Biological information measuring device and biological information measuring program

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

[Technical Field]

[0001] The present invention relates to a biological information measuring device and a biological information measuring program. [Background technology]

[0002] For example, Patent Document 1 describes a method for measuring the circulation time of oxygen transport, which calculates changes in oxygen saturation based on an absorbance signal of arterial blood extracted from a living body using a sensor. This method varies the amount of oxygen inhaled into the living body, sets the point at which this change occurs as a reference point, and measures the time from the reference point until the oxygen saturation of the arterial blood changes.

[0003] Patent Document 2 also describes a biological information measuring device capable of measuring changes in blood oxygen concentration. This biological information measuring device includes: a corrector that receives 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 corrects at least one of the first signal and the second signal so as to reduce a difference between an amount of change in the first signal and an amount of change in the second signal that accompanies a change in the arterial blood volume of the living body; and a calculator that calculates a change in blood oxygen concentration in the living body based on the first signal and the second signal, at least one of which has been corrected by the corrector. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-231012 [Patent Document 2] Japanese Patent Application Publication No. 2019-166144 Summary of the Invention [Problem to be solved by the invention]

[0005] The simplest way to change a subject's blood oxygen level is to have them hold their breath. However, due to resistance to holding their breath, they may end up holding their breath with a lot of air in their lungs. In this case, the waveform pattern representing the change in blood oxygen level becomes an inappropriate pattern, where the inflection points of the waveform pattern caused by the change in blood oxygen level cannot be identified.

[0006] In the existing technology, in order to determine whether the waveform pattern is appropriate, The algorithm checks whether there are multiple inappropriate patterns where the inflection points of the waveform patterns caused by complex changes in blood oxygen levels cannot be identified. This requires a complex algorithm and places a heavy burden on the judgment process, so a simpler judgment method is desired.

[0007] The present disclosure provides: Complex The object of the present invention is to provide a biological information measuring device and a biological information measuring program that can easily determine whether a waveform pattern is inappropriate, compared to when an algorithm is used to determine an appropriate waveform pattern. [Means for solving the problem]

[0008] In order to achieve the above-mentioned object, a biological information measuring device according to a first aspect includes a processor, which acquires a first signal representing a change in the amount of light of a first wavelength detected from a living organism and a second signal representing a change in the amount of light of a second wavelength detected from the living organism, corrects either the value of the first signal or the value of the second signal by multiplying the value of the first signal or the value of the second signal 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 a change in the arterial blood volume of the living organism is small, calculates a waveform pattern representing a change in blood oxygen concentration in the living organism, expressed as the difference between the value of the first signal, one of which has been corrected by the coefficient, and the value of the second signal, and determines that the waveform pattern is appropriate if a value representing the degree of correlation between the first signal and the second signal is less than a threshold value.

[0009] In addition, the second aspect of the bioinformation measuring device is the bioinformation measuring device of the first aspect, in which the processor detects an inflection point in the blood oxygen concentration associated with a change in the amount of inhaled oxygen of the living body from the waveform pattern determined to be appropriate, and determines the time from the point at which the amount of inhaled oxygen of the living body changes to the detected inflection point in the blood oxygen concentration.

[0010] In addition, in the biological information measuring device of the third aspect, in the biological information measuring device of the first or second aspect, when the value representing the degree of correlation is equal to or greater than the threshold value, the processor determines that the waveform pattern is inappropriate and issues a warning urging remeasurement.

[0011] Furthermore, in the biological information measurement device according to a fourth aspect, in the biological information measurement device according to the third aspect, the warning includes a message urging the user to exhale sufficiently, hold their breath, and then measure again.

[0012] In addition, a biological information measuring device according to a fifth aspect is a biological information measuring device according to any one of the first to fourth aspects, in which a value representing the degree of correlation is calculated from the first signal and the second signal at a predetermined time after the amount of inhaled oxygen of the living body is changed.

[0013] A biological information measurement device according to a sixth aspect is the biological information measurement device according to the fifth aspect, wherein the predetermined time is a period of two or more beats of the living body.

[0014] In addition, a biological information measuring device according to a seventh aspect is a biological information measuring device according to any one of the first to sixth aspects, wherein the coefficient is expressed as 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, and the correction is performed by multiplying the coefficient by the value of the first signal or the value of the second signal after changing the amount of inspired oxygen in the living body.

[0015] In addition, the biological information measuring device of the eighth aspect is the biological information measuring device of the first aspect, wherein the processor calculates the coefficient from the slope of a regression line obtained from the values ​​of the first signal and the values ​​of the second signal.

