1 that emits IR light is hereinafter referred to as "light-emitting element LD1". The light-emitting element 1 that emits red light will be referred to as "light-emitting element LD2". Further, as an example, the light emitting element LD1 is assumed to be the light emitting element 1 used for calculating the blood flow rate, and the light emitting elements LD1 and LD2 are assumed to be the light emitting elements 1 used for calculating the blood oxygen saturation.

In addition, when measuring the oxygen saturation in blood, it is known that the measurement frequency of the amount of light received is sufficient at about 30 Hz to 1000 Hz. Also, about 30 Hz to 1000 Hz is sufficient. Therefore, from the viewpoint of power consumption of the light emitting element LD2, it is preferable to set the emission frequency of the light emitting element LD2 lower than that of the light emitting element LD1. , the light emitting element LD1 and the light emitting element LD2 may alternately emit light.

Next, the principle of measuring a respiratory waveform from a pulse wave signal obtained from a peripheral site of the living body 8 will be described with reference to FIG. As an example of the peripheral site here, fingertips, toetips, earlobes, and the like can be mentioned. Note that the peripheral site includes a site beyond the elbow, a site beyond the knee, and the like. A respiratory waveform is a waveform of a signal that indicates the respiratory state of the living body 8, and is a waveform of a time-series signal that indicates temporal changes in expiration and inspiration.

FIG. 9 is a schematic diagram for explaining the principle of measuring a respiratory waveform according to this embodiment. As shown in FIG. 9, during inspiration, the amplitude of the pulse wave signal decreases by the following steps.
(S1) The intrathoracic pressure decreases to negative pressure, and the lungs expand.
(S2) The venous return volume increases.
(S3) The amount of blood flowing into the right atrium increases.
(S4) The vascular bed of the lung expands and the amount of blood retained in the lung increases.
(S5) The amount of blood returning from the lungs to the left atrium decreases.
(S6) The stroke volume of the left ventricle decreases.
(S7) The amplitude of the pulse wave signal decreases.

On the other hand, during exhalation, the amplitude of the pulse wave signal increases through the following steps.
(S8) Blood squeezed out of the lungs flows into the left ventricle.
(S9) The amplitude of the pulse wave signal increases.

In other words, since the pulsation caused by the "pumping action of the heart" is superimposed by the effect of the "pumping action of the lungs" caused by respiration, it is possible to measure the respiratory waveform from the pulse wave signal obtained from the peripheral part of the living body 8. becomes.

Next, with reference to FIG. 10, the principle of measuring LFCT (Lung to Finger Circulation Time), which is an example of an index correlated with the stroke volume of blood from the heart, will be described. The stroke volume referred to here is not limited to the above-described cardiac output, but also includes stroke volume, cardiac index, and the like. Cardiac output is defined as the volume of blood ejected into arteries by contraction of the heart per unit time (for example, 1 minute). Stroke volume is defined as the volume of blood pumped into an artery by one contraction of the heart. Cardiac index is defined as the coefficient obtained by dividing the cardiac output by the subject's body surface area. LFCT is defined as the time it takes for oxygen taken in by respiration to reach the fingertips through the lungs and heart.

FIG. 10 is a schematic diagram for explaining the principle of measuring the stroke volume according to this embodiment. As shown in FIG. 10, there is a correlation between the stroke volume and LFCT. For example, if CO is the cardiac output, which is an example of the cardiac output, the cardiac output CO is calculated by the following equation (6).

(Number 6)
CO=(a ₀ ×S)/LFCT (6)
Here, a ₀ is a constant, and for example a ₀ =50 is used. Also, S is the subject's body surface area (m ² ), and the unit of LFCT is seconds.

FIG. 11 is a graph for explaining an example of the LFCT measurement method according to this embodiment. In FIG. 11, the vertical axis represents the reciprocal of oxygen saturation, and the horizontal axis represents time.

As shown in FIG. 11, the LFCT according to this embodiment is measured from the change in oxygen saturation described above. That is, the LFCT is obtained by measuring the time from when breathing is resumed after stopping breathing for a certain period to the inflection point indicating recovery of oxygen saturation.

By the way, as described above, the simplest method for changing the blood oxygen concentration of a subject is to have the subject hold his or her breath. However, because of the reluctance to hold the breath, there are cases where the breath is held while the lungs contain a lot of air. In this case, the waveform pattern representing the change in the blood oxygen concentration becomes an inappropriate pattern in which the inflection point of the waveform pattern caused by the change in the blood oxygen concentration cannot be specified. With the existing technology, a complicated algorithm is required to determine whether the waveform pattern is appropriate, and the load on the determination process is heavy. Therefore, a simple determination method is desired.

The present disclosure describes a biological information measurement device that easily determines that the waveform pattern is not inappropriate compared to determining an appropriate waveform pattern using an existing algorithm.

In this embodiment, when there is no change in the blood oxygen concentration, there is only a change in the output due to pulsation (blood volume), and the correlation of the pulse wave due to the two wavelengths (for example, IR light signal and red light signal) to be measured is Note that the correlation is low when there is a high and changing blood oxygen concentration. In other words, when the correlation between the two pulse waves is high, the waveform pattern representing changes in blood oxygen concentration is inappropriate, and when the correlation is low, the waveform pattern representing changes in blood oxygen concentration is inappropriate. Determine that there is.

FIG. 12 is a block diagram showing an example of the electrical configuration of the biological information measuring device 10 according to this embodiment.