[0016] In addition, a biological information measuring device according to a ninth aspect is the biological information measuring device according to the eighth aspect, in which the regression line is obtained from the values ​​of the first signal and the second signal over a period of two or more beats of the living body.

[0017] In addition, a biological information measuring device according to a 10th aspect is a biological information measuring device according to the 8th or 9th aspect, in which the processor calculates the coefficient when a value representing the degree of correlation between the first signal and the second signal is greater than or equal to a predetermined value.

[0018] In addition, a biological information measuring device according to an eleventh aspect is a 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, and calculates the coefficient when the difference in slope of each regression line is within a predetermined range.

[0019] In addition, a biological information measuring device according to a 12th aspect is a biological information measuring device according to any one of the 8th to 11th aspects, wherein the processor calculates the coefficient when the LF / HF ratio, which is an index indicating the state of tension of the living body, is below a threshold value.

[0020] Furthermore, in order to achieve the above-mentioned object, a biological information measurement program according to a thirteenth aspect causes a computer to acquire a first signal representing a change in the amount of light of a first wavelength detected from a living organism and a second signal representing a change in the amount of light of a second wavelength detected from the living organism, correct either the value of the first signal or the value of the second signal by multiplying the value of the first signal or the value of the second signal 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 a change in the arterial blood volume of the living organism is small, calculate a waveform pattern representing a change in blood oxygen concentration in the living organism, expressed as the difference between the value of the first signal, one of which has been corrected by the coefficient, and the value of the second signal, and determine that the waveform pattern is appropriate if a value representing the degree of correlation between the first signal and the second signal is less than a threshold value. [Effects of the Invention]

[0021] According to the first and thirteenth aspects, Complex This has the advantage that it is possible to more easily determine whether a waveform pattern is inappropriate, compared to when an algorithm is used to determine an appropriate waveform pattern.

[0022] According to the second aspect, there is an effect that the time to the inflection point of the blood oxygen concentration can be determined with high accuracy compared to when the difference between the plurality of corrected signals is not used.

[0023] According to the third aspect, there is an effect that it is possible to prompt the user to perform remeasurement when the waveform pattern is inappropriate.

[0024] According to the fourth aspect, there is an effect that it is possible to encourage the person to hold their breath after fully exhaling.

[0025] According to the fifth aspect, there is an effect that the value representing the degree of correlation can be calculated with high accuracy compared to a case where the predetermined time after the intake oxygen amount is changed is not taken into consideration.

[0026] According to the sixth aspect, there is an advantage that the value representing the degree of correlation can be calculated with higher accuracy compared to the case where the period of one beat after the amount of inspired oxygen is changed is used.

[0027] According to the seventh aspect, there is an effect that a coefficient with higher accuracy can be obtained compared to the case where the coefficient is obtained after the amount of oxygen inhaled by the living body is changed.

[0028] According to the eighth aspect, there is an effect that the coefficient can be obtained more easily than when the slope of the regression line is not taken into consideration.

[0029] According to the ninth aspect, there is an effect that a highly accurate regression line can be obtained compared to when the period is one beat of the living body.

[0030] According to the tenth aspect, there is an effect that a stable coefficient can be obtained compared to when the value representing the degree of correlation is less than a predetermined value.

[0031] According to the eleventh aspect, there is an effect that a stable coefficient can be obtained compared to a case where the difference in the slope of the regression line is outside a predetermined range.

[0032] According to the twelfth aspect, there is an effect that a stable coefficient can be obtained compared to when the LF / HF ratio is larger than the threshold value. [Brief explanation of the drawings]