As shown in FIG. 12, the biological information measuring apparatus 10 according to this embodiment includes a light emission control unit 12, a drive circuit 14, an amplifier circuit 16, an A/D (Analog/Digital) conversion circuit 18, a control unit 20, a display unit 22, a light-emitting element LD1, a light-emitting element LD2, and a light-receiving element 3; The light emitting element LD1, the light emitting element LD2, the light receiving element 3, and the amplifier circuit 16 constitute a sensor section. Further, the light emission control section 12, the drive circuit 14, the amplifier circuit 16, the A/D conversion circuit 18, the control section 20, and the display section 22 constitute a main body section. In this embodiment, the sensor section and the main body section are configured separately, and can communicate with each other via a cable or wirelessly. Note that the sensor section and the main body section may be configured integrally. Also, the sensor unit is attached so as to be in close contact with the living body 8 so as to prevent the input of external light. The sensor unit according to the present embodiment is attached to the fingertip of the living body 8 as an example, but can be attached to other peripheral sites such as the earlobe.

The light emission control unit 12 outputs a control signal for controlling the light emission cycle and light emission period of the light emitting elements LD1 and LD2 to the driving circuit 14 including a power supply circuit that supplies driving power to the light emitting elements LD1 and LD2. . Note that the light emission control unit 12 may be implemented as part of the control unit 20 .

Upon receiving the control signal from the light emission control unit 12, the drive circuit 14 supplies drive power to the light emitting element LD1 and the light emitting element LD2 in accordance with the light emitting period and the light emitting period indicated by the control signal. Drive LD2.

The light-receiving element 3 receives the light of the first wavelength from the light-emitting element LD1, and receives the light of the second wavelength from the light-emitting element LD2 and the first light-receiving signal corresponding to the received light of the first wavelength. , and a second received light signal corresponding to the received light of the second wavelength. In the present embodiment, a wavelength range corresponding to the infrared region is applied as the first wavelength, and a wavelength range corresponding to the red region is applied as the second wavelength. An IR light signal is applied to the first light reception signal, and a red light signal is applied to the second light reception signal.

The amplifier circuit 16 converts the current corresponding to the intensity of the light generated by the light receiving element 3 into a voltage and amplifies it to a voltage level defined as the input voltage range of the A/D conversion circuit 18 .

The A/D conversion circuit 18 receives the voltage amplified by the amplifier circuit 16, quantifies the amount of light received by the light receiving element 3 represented by the magnitude of the voltage, and outputs the result.

The control unit 20 includes a CPU (Central Processing Unit) 20A, a ROM (Read Only Memory) 20B, and a RAM (Random Access Memory) 20C. A biological information measurement program is stored in the ROM 20B. This biological information measurement program may be pre-installed in the biological information measurement device 10, for example. The biological information measurement program may be stored in a non-volatile storage medium or distributed via a network and installed in the biological information measurement device 10 as appropriate. Examples of nonvolatile storage media include CD-ROMs (Compact Disc Read Only Memory), magneto-optical discs, HDDs, DVD-ROMs (Digital Versatile Disc Read Only Memory), flash memories, memory cards, and the like. be.

The display unit 22 displays the measurement results of the biological information. For the display unit 22, for example, a liquid crystal display (LCD), an organic EL (Electro Luminescence) display, or the like is used. The display unit 22 integrally has a touch panel.

FIG. 13 is a diagram showing an example of arrangement of the light-emitting element LD1, the light-emitting element LD2, and the light-receiving element 3 in the biological information measuring device 10 according to this embodiment. FIG. 14 is a diagram showing another example of arrangement of the light-emitting element LD1, the light-emitting element LD2, and the light-receiving element 3 in the biological information measuring device 10 according to this embodiment.

As shown in FIG. 13, the light-emitting element LD1, the light-emitting element LD2, and the light-receiving element 3 are arranged side by side toward one surface of the living body 8. As shown in FIG. In this case, the light receiving element 3 receives light from the light emitting elements LD1 and LD2 that has passed through the vicinity of the surface of the living body 8 .

The arrangement of the light emitting element LD1, the light emitting element LD2, and the light receiving element 3 is not limited to the arrangement example shown in FIG. For example, as shown in FIG. 14, the light-emitting elements LD1 and LD2 and the light-receiving element 3 may be arranged at positions facing each other with the living body 8 interposed therebetween. In this case, the light receiving element 3 receives light from the light emitting elements LD1 and LD2 that has passed through the living body 8 .

Here, as an example, the light-emitting element LD1 and the light-emitting element LD2 are both surface-emitting laser elements, but they may be edge-emitting laser elements. Further, the light emitted from each of the light emitting element LD1 and the light emitting element LD2 may not be laser light. In this case, a light-emitting diode (LED) or an organic light-emitting diode (OLED) may be used for each of the light-emitting element LD1 and the light-emitting element LD2.

FIG. 15 is a graph showing an example of data sampling timing in the light receiving element 3 according to this embodiment. In FIG. 15, positions of circles indicate sampling timings. In FIG. 15, the vertical axis represents the output voltage of the light receiving element 3, and the horizontal axis represents time.

As shown in FIG. 15, when the output voltage corresponding to the light received _by the light receiving element 3 from the light emitting element LD1 is IR ₁ , IR ₂ , . = IR ₁ , IR ₂ , . . . , IR _n are obtained. Similarly, when the output voltages corresponding to the light received by the light receiving element 3 from the light emitting element LD2 are _Red1 , _Red2 , . . . , _Redn , Red(t)= _Red1 , Red ₂ , . . . , Red _n are obtained. At this time, a period during which no light is emitted may be provided for both the light emitting element LD1 and the light emitting element LD2 to obtain outputs Dark ₁ , Dark ₂ , . . . , Dark _n in the dark state. In this case, IR(t) may be IR ₁ -Dark ₁ , IR ₂ -Dark ₂ , . . . IR _n -Dark _n . Similarly, Red(t) may be Red ₁ -Dark ₁ , Red ₂ -Dark ₂ , . . . Red _n -Dark _n . It is desirable to sample these data while the output is stable near the end of the light emitting period.

The CPU 20A of the biological information measurement device 10 according to this embodiment writes the biological information measurement program stored in the ROM 20B into the RAM 20C and executes it, thereby functioning as each unit shown in FIG. Note that the CPU 20A is an example of a processor.

FIG. 16 is a block diagram showing an example of the functional configuration of the biological information measuring device 10 according to the first embodiment.

As shown in FIG. 16, the CPU 20A of the biological information measurement device 10 according to the present embodiment includes an acquisition unit 30, a correction unit 31, a calculation unit 32, a determination unit 33, a detection unit 34, an identification unit 35, and an estimation unit 36. Function.

The obtaining unit 30 obtains each of the IR light signal and the red light signal output from the light receiving element 3 . In this case, the IR light signal is an example of the first signal, and the red light signal is an example of the second signal.

For example, the correction unit 31 adjusts the amount of change in the IR light signal (hereinafter referred to as ΔIR) and the amount of change in the red light signal (hereinafter referred to as ΔRed) is corrected by multiplying the IR light signal by a coefficient. This change in arterial blood volume represents the amplitude of the pulsation associated with the heartbeat. Note that the correction target may be the value of the red light signal.

It is desirable that the above correction be a correction to make ΔIR and ΔRed the same. where ΔIR is expressed as the amplitude of the IR light signal and ΔRed is expressed as the amplitude of the red light signal. In this case, the above correction is performed by multiplying the IR light signal value (IR(t)) by a coefficient α represented by the amplitude ratio of ΔIR and ΔRed (ΔRed/ΔIR). That is, the corrected output of IR(t) is α×IR(t).

The calculator 32 calculates a waveform pattern representing changes in blood oxygen concentration in the living body 8 based on the IR light signal and the red light signal corrected by the corrector 31 . The waveform pattern representing the change in the blood oxygen concentration is represented, for example, by the difference between the IR light signal and the red light signal corrected by the correction unit 31 (hereinafter, this difference is referred to as "pulse wave difference"). be. For example, when the pulse wave difference is β(t), β(t) is obtained by the following equation (7).

(Number 7)
β(t)=α×IR(t)−Red(t) (7)

The determination unit 33 determines that the pulse wave difference β(t) calculated by the calculation unit 32 is appropriate when the value representing the degree of correlation between the IR light signal and the red light signal is less than the threshold. As the value representing the degree of correlation, for example, a regression obtained from a scatter diagram plotting the value of the IR light signal (eg, voltage value) and the value of the red light signal (eg, voltage value) in a predetermined period A coefficient of determination (=R ² ), which is an index representing the goodness of fit of the straight line, is used. A determination coefficient is calculated|required by a well-known method. The coefficient of determination is a value between 0 and 1, and the closer to 1, the higher the correlation between the IR light signal and the red light signal. Also, the threshold is, for example, 0.8, more preferably 0.7. On the other hand, when the value representing the degree of correlation is equal to or greater than the threshold, the determining unit 33 determines that the pulse wave difference β(t) is inappropriate, and issues a warning to prompt remeasurement. This warning includes, for example, a message prompting the user to take a full breath, hold his breath, and re-measure.

FIG. 17A is a scatter diagram showing an example of the correlation between the IR light output voltage and the red light output voltage when there is a change in blood oxygen concentration. FIG. 17B is a scatter diagram showing an example of the correlation between the IR light output voltage and the red light output voltage when there is no change in blood oxygen concentration. In the figure, the vertical axis indicates the output voltage of the red light signal, and the horizontal axis indicates the output voltage of the IR light signal.

According to the scatter diagram of FIG. 17(A), when the blood oxygen concentration is changing, that is, when the oxygen saturation is decreasing, the correlation between the IR light signal and the red light signal is low, and the coefficient of determination ( In this example, R ² =0.4995) is below the threshold (eg, 0.8). In this case, the pulse wave difference β(t) is determined to be appropriate. On the other hand, according to the scatter diagram of FIG. 17B, when the blood oxygen concentration does not change, that is, when the oxygen saturation does not decrease, the correlation between the IR light signal and the red light signal is high, and the determination The coefficient (R ² =0.9258 in this example) is greater than or equal to the threshold (0.8, for example). In this case, the pulse wave difference β(t) is determined to be inappropriate.

A value representing the degree of correlation is calculated from the IR light signal and the red light signal at a predetermined time (for example, 30 seconds) after the intake oxygen amount of the living body 8 is changed (after respiration is resumed). Since it is difficult to obtain an accurate correlation with one beat, it is desirable that the predetermined time be a period of two or more beats of the living body 8 .

The detection unit 34 detects the inflection point of the blood oxygen concentration associated with the change in the intake oxygen amount of the living body 8 from the pulse wave difference β(t) determined to be appropriate by the determination unit 33 . One example of a method for changing the amount of inspired oxygen is by holding the breath. In addition, the change in the amount of inspired oxygen here means a change that changes the blood oxygen concentration over at least several seconds, and is normal breathing conditions (e.g., average breathing frequency, deep breathing) It does not include slight changes due to In other words, there is no change in the amount of inspired oxygen under normal breathing conditions, but when the normal breathing state is changed by stopping breathing, weakening breathing, or inhaling gas with a high oxygen concentration, the amount of inspired oxygen changes. It is judged that

The identifying unit 35 identifies the time from when the intake oxygen amount of the living body 8 changes to the inflection point of the blood oxygen concentration detected by the detecting unit 34 . Note that the time point at which the inspiratory oxygen amount changes is, for example, the time point at which breathing is resumed from a state of respiratory arrest. In this embodiment, the time specified by the specifying unit 35 is LFCT.