[0033] [Figure 1] 10A and 10B are schematic diagrams showing an example of measurement of blood flow information and blood oxygen saturation according to an embodiment. [Figure 2] 10 is a graph showing an example of a change in the amount of received light due to reflected light from a living body according to the embodiment. [Figure 3] 10A and 10B are schematic diagrams illustrating a Doppler shift that occurs when a blood vessel is irradiated with laser light according to an embodiment. [Figure 4] 1A and 1B are schematic diagrams illustrating speckles that occur when a blood vessel is irradiated with laser light according to an embodiment. [Figure 5] 10 is a graph illustrating an example of a spectrum distribution for each frequency per unit time according to the embodiment. [Figure 6] 10 is a graph showing an example of a change in blood flow rate per unit time according to the embodiment. [Figure 7] 10 is a graph showing an example of a change in the amount of light absorbed by a living body according to the embodiment. [Figure 8] 1 is a graph showing an example of absorbance characteristics of hemoglobin according to an embodiment. [Figure 9] 1 is a schematic diagram illustrating a principle of measuring a respiratory waveform according to an embodiment. FIG. [Figure 10] FIG. 2 is a schematic diagram illustrating the principle of measuring stroke volume according to the embodiment. [Figure 11] 10 is a graph for explaining an example of a method for measuring LFCT according to an embodiment. [Figure 12] FIG. 2 is a block diagram showing an example of the electrical configuration of the biological information measuring device according to the embodiment. [Figure 13] 3 is a diagram showing an example of the arrangement of light emitting elements and light receiving elements in the biological information measuring device according to the embodiment; FIG. [Figure 14] 10A and 10B are diagrams illustrating another example of the arrangement of light-emitting elements and light-receiving elements in the biological information measuring device according to the embodiment. [Figure 15] 10 is a graph showing an example of data sampling timing in a light receiving element according to the embodiment. [Figure 16] 1 is a block diagram showing an example of the functional configuration of a biological information measuring device according to a first embodiment. [Figure 17] Scatter plots (A) and (B) show an example of the correlation between IR light output voltage and red light output voltage when there is a change in blood oxygen concentration, and when there is no change in blood oxygen concentration. [Figure 18] 5 is a flowchart showing an example of the processing flow of a biological information measurement program according to the first embodiment. [Figure 19]10 is a graph showing an example of the amplitude of an IR light signal and the amplitude of a red light signal according to an embodiment. [Figure 20] 10 is a graph showing an example of the relationship between a coefficient and a pulse wave difference according to the embodiment. [Figure 21] 10 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 an embodiment. [Figure 22] 10 is a graph showing an example of time series data of an IR light signal and time series data of a red light signal after correction according to an embodiment. [Figure 23] 10 is a graph showing an example of a monitoring result based on a difference in pulse waves according to the embodiment. [Figure 24] 10 is a graph showing an example of an LFCT determined from a pulse wave difference according to the embodiment. [Figure 25] Graph (A) shows time-series data of IR light signals and red light signals relating to a first inappropriate pattern. Graph (B) shows a first inappropriate pattern of pulse wave difference β(t). Graph (C) shows a scatter plot showing the correlation between IR light output voltage and red light output voltage for the first inappropriate pattern. [Figure 26] Graph (A) shows time-series data of IR light signals and red light signals relating to a second inappropriate pattern. Graph (B) shows a second inappropriate pattern of pulse wave difference β(t). Graph (C) shows a scatter plot showing the correlation between IR light output voltage and red light output voltage for the second inappropriate pattern. [Figure 27] (A) is a graph showing time series data of IR light signals and red light signals relating to the third inappropriate pattern. (B) is a graph showing the third inappropriate pattern of pulse wave difference β(t). (C) is a scatter plot showing the correlation between IR light output voltage and red light output voltage for the third inappropriate pattern. [Figure 28] Graph (A) shows time-series data of IR light signal and red light signal for the fourth inappropriate pattern. Graph (B) shows the fourth inappropriate pattern of pulse wave difference β(t). Graph (C) shows a scatter plot showing the correlation between IR light output voltage and red light output voltage for the fourth inappropriate pattern. [Figure 29]FIG. 10 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. [Figure 30] FIG. 10 is a diagram showing an example of a preparation period, a breath-holding period, and a follow-up period in LFCT measurement. [Figure 31] 10A and 10B are diagrams illustrating the correlation between an IR light signal and a red light signal. [Figure 32] FIG. 10 is a diagram illustrating the correlation between an IR light signal and a red light signal. [Figure 33] (A) is a graph showing time series data of IR light signal and red light signal during systole and diastole. (B) is a scatter plot showing the correlation between IR light output voltage and red light output voltage during systole and diastole. [Figure 34] (A) is a scatter plot showing an example of the regression lines for the systolic and diastolic periods when there is no change in blood oxygen concentration, and (B) is a scatter plot showing an example of the regression lines for the systolic and diastolic periods when there is a change in blood oxygen concentration. DETAILED DESCRIPTION OF THE INVENTION

[0034] An example of an embodiment of the technology of the present disclosure will be described in detail below with reference to the drawings. Note that components and processes that perform the same operations, actions, and functions are given the same reference numerals throughout the drawings, and duplicated descriptions may be omitted as appropriate. Each drawing is merely a schematic illustration to allow a sufficient understanding of the technology of the present disclosure. Therefore, the technology of the present disclosure is not limited to the illustrated examples. Furthermore, in this embodiment, descriptions of configurations that are not directly related to the present invention or well-known configurations may be omitted.