The estimation unit 36 estimates the stroke volume from the LFCT identified by the identification unit 35 . For example, the above equation (6) is used to estimate the cardiac output, which is an example of the stroke volume.

Here, each of the IR light signal and the red light signal contains a component representing changes in blood volume due to pulsation, nerve activity, etc., and a component representing changes in oxygen concentration due to changes in the amount of inspired oxygen. . Then, according to the pulse wave difference β(t), by multiplying IR(t) by a coefficient α(=ΔRed/ΔIR) and adopting the difference between α×IR(t) and Red(t), Components representing changes in arterial blood volume are canceled out, and only components representing changes in oxygen concentration are extracted.

In the above description, the coefficient α is set to (ΔRed/ΔIR) to correct the IR light signal, but the coefficient α may be set to (ΔIR/ΔRed) to correct the red light signal. In this case, the pulse wave difference β(t) is obtained by the following equation (8).

(Number 8)
β(t)=IR(t)−α×Red(t) (8)

Further, in the above description, the case of correcting either one of the IR light signal and the red light signal, which are examples of the two pulse wave signals, has been described. good. Also, in the above description, the red light signal is subtracted from the IR light signal, but the IR light signal may be subtracted from the red light signal. In that case, the direction of the inflection point appearing in β(t) is different.

Here, the pulse wave signal for obtaining the coefficient α and the pulse wave signal to which the obtained coefficient α is applied are temporally shifted. That is, the above correction is performed by multiplying IR(t) or Red(t) after changing the intake oxygen amount by a coefficient α represented by the amplitude ratio between ΔIR and ΔRed before changing the intake oxygen amount. is done in For example, it is desirable to use the pulse wave signal at rest before respiratory arrest as the pulse wave signal for obtaining the coefficient α.

Next, the action of the biological information measuring device 10 according to the first embodiment will be described with reference to FIG. 18 . Note that FIG. 18 is a flowchart showing an example of the flow of processing of the biological information measurement program according to the first embodiment.

First, when the biometric information measuring apparatus 10 is turned on by the operation of the subject or the person in charge of measurement, the biometric information measuring program is started and the following steps are executed.

At step 100 in FIG. 18 , the CPU 20A obtains the amplitude (ΔIR) of the IR light signal obtained from the light receiving element 3 and the amplitude (ΔRed) of the red light signal obtained from the light receiving element 3 . In this step 100, first, each of ΔIR and ΔRed is obtained as the pulse wave amplitude while the subject is in a resting state.

FIG. 19 is a graph showing an example of the amplitude of the IR light signal and the amplitude of the red light signal according to this embodiment.
In FIG. 19, the vertical axis represents the output voltage of the light receiving element 3, and the horizontal axis represents time.

As shown in FIG. 19, the CPU 20A acquires ΔIR from IR(t), which is the time-series data of the value of the IR light signal, and acquires ΔRed from Red(t), which is the time-series data of the value of the red light signal. do.

At step 102, the CPU 20A derives a coefficient α represented by an amplitude ratio between ΔIR and ΔRed based on ΔIR and ΔRed obtained at step 100. FIG. The coefficient α is derived by the method shown below as an example.
(a) Adopt an amplitude ratio obtained at an arbitrary timing. In this case, it may be after the start of LFCT measurement.
(b) Employing an average value of a plurality of amplitude ratios obtained over a certain period of time. In the case of this method, a coefficient α suitable for measurement can be obtained as compared with the case of deriving the coefficient α by adopting the amplitude ratio of only one point.
(c) After the measurement, as an example, as shown in FIG. 20, the coefficient α is varied between 0 and 1, and the value with the smallest pulsation frequency component appearing in the pulse wave difference β(t) is adopted. However, when the coefficient α is (ΔRed/ΔIR), the condition of ΔIR>ΔRed is satisfied. For this method, the measurement time is shortened, for example, because it is not necessary to derive the coefficient α during the measurement.

FIG. 20 is a graph showing an example of the relationship between the coefficient α and the pulse wave difference β(t) according to this embodiment.
In FIG. 20, the vertical axis represents the pulse wave difference β(t). Also, in this example, α=ΔRed/ΔIR and β(t)=α×IR(t)−Red(t).

The upper diagram in FIG. 20 shows the overall waveform and expanded waveform of the pulse wave difference β(t) when the coefficient α=0.2. The left figure is the overall waveform, and the right figure is the expanded waveform.

The middle diagram in FIG. 20 shows the overall waveform and expanded waveform of the pulse wave difference β(t) when the coefficient α=0.3583. The left figure is the overall waveform, and the right figure is the expanded waveform.

The lower diagram of FIG. 20 shows the overall waveform and expanded waveform of the pulse wave difference β(t) when the coefficient α=0.6. The left figure is the overall waveform, and the right figure is the expanded waveform.

From the above, it can be seen that the pulsation frequency component appearing in the pulse wave difference β(t) is the smallest when the coefficient α=0.3583. Therefore, according to the above method (c), the coefficient α=0.3583 is adopted, and the pulse wave difference β(t) with the inflection point of the oxygen concentration at the correct position is obtained.

At step 104, the CPU 20A accepts an instruction to start LFCT measurement while the subject remains at rest. For example, the subject or the person in charge of measurement issues an instruction to start measurement via the touch panel of the display unit 22 or the like.

At step 106, the CPU 20A instructs the subject to start breath-holding. Specifically, for example, a message such as "Please hold your breath."

At step 108, the CPU 20A instructs the subject to resume breathing after a certain period of time (for example, 20 seconds) has passed since the start of breath-holding. Specifically, for example, a message may be displayed on the display unit 22 to instruct resuming breathing by countdown, or an instruction may be given by voice. Alternatively, the fact that breathing has resumed may be input by the subject's own operation (pressing a button, etc.).