[0035] [First embodiment] First, with reference to FIG. 1, a method for measuring blood flow information and blood oxygen saturation, which are examples of biological information relating to blood, will be described.

[0036] FIG. 1 is a schematic diagram showing an example of measurement of blood flow information and blood oxygen saturation according to this embodiment. As shown in Figure 1, blood flow information and blood oxygen saturation are measured by irradiating light from a light-emitting element 1 onto the subject's body (living body 8), receiving the light from a light-receiving element 3, and measuring the intensity of the light reflected or transmitted by arteries 4, veins 5, capillaries 6, etc. that are distributed throughout the body of the living body 8, i.e., the amount of reflected or transmitted light received.

[0037] (Blood flow information measurement) FIG. 2 is a graph showing an example of a change in the amount of received light due to reflected light from a 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.

[0038] As shown in FIG. 2, the amount of light received by the light receiving element 3 changes over time, which is thought to be due to the influence of three optical phenomena that occur when light is irradiated onto a living body 8 including blood vessels.

[0039] The first optical phenomenon is a change in light absorption caused by a change in the amount of blood present in the blood vessel being measured due to pulsation. Blood contains blood cells such as red blood cells, which move within blood vessels such as capillaries 6. As the blood volume changes, the number of blood cells moving within the blood vessels also changes, which can affect the amount of light received by the light-receiving element 3.

[0040] The second optical phenomenon is the effect of the Doppler shift.

[0041] FIG. 3 is a schematic diagram illustrating the Doppler shift that occurs when a blood vessel is irradiated with laser light according to this embodiment.

[0042] As shown in FIG. 3, when coherent light 40, such as laser light, having a frequency ω0 is irradiated from a light-emitting element 1 onto an area including capillaries 6, an example of a blood vessel, scattered light 42 from blood cells moving through the capillaries 6 exhibits a Doppler shift with a difference frequency Δω0 determined by the velocity of the blood cells. Meanwhile, the frequency of scattered light 42 from tissue (stationary tissue) such as skin, which does not contain moving bodies such as blood cells, maintains the same frequency ω0 as the frequency of the irradiated laser light. Therefore, the frequency ω0 + Δω0 of the laser light scattered by blood vessels such as capillaries 6 and the frequency ω0 of the laser light scattered by stationary tissue interfere with each other, resulting in a beat signal having a difference frequency Δω0 observed by the light-receiving element 3. The amount of light received by the light-receiving element 3 changes over time. The difference frequency Δω0 of the beat signal observed by the light-receiving element 3 depends on the velocity of the blood cells, but is within a range with an upper limit of approximately several tens of kHz.

[0043] A third optical phenomenon is the effect of speckles.

[0044] FIG. 4 is a schematic diagram illustrating speckles that occur when blood vessels are irradiated with laser light according to this embodiment.

[0045] As shown in Figure 4, when coherent light 40 such as laser light is irradiated from a light-emitting element 1 onto a blood cell 7 such as a red blood cell moving in a blood vessel in the direction of arrow 44, the laser light that hits the blood cell 7 is scattered in various directions. The scattered light rays have different phases and therefore interfere with each other randomly. This results in a random speckled light intensity distribution. The light intensity distribution pattern formed in this way is called a "speckle pattern."

[0046] As already explained, as blood cells 7 move through blood vessels, the scattering state of light in blood cells 7 changes, causing the speckle pattern to fluctuate over time. Therefore, the amount of light received by light-receiving element 3 changes over time.

[0047] 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 as shown in Figure 2 is obtained, data included within a predetermined unit time T0 is extracted and, for example, a Fast Fourier Transform (FFT) is performed on the extracted data to obtain a spectral distribution for each frequency ω.

[0048] 5 is a graph showing an example of a spectral distribution for each frequency ω in unit time T0 according to this embodiment. In FIG. 5, the horizontal axis of graph 82 represents frequency ω, and the vertical axis represents spectral intensity.

[0049] 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 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 graph 82, it is proportional to the value obtained by integrating the product of the power spectrum at frequency ω and frequency ω with respect to frequency ω, divided by the area of ​​shaded area 84.

[0050] Since the blood flow rate is expressed as the product of the blood volume and the blood flow velocity, it can be calculated using the above formula for calculating the blood volume and the blood flow velocity. The blood flow rate, blood flow velocity, and blood volume are examples of blood flow information, and blood flow information is not limited to these.