At step 110, the CPU 20A determines whether or not a predetermined time has elapsed after resuming breathing. This predetermined time is set in advance as a time for follow-up observation, and is, for example, 60 seconds. In addition, since the arrival time of oxygen differs depending on the measurement site, it is preferable to preset the time for follow-up observation suitable for the measurement site. If it is determined that the predetermined time has elapsed (in the case of affirmative determination), the process proceeds to step 112, and if it is determined that the predetermined time has not elapsed (in the case of a negative determination), step 110 waits.

At step 112, the CPU 20A performs correction by multiplying IR(t) or Red(t) obtained from the above measurement by the coefficient α derived at step 102 above. In the present embodiment, IR(t) is multiplied by the coefficient α(ΔRed/ΔIR) for correction. However, when correcting Red(t), the coefficient α may be set to ΔIR/ΔRed.

FIG. 21 is a graph showing an example of time-series data of an IR light signal and time-series data of a red light signal according to this embodiment. In FIG. 21, the vertical axis represents the output voltage of the light receiving element 3, and the horizontal axis represents time. As shown in FIG. 21, graph g1 represents IR(t), which is time-series data of the IR light signal. A graph g2 represents Red(t), which is the time-series data of the red light signal.

FIG. 22 is a graph showing an example of corrected time-series data of the IR light signal and time-series data of the red light signal according to the present embodiment. In FIG. 22, the vertical axis represents the output voltage of the light receiving element 3, and the horizontal axis represents time. As shown in FIG. 22, a graph g3 represents α×IR(t) obtained by multiplying IR(t) by a coefficient α to adjust the offset. A graph g4 represents Red(t), which is the time-series data of the red light signal.

It should be noted that the instruction to resume breathing in step 108 may be made when a decrease in blood oxygen concentration is detected.

FIG. 23 is a graph showing an example of a monitoring result based on a pulse wave difference according to the present embodiment. In FIG. 23, the vertical axis represents pulse wave difference β(t), and the horizontal axis represents time. As understood from FIG. 23, the change in oxygen saturation due to breath-holding clearly appears.

Next, in step 114, the CPU 20A calculates the pulse wave difference β(t) from α×IR(t) obtained by the correction in step 112 and Red(t) using the above equation (7). calculate. When Red(t) is corrected, the pulse wave difference β(t) can be calculated using the above equation (8).

Next, at step 116, the CPU 20A determines whether or not a value representing the degree of correlation between the IR light signal and the red light signal (e.g. coefficient of determination ^R2 ) is less than a threshold value (e.g. 0.8). If it is determined that the value representing the degree of correlation is less than the threshold value (in the case of affirmative determination), the pulse wave difference β(t) calculated in step 114 is determined to be appropriate, the process proceeds to step 118, and the value representing the degree of correlation is greater than or equal to the threshold value (negative determination), the pulse wave difference β(t) calculated in step 114 is determined to be inappropriate, and the process proceeds to step 122 .

At step 118, the CPU 20A detects the inflection point of the blood oxygen concentration associated with the change in the intake oxygen amount of the subject from the pulse wave difference β(t) calculated at step 114. FIG.

At step 120, the CPU 20A specifies the time from when the intake oxygen amount of the subject changes to the point of inflection detected at step 118 as LFCT, and terminates the series of processes by the biological information measurement program. In the present embodiment, the processing up to the specification of the LFCT is performed. Further, the specified LFCT may be applied to the above equation (6) to calculate the cardiac output, which is an example of the stroke volume. .

FIG. 24 is a graph showing an example of LFCT identified from the pulse wave difference β(t) according to this embodiment. In FIG. 24, the vertical axis represents the pulse wave difference β(t), and the horizontal axis represents time.

As shown in FIG. 24, the time from the time of resuming breathing to the inflection point indicated by the maximum value of the pulse wave difference β(t) (=α×IR(t)−Red(t)) is LFCT. do.

In FIG. 24, a graph g5 represents the pulse wave difference β(t) as a moving average of single-width n data (n=64 in this example). A graph g6 represents the pulse wave difference β(t) when the coefficient α=0.3583. By using the moving average of the single-width n data as the pulse wave difference β(t) in this way, the residual pulse wave component due to the difference in the blood oxygen concentration is removed, so that a more accurate LFCT can be obtained.

Further, according to the graphs g5 and g6 shown in FIG. 24, the value of the pulse wave difference β(t) increases immediately after the breath-holding period ends and breathing resumes, and after reaching one peak, the value I know it's going down. Since the pulse wave difference β(t) increases as the blood oxygen concentration decreases, the peak point is when the blood oxygen concentration is the lowest, and the inflection point where it begins to fall is when oxygen is released in the blood due to resumption of breathing. It indicates that it has started to be taken in. Therefore, the time from resuming respiration to reaching the peak is specified as LFCT.

On the other hand, at step 122, the CPU 20A issues a warning to prompt re-measurement, and terminates the series of processes by the biological information measurement program. Note that this warning is performed by, for example, displaying a message on the display unit 22 prompting the user to take a full breath, hold his breath, and perform the measurement again.

Next, referring to FIGS. 25 to 28, a plurality of inappropriate patterns (first to fourth inappropriate patterns) of the pulse wave difference β(t) and the degree of correlation between the IR light signal and the red light signal are calculated. A specific description will be given of the correspondence with the coefficient of determination ^R2 .

FIG. 25A is a graph showing time-series data of the IR light signal and the red light signal according to the first inappropriate pattern. FIG. 25B is a graph showing a first inappropriate pattern of pulse wave difference β(t). FIG. 25C is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the first inappropriate pattern.