[0051] 6 is a graph showing an example of a change in blood flow per unit time T0 according to this embodiment. In FIG. 6, the horizontal axis of graph 86 represents time, and the vertical axis represents blood flow.

[0052] As shown in Figure 6, blood flow fluctuates over time, but the trends of this fluctuation can be classified into two types. For example, compared to the fluctuation range 88 of blood flow in section T1 in Figure 6, the fluctuation range 90 of blood flow in section T2 is larger. This is thought to be because the changes in blood flow in section T1 are mainly due to changes in blood flow associated with pulsation, while the changes in blood flow in section T2 indicate changes in blood flow associated with causes such as congestion or nerve activity.

[0053] (Oxygen saturation measurement) Next, we will explain how to measure blood oxygen saturation. Blood oxygen saturation is an example of blood oxygen concentration and is an index that shows the degree to which hemoglobin in the blood is bound to oxygen. As blood oxygen saturation decreases, symptoms such as anemia become more likely to occur.

[0054] Fig. 7 is a graph showing an example of a change 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 absorbed.

[0055] As shown in FIG. 7, the amount of light absorbed by the living body 8 tends to fluctuate over time.

[0056] Furthermore, looking at the breakdown of the variation in light absorption in the living body 8, it is known that the amount of light absorption varies mainly in the arteries 4, while in the veins 5 and other tissues including stationary tissues, the amount of variation is such that the amount of light absorption can be considered to be unchanged compared to the arteries 4. This is because arterial blood pumped from the heart moves within the blood vessels accompanied by a pulse wave, causing the arteries 4 to expand and contract over time along the cross-sectional direction of the arteries 4, resulting in changes in the thickness of the arteries 4. In addition, in FIG. 7, the range indicated by arrow 94 indicates the amount of variation in light absorption corresponding to changes in the thickness of the arteries 4.

[0057] In Figure 7, time t a The amount of light received at I a , time t b The amount of light received at I b Then, the amount of change ΔA in the amount of light absorbed due to a change in the thickness of the artery 4 is expressed by equation (1).

[0058] (Number 1) ΔA=ln(I b / I a )···(1)

[0059] FIG. 8 is a graph showing an example of the absorbance characteristics of hemoglobin according to this embodiment. In FIG. 8, the vertical axis represents absorbance and the horizontal axis represents wavelength.

[0060] 8, it is known that hemoglobin bound to oxygen flowing through artery 4 (oxyhemoglobin) easily absorbs light in the infrared (IR) region, particularly having a wavelength around 880 nm, while hemoglobin not bound to oxygen (reduced hemoglobin) easily absorbs light in the red region, particularly having a wavelength around 665 nm. Furthermore, it is known that oxygen saturation is proportional to the ratio of the change ΔA in the amount of light absorbed at different wavelengths.

[0061] Therefore, compared to other wavelength combinations, infrared light (IR light) and red light, which are more likely to cause a difference in the amount of absorbance between oxygenated hemoglobin and reduced hemoglobin, are used to irradiate IR light onto a living body 8. IR and the change in the amount of absorbance ΔA when red light is irradiated onto the living body 8 Red By calculating the ratio of these values, the oxygen saturation S can be calculated using equation (2). In equation (2), k is a proportionality constant.

[0062] (Number 2) S=k(ΔA Red / ΔA IR )···(2)

[0063] That is, when calculating the oxygen saturation level in the blood, a plurality of light-emitting elements 1 each emitting light of a different wavelength, specifically a light-emitting element 1 emitting IR light and a light-emitting element 1 emitting red light, may emit light during periods that overlap, but preferably the light-emitting periods do not overlap. Then, the light reflected or transmitted by each light-emitting element 1 is received by the light-receiving element 3, and the oxygen saturation level is measured by calculating equations (1) and (2) or known equations obtained by modifying these equations from the amount of light received at each light-receiving point.

[0064] As a known formula obtained by modifying the above formula (1), for example, the amount of change ΔA in the amount of light absorbed may be expressed as formula (3) by expanding formula (1).

[0065] (Number 3) ΔA=lnI b -lnI a ···(3)

[0066] Moreover, equation (1) can be transformed into equation (4).

[0067] (Number 4) ΔA=ln(I b / I a )=ln(1+(I b -I a ) / I a ) ···(4)

[0068] Usually, (I b -I a )≪I a Therefore, ln(I b / I a )≒(I b -I a ) / I a Therefore, instead of equation (1), equation (5) may be used as the change ΔA in the amount of light absorbed.

[0069] (Number 5) ΔA≒(I b -I a ) / I a ···(5)

[0070] 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 will be referred to as the "light-emitting element LD1," and the light-emitting element 1 that emits red light will be referred to as the "light-emitting element LD2." As an example, the light-emitting element LD1 will be referred to as the light-emitting element 1 used in calculating blood flow, and the light-emitting elements LD1 and LD2 will be referred to as the light-emitting elements 1 used in calculating blood oxygen saturation.

[0071] Furthermore, since it is known that a measurement frequency of the amount of received light of approximately 30 Hz to 1000 Hz is sufficient when measuring blood oxygen saturation, the light emission frequency, which represents the number of times the light-emitting element LD2 blinks per second, is also sufficient if it is approximately 30 Hz to 1000 Hz. Therefore, from the viewpoint of power consumption and the like in the light-emitting element LD2, it is preferable to set the light emission frequency of the light-emitting element LD2 lower than the light emission frequency of the light-emitting element LD1, but it is also possible to match the light emission frequency of the light-emitting element LD2 to the light emission frequency of the light-emitting element LD1 and cause the light-emitting elements LD1 and LD2 to emit light alternately.

[0072] Next, the principle of measuring a respiratory waveform from a pulse wave signal obtained from a peripheral part of the living body 8 will be described with reference to Fig. 9. Examples of peripheral parts here include fingertips, toes, earlobes, etc. Peripheral parts also include parts beyond the elbow and parts beyond the knee. The 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 the time change of exhalation and inhalation.

[0073] 9 is a schematic diagram illustrating 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 through the following steps. (S1) Intrathoracic pressure decreases and becomes negative, causing the lungs to expand. (S2) Venous return increases. (S3) The amount of blood flowing into the right atrium increases. (S4) The pulmonary vascular bed expands, increasing the amount of blood the lungs can hold. (S5) The amount of blood returning from the lungs to the left atrium decreases. (S6) Left ventricular stroke volume decreases. (S7) The amplitude of the pulse wave signal decreases.

[0074] 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.

[0075] In other words, since the influence of the "pumping action of the lungs" caused by breathing is superimposed on the pulsation caused by the "pumping action of the heart," it is possible to measure the respiratory waveform from a pulse wave signal obtained from a peripheral part of the living body 8.

[0076] 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 cardiac blood output, will be described. The stroke volume referred to here is not limited to the cardiac output described above, but also includes stroke volume, cardiac index, etc. Cardiac output is defined as the amount of blood pumped into an artery by contraction of the heart per unit time (e.g., one minute). Stroke volume is defined as the amount of blood pumped into an artery by one contraction of the heart. Cardiac index is defined as a 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 through breathing to pass through the lungs and heart and reach the fingertip.

[0077] Fig. 10 is a schematic diagram illustrating 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 the cardiac output, which is an example of the stroke volume, is CO, the cardiac output CO is calculated by the following equation (6).

[0078] (Number 6) CO = (a0 × S) / LFCT (6) Here, a0 is a constant, for example, a0 = 50. S is the subject's body surface area (m 2 ), and the unit of LFCT is seconds.

[0079] 11 is a graph for explaining an example of the LFCT measurement method according to this embodiment, in which the vertical axis represents the reciprocal of oxygen saturation and the horizontal axis represents time.

[0080] As shown in Figure 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 resumes after a certain period of cessation to the inflection point indicating the recovery of oxygen saturation.

[0081] As mentioned above, the simplest method for changing a subject's blood oxygen level is to have the subject hold their breath. However, due to resistance to holding their breath, subjects may end up holding their breath with a large amount of air in their lungs. In this case, the waveform pattern representing the change in blood oxygen level becomes an inappropriate pattern, in which the inflection points caused by the change in blood oxygen level cannot be identified. Existing technologies require complex algorithms to determine whether the waveform pattern is appropriate, which places a heavy burden on the determination process. Therefore, a simple determination method is desired.

[0082] The present disclosure describes a biological information measurement device that can easily determine whether a waveform pattern is inappropriate, compared to when an appropriate waveform pattern is determined using an existing algorithm.

[0083] In this embodiment, when there is no change in blood oxygen concentration, only the change in output is due to pulsation (blood volume), and the correlation between the pulse waves of the two measured wavelengths (e.g., IR light signal, red light signal) is high, but when there is a change in blood oxygen concentration, the correlation becomes low. In other words, when the correlation between the two pulse waves is high, the waveform pattern representing the change in blood oxygen concentration is determined to be inappropriate, and when the correlation is low, the waveform pattern representing the change in blood oxygen concentration is determined to be appropriate.