The first inappropriate pattern is, as shown in FIG. 25B, a pattern in which two or more minimum values exist and the values are so similar that it is impossible to determine which one should be the minimum value. In existing algorithms, if the ratio of the difference between the second smallest minimum value and the median value is greater than the threshold value (0.5) when the difference between the first smallest minimum value and the median value is 1, It was determined to be the first inappropriate pattern. On the other hand, as shown in FIG. 25C, the determination coefficient ^R2 representing the degree of correlation between the IR light signal and the red light signal is 0.8907, which is equal to or greater than the threshold value (for example, 0.8). In the method of the present embodiment, the pulse wave difference β(t) is determined to be inappropriate if the coefficient of determination R ² is equal to or greater than the threshold. is determined. The direction of the peaks in FIG. 25B is opposite to the direction of the peaks in FIG. Further, in the above, the pulse wave difference β(t) is defined as α×IR(t)−Red(t), but here the pulse wave difference β(t) is defined as Red(t)−α× Define IR(t). The same applies to FIGS. 26(B), 27(B), and 28(B) which will be described later.

FIG. 26A is a graph showing time-series data of the IR light signal and the red light signal according to the second inappropriate pattern. FIG. 26B is a graph showing a second inappropriate pattern of the pulse wave difference β(t). FIG. 26C is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the second inappropriate pattern.

The second inappropriate pattern is a pattern that has a local minimum but is broad, as shown in FIG. 26(B). In the existing algorithm, when the maximum value of the pulse wave difference β(t) after resuming respiration is 1 and the minimum value is 0, the length of time when both sides of the minimum value and the minimum value intersect with 0.2 is If the duration is equal to or greater than the threshold value (20 seconds), it is determined as the second inappropriate pattern. On the other hand, as shown in FIG. 26C, the coefficient of determination ^R2 representing the degree of correlation between the IR light signal and the red light signal is 0.8587, which is equal to or greater than the threshold value (0.8, for example). In the method of the present embodiment, if the coefficient of determination ^R2 is equal to or greater than the threshold, the pulse wave difference β(t) is determined to be inappropriate. is determined.

FIG. 27A is a graph showing time-series data of the IR light signal and the red light signal according to the third inappropriate pattern. FIG. 27B is a graph showing a third inappropriate pattern of the pulse wave difference β(t). FIG. 27C is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the third inappropriate pattern.

The third inappropriate pattern, as shown in FIG. 27B, is a pattern in which the value of the pulse wave difference β(t) at the time of resuming breathing is equal to or less than the minimum value. In the existing algorithm, the value at the time of resuming breathing is compared with the minimum value and the minimum value, and if the value at the time of resuming breathing is equal to or less than the minimum value, it is determined as the third inappropriate pattern. On the other hand, as shown in FIG. 27C, the coefficient of determination ^R2 representing the degree of correlation between the IR light signal and the red light signal is 0.9844, which is equal to or greater than the threshold value (for example, 0.8). In the method of the present embodiment, the pulse wave difference β(t) is determined to be inappropriate if the coefficient of determination ^R2 is equal to or greater than the threshold. is determined.

FIG. 28A is a graph showing time-series data of the IR light signal and the red light signal according to the fourth inappropriate pattern. FIG. 28B is a graph showing a fourth inappropriate pattern of pulse wave difference β(t). FIG. 28C is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage for the fourth inappropriate pattern.

The fourth inappropriate pattern, as shown in FIG. 28B, is a pattern in which the minimum value of the pulse wave difference β(t) after resuming breathing is not the minimum value. The existing algorithm compares the minimum value and the minimum value of the pulse wave difference β(t) and the minimum value, and if the minimum value and the minimum value do not match, it is determined as the fourth inappropriate pattern. On the other hand, as shown in FIG. 28C, the coefficient of determination ^R2 representing the degree of correlation between the IR light signal and the red light signal is 0.9758, which is greater than or equal to the threshold (for example, 0.8). In the method of the present embodiment, if the coefficient of determination ^R2 is equal to or greater than the threshold, the pulse wave difference β(t) is determined to be inappropriate. is determined.

Thus, according to the present embodiment, when the correlation between the two pulse waves is high, the waveform pattern representing changes in the blood oxygen concentration is inappropriate, and when the correlation is low, the blood oxygen concentration A waveform pattern representing a change is determined to be suitable. Therefore, inappropriate patterns can be easily determined compared to existing algorithms. In addition, since the state of change in blood oxygen concentration due to breath-holding can be known, when the change in blood oxygen concentration is small, a warning is issued and remeasurement is urged, thereby improving the reliability of the data.

[Second embodiment]
In the first embodiment, the coefficient α used for correction is the amplitude ratio between the amplitude of the IR light signal and the amplitude of the red light signal. In this embodiment, a mode will be described in which the coefficient α is calculated from the slope of the regression line obtained from the IR light signal and the red light signal.

Note that the biological information measuring device according to the present embodiment has the same components as the biological information measuring device 10 described in the first embodiment, so repeated description thereof will be omitted, and refer to FIG. Then, only the points of difference of the correction unit 31 will be described.

The correction unit 31 calculates the coefficient α from the slope of the regression line obtained from the IR light signal value and the red light signal value.

FIG. 29 is a scatter diagram showing an example of the correlation between the IR light output voltage and the red light output voltage according to the second embodiment.

A regression line is obtained from the scatter diagram of FIG. In this case, the regression line is obtained as y=0.3974x+0.2228. The slope "0.3974" of this regression line is set as the coefficient α. At this time, if the beat rate of the living body 8 is 1 beat, even if there is a change in blood oxygen concentration, the correlation may not decrease. Therefore, it is desirable to calculate the regression line from the values of the IR light signal and the red light signal in a period of two or more beats of the living body 8 .

Further, the correction unit 31 calculates the coefficient α when the value (for example, determination coefficient R ² ) representing the degree of correlation between the IR light signal and the red light signal is equal to or greater than a predetermined value (for example, 0.9). You may do so.

FIG. 30 is a diagram showing an example of a preparation period, a breath-holding period, and a follow-up period in LFCT measurement.

In FIG. 30, the coefficient α is calculated from the regression line data before the start of LFCT measurement, that is, before the start of breath-holding. During the period before the start of breath-holding, the change in blood oxygen concentration is relatively small and the correlation between the IR light signal and the red light signal is high. In subjects with a relatively short LFCT, a drop in blood oxygen levels begins to appear early after breath-holding. For example, in a subject with an LFCT of 10 seconds, a change in blood oxygen concentration appears about 10 seconds after breath-holding. . In contrast, before the start of breath-holding, no significant change in blood oxygen concentration appears. Therefore, the coefficient α is calculated from the data of the regression line in the period before the start of breath-holding.

31(A), 31(B), and 32 are diagrams for explaining the correlation between the IR light signal and the red light signal. FIG. 31(A) shows time-series data of the IR light signal and the red light signal, and FIG. 31(B) shows the LFCT of the pulse wave difference β(t). FIG. 32 is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage in each period of the time-series data of the IR light signal and the red light signal shown in FIG. 31(A).

In FIG. 31(A), the IR light signal and the red light signal in region (1) have a coefficient of determination ^R2 of 0.9987, as shown in the upper diagram of FIG. 32, indicating a relatively high correlation. . In this case, the change in blood oxygen concentration is small and the correlation of the regression line is high, so the coefficient α may be determined in region (1). This eliminates the error in the coefficient α due to changes in the blood oxygen concentration.

Similarly, in FIG. 31(A), the IR light signal and the red light signal in region (2) have a coefficient of determination ^R2 of 0.9876, as shown in the middle diagram of FIG. I understand. Region (2) is the region with changes in blood volume. In this case, the change in blood oxygen concentration is small and the correlation of the regression line is high, so the coefficient α may be determined in region (2). This eliminates the error in the coefficient α due to changes in the blood oxygen concentration.

On the other hand, in FIG. 31A, the IR light signal and the red light signal in region (3) have a coefficient of determination ^R2 of 0.8069, as shown in the lower diagram of FIG. 32, indicating a relatively low correlation. I understand. Region (3) is the region near the LFCT peak. For example, it is desirable to determine the coefficient α before starting measurement (before the preparation period). In this case, it is possible to observe changes in oxygen concentration in real time based on the determined coefficient α. Specifically, the coefficient α is determined when the change in oxygen concentration is small, that is, when the correlation is high. If the coefficient of determination is a correlation such as regions (1) and (2), the coefficient α may be determined, but a correlation such as region (3) does not determine the coefficient α.

Further, the correction unit 31 divides the regression line into systole and diastole, and calculates the coefficient α when the difference in slope of each regression line is within a predetermined range (for example, 20%). can be

FIG. 33(A) is a graph showing time-series data of the IR light signal and the red light signal during systole and diastole. FIG. 33B is a scatter diagram showing the correlation between the IR light output voltage and the red light output voltage during systole and diastole. The data in FIG. 33(B) show data for one beat when the blood oxygen concentration is changing. In this case, there is a difference in slope of the regression line between systole and diastole, and the correlation is low as a whole. The systole is the period when the heart contracts to pump out blood and the blood pressure rises, and the diastole is the period when the heart expands and the blood that has circulated throughout the body returns to the heart and the blood pressure drops. .

As shown in FIG. 33(B), when the blood oxygen concentration changes, a difference occurs between the slope of the regression line during systole and the slope of the regression line during diastole. In other words, when the difference between the slope of the regression line during systole and the slope of the regression line during diastole is small, the blood oxygen concentration does not change, resulting in a high correlation. change, so the correlation is low.

FIG. 34A is a scatter diagram showing an example of regression lines for each of systole and diastole when there is no change in blood oxygen concentration. FIG. 34B is a scatter diagram showing an example of regression lines for each of systole and diastole when there is a change in blood oxygen concentration.

The scatter diagram of FIG. 34(A) shows an example in which the blood oxygen concentration does not change, and the slope of the regression line during systole and the slope of the regression line during diastole approximately match. The regression line in systole is expressed as y=0.6994x+0.4318, and the regression line in diastole is expressed as y=0.6665x+0.4574. On the other hand, the scatter diagram of FIG. 34(B) shows an example in which the blood oxygen concentration changes, and the slope of the regression line during systole differs from the slope of the regression line during diastole. The regression line in systole is expressed as y=0.8266x+0.263, and the regression line in diastole is expressed as y=0.5797x+0.487. Therefore, as described above, the coefficient α is calculated when the difference in slope of each regression line is within a predetermined range (for example, 20%). This eliminates the error in the coefficient α due to changes in the blood oxygen concentration.

Further, the correction unit 31 may calculate the coefficient α when the LF/HF ratio, which is an index indicating the tension state of the living body 8, is equal to or less than a threshold value (eg, 4.0).

When the living body 8 is in a tense state, the sympathetic nerves act, causing the heart to beat faster and the peripheral blood vessels to constrict, which may change the blood circulation state and affect the LFCT measurement. Therefore, it is desirable to measure in a resting state.

An example of an index for knowing the state of tension is the LF/HF ratio, which is the integral ratio of the pulse wave's low-frequency component (mainly caused by Meyer waves) and high-frequency component (mainly caused by respiration). . For example, the coefficient α may be determined when the value of the LF/HF ratio is 4.0 or less. By using this index, it is possible to prevent measurement in a state of hyperactive nerves.

In the above, when the correlation between the IR light signal and the red light signal (correlation of the pulse wave), the difference in slope of the regression line in each of systole and diastole, and the LF/HF ratio are satisfied, the coefficient Although the case of determining α has been described, the coefficient α may be determined when all these conditions are satisfied.