[0084] FIG. 12 is a block diagram showing an example of the electrical configuration of the biological information measurement device 10 according to this embodiment.

[0085] As shown in FIG. 12 , the biological information measuring device 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 unit. The light-emission control unit 12, the drive circuit 14, the amplifier circuit 16, the A / D conversion circuit 18, the control unit 20, and the display unit 22 constitute a main body unit. In this embodiment, the sensor unit and the main body unit are configured separately and are capable of communicating with each other via wire or wirelessly. The sensor unit and the main body unit may also be configured integrally. The sensor unit is attached in close contact with the living body 8 to prevent external light from entering. The sensor unit according to this embodiment is attached to the fingertip of the living body 8, as an example, but can also be attached to other peripheral parts such as the earlobe.

[0086] 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 a drive circuit 14 including a power supply circuit for supplying drive power to the light-emitting elements LD1 and LD2. The light-emission control unit 12 may be realized as a part of the control unit 20.

[0087] Upon receiving a control signal from the light emission control unit 12, the drive circuit 14 supplies drive power to the light emitting elements LD1 and LD2 in accordance with the light emission cycle and light emission period instructed by the control signal, thereby driving the light emitting elements LD1 and LD2.

[0088] The light receiving element 3 receives light of a first wavelength from the light emitting element LD1 and outputs a first light receiving signal corresponding to the received light of the first wavelength, and receives light of a second wavelength from the light emitting element LD2 and outputs a second light receiving signal corresponding to the received light of the second wavelength. In this embodiment, the first wavelength is a wavelength range corresponding to the infrared region, and the second wavelength is a wavelength range corresponding to the red region. An IR light signal is used as the first light receiving signal, and a red light signal is used as the second light receiving signal.

[0089] The amplifier circuit 16 converts a current corresponding to the intensity of light generated by the light receiving element 3 into a voltage, and amplifies the voltage to a level defined as the input voltage range of the A / D conversion circuit 18 .

[0090] The A / D conversion circuit 18 receives the voltage amplified by the amplifier circuit 16 as an input, digitizes the amount of light received by the light receiving element 3, which is represented by the magnitude of the voltage, and outputs the digitized amount.

[0091] 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 appropriately in the biological information measurement device 10. Examples of non-volatile storage media include a CD-ROM (Compact Disc Read Only Memory), a magneto-optical disk, a HDD, a DVD-ROM (Digital Versatile Disc Read Only Memory), a flash memory, a memory card, etc.

[0092] The display unit 22 displays the measurement results of the biological information. For example, a liquid crystal display (LCD) or an organic electroluminescence (EL) display is used as the display unit 22. The display unit 22 has an integrated touch panel.

[0093] Fig. 13 is a diagram showing an example of the 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 the 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.

[0094] 13, the light-emitting element LD1, the light-emitting element LD2, and the light-receiving element 3 are arranged side by side facing one surface of the living body 8. In this case, the light-receiving element 3 receives light from the light-emitting element LD1 and the light-emitting element LD2 that has passed through the vicinity of the surface of the living body 8.

[0095] 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. 13. For example, as shown in Fig. 14, the light-emitting element LD1 and the light-emitting element LD2 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 element LD1 and the light-emitting element LD2 that has passed through the living body 8.

[0096] Here, as an example, the light-emitting elements LD1 and LD2 are both described as surface-emitting laser elements, but they are not limited to this and may be edge-emitting laser elements. Furthermore, the light emitted from each of the light-emitting elements LD1 and LD2 does not have to be laser light. In this case, each of the light-emitting elements LD1 and LD2 may be a light-emitting diode (LED) or an organic light-emitting diode (OLED).

[0097] Fig. 15 is a graph showing an example of the timing of sampling data in the light receiving element 3 according to this embodiment. In Fig. 15, the positions of the circles indicate the sampling timing. In Fig. 15, the vertical axis represents the output voltage of the light receiving element 3, and the horizontal axis represents time.

[0098] As shown in FIG. 15, the output voltages corresponding to the light received by the light receiving element 3 from the light emitting element LD1 are denoted as IR1, IR2, . . . , IR n In this case, the time series data is IR(t)=IR1, IR2, . . ., IR n Similarly, the output voltages corresponding to the light received by the light receiving element 3 from the light emitting element LD2 are expressed as Red1, Red2, . . . , Red nIn this case, the time series data is Red(t)=Red1, Red2, . . ., Red n At this time, a period in which both the light emitting element LD1 and the light emitting element LD2 do not emit light is provided, and outputs in the dark state are Dark1, Dark2, . . . , Dark n In this case, IR(t) can be obtained by dividing IR1-Dark1, IR2-Dark2, . . ., IR n -Dark n Similarly, Red(t) can be expressed as Red1-Dark1, Red2-Dark2, ..., Red n -Dark n It is desirable to sample this data near the end of the light emission period when the output is stable.