Thus, according to this embodiment, even if the pulsation is small, a stable coefficient α can be obtained from many data. Also, since the coefficient α is obtained from the pulse wave data, a stable coefficient α can be obtained even for a short period of time. Further, when determining the coefficient α, a stable coefficient α can be obtained by using the correlation of the pulse wave, the difference in slope of the regression line between the systole and the diastole, and the LF/HF ratio.

In each of the above-described embodiments, the processor refers to a processor in a broad sense, and includes general-purpose processors (eg, CPU: Central Processing Unit, etc.) and dedicated processors (eg, GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

Further, the operations of the processors in each of the above embodiments may be performed not only by one processor but also by cooperation of a plurality of physically separated processors. Also, the order of each operation of the processor is not limited to the order described in each of the above embodiments, and may be changed as appropriate.

The biological information measuring device according to the embodiment has been described above as an example. The embodiment may be in the form of a program for causing a computer to execute the function of each unit provided in the biological information measuring device. Embodiments may be in the form of a computer-readable non-transitory storage medium storing these programs.

In addition, the configuration of the biological information measuring apparatus described in the above embodiment is merely an example, and may be changed according to circumstances without departing from the scope of the invention.

Further, the flow of processing of the program described in the above embodiment is also an example, and unnecessary steps may be deleted, new steps added, or the processing order changed without departing from the scope of the invention. good.

Further, in the above embodiment, a case has been described in which the processing according to the embodiment is realized by a software configuration using a computer by executing a program, but the present invention is not limited to this. Embodiments may be implemented by, for example, a hardware configuration or a combination of hardware and software configurations.

1 Light-emitting element 3 Light-receiving element 4 Artery 5 Vein 6 Capillary vessel 7 Blood cell 8 Living body 10 Biological information measuring device 12 Light emission control unit 14 Drive circuit 16 Amplifier circuit 18 A/D conversion circuit 20 Control unit 20A CPU
20B ROM
20C RAM
22 display unit 30 acquisition unit 31 correction unit 32 calculation unit 33 determination unit 34 detection unit 35 identification unit 36 estimation unit

Claims (13)

with a processor
The processor
Acquiring a first signal representing a change in the amount of light of a first wavelength detected from a living body and a second signal representing a change in the amount of light of a second wavelength detected from the living body,
A difference between the amount of change in the first signal and the amount of change in the second signal, which accompanies the change in the arterial blood volume of the living body, with respect to the value of the first signal or the value of the second signal. correcting either the value of the first signal or the value of the second signal by multiplying a coefficient such that
calculating a waveform pattern representing a change in blood oxygen concentration in the living body, which is expressed as a difference between the value of the first signal and the value of the second signal, one of which is corrected by the coefficient;
The biological information measuring apparatus determines that the waveform pattern is appropriate when a value representing a degree of correlation between the first signal and the second signal is less than a threshold. The processor detects an inflection point of the blood oxygen concentration accompanying a change in the amount of inspired oxygen of the living body from the waveform pattern determined to be appropriate,
The biological information measuring device according to claim 1, wherein the time from the time when the intake oxygen amount of the living body changes to the inflection point of the detected blood oxygen concentration is specified. 3. The biological information measuring apparatus according to claim 1, wherein the processor determines that the waveform pattern is inappropriate and issues a warning prompting remeasurement when the value representing the degree of correlation is equal to or greater than the threshold. . 4. The biological information measuring device according to claim 3, wherein the warning includes a message that prompts the user to breathe out sufficiently and then hold his or her breath to perform the measurement again. The value representing the degree of correlation is calculated from the first signal and the second signal at a predetermined time after the amount of inspired oxygen in the living body is changed. 2. The biological information measuring device according to item 1. The biological information measuring device according to claim 5, wherein the predetermined time is a period of two or more beats of the living body. The coefficient is represented by an amplitude ratio between the amplitude of the first signal and the amplitude of the second signal before changing the amount of inspired oxygen in the living body,
The correction is performed by multiplying the value of the first signal or the value of the second signal after changing the inspiratory oxygen amount of the living body by the coefficient. 1. The biological information measuring device according to 1. The biological information measuring device according to claim 1, wherein the processor calculates the coefficient from a slope of a regression line obtained from the value of the first signal and the value of the second signal. The biological information measuring device according to claim 8, wherein the regression line is obtained from the values of the first signal and the values of the second signal in a period of two or more beats of the living body. 10. The biological information measuring device according to claim 8, wherein the processor calculates the coefficient when a value representing the degree of correlation between the first signal and the second signal is equal to or greater than a predetermined value. . 11. Any one of claims 8 to 10, wherein the processor divides the regression line into systole and diastole and calculates the coefficient when a difference in slope of each regression line is within a predetermined range. 1. The biological information measuring device according to 1. The biological information measuring device according to any one of claims 8 to 11, wherein the processor calculates the coefficient when the LF/HF ratio, which is an index indicating the tension state of the living body, is equal to or less than a threshold. . Acquiring a first signal representing a change in the amount of light of a first wavelength detected from a living body and a second signal representing a change in the amount of light of a second wavelength detected from the living body,
A difference between the amount of change in the first signal and the amount of change in the second signal, which accompanies the change in the arterial blood volume of the living body, with respect to the value of the first signal or the value of the second signal. correcting either the value of the first signal or the value of the second signal by multiplying a coefficient such that
calculating a waveform pattern representing a change in blood oxygen concentration in the living body, which is expressed as a difference between the value of the first signal and the value of the second signal, one of which is corrected by the coefficient;
Determining that the waveform pattern is appropriate when a value representing the degree of correlation between the first signal and the second signal is less than a threshold;
A biological information measurement program to be executed by a computer.

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