[0099] The CPU 20A of the biological information measuring device 10 according to this embodiment writes a biological information measuring program stored in the ROM 20B into the RAM 20C and executes it to function as each unit shown in Fig. 16. The CPU 20A is an example of a processor.

[0100] 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.

[0101] As shown in FIG. 16, the CPU 20A of the biological information measuring device 10 according to this embodiment functions as an acquisition unit 30, a correction unit 31, a calculation unit 32, a determination unit 33, a detection unit , an identification unit 35, and an estimation unit .

[0102] The acquisition unit 30 acquires each of the IR optical signal and the red optical signal output from the light receiving element 3. In this case, the IR optical signal is an example of a first signal, and the red optical signal is an example of a second signal.

[0103] The correction unit 31 corrects the IR optical signal by multiplying the value of the IR optical signal by a coefficient so as to reduce the difference between the amount of change in the IR optical signal (hereinafter referred to as ΔIR) and the amount of change in the red optical signal (hereinafter referred to as ΔRed) that accompanies changes in the arterial blood volume of the living body 8. This change in arterial blood volume represents the amplitude of pulsation associated with the heartbeat. Note that the value of the red optical signal may also be corrected.

[0104] The above correction is preferably a correction that makes ΔIR and ΔRed equal. Here, ΔIR is expressed as the amplitude of the IR optical signal, and ΔRed is expressed as the amplitude of the red optical signal. In this case, the above correction is performed by multiplying the value of the IR optical signal (IR(t)) by a coefficient α, which is expressed as the amplitude ratio between ΔIR and ΔRed (ΔRed / ΔIR). In other words, the output after correction of IR(t) is α × IR(t).

[0105] The calculation unit 32 calculates a waveform pattern representing a change in the blood oxygen concentration in the living body 8 based on the IR light signal and the red light signal corrected by the correction unit 31. The waveform pattern representing a 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 will be referred to as the "pulse wave difference"). For example, if the pulse wave difference is β(t), β(t) can be calculated by the following equation (7).

[0106] (Number 7) β(t)=α×IR(t)-Red(t)...(7)

[0107] 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 optical signal and the red optical signal is less than a threshold value. The value representing the degree of correlation may be, for example, a coefficient of determination (=R ), which is an index representing the goodness of fit of a regression line obtained from a scatter diagram in which values ​​of the IR optical signal (e.g., voltage values) and values ​​of the red optical signal (e.g., voltage values) over a predetermined period are plotted. 2) is used. The coefficient of determination is calculated using a known method. The coefficient of determination is a value between 0 and 1, and the closer it is to 1, the higher the correlation between the IR light signal and the red light signal. The threshold value is, for example, 0.8, and more preferably 0.7. On the other hand, if the value representing the degree of correlation is equal to or greater than the threshold value, the determination unit 33 determines that the pulse wave difference β(t) is inappropriate and issues a warning to urge remeasurement. This warning includes, for example, a message urging the user to exhale sufficiently, hold their breath, and then remeasure.

[0108] Fig. 17(A) is a scatter plot 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. 17(B) is a scatter plot 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 represents the output voltage of the red light signal, and the horizontal axis represents the output voltage of the IR light signal.

[0109] According to the scatter plot in Figure 17(A), when the blood oxygen concentration changes, that is, when the oxygen saturation level decreases, 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 less than the threshold value (for example, 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 in FIG. 17(B), if the blood oxygen concentration has not changed, that is, if the oxygen saturation level has not decreased, the correlation between the IR light signal and the red light signal is high, and the coefficient of determination (in this example, R 2 = 0.9258) is equal to or greater than a threshold value (for example, 0.8). In this case, the pulse wave difference β(t) is determined to be inappropriate.

[0110] Furthermore, the value representing the degree of correlation is calculated from the IR light signal and the red light signal for a predetermined time (e.g., 30 seconds) after changing the amount of inhaled oxygen (after resumption of breathing) of the living body 8. Since it is difficult to obtain an accurate correlation for one beat, it is desirable that the predetermined time be a period of two or more beats of the living body 8.

[0111] The detection unit 34 detects an inflection point in the blood oxygen concentration associated with a change in the amount of inspired oxygen of the living body 8 from the pulse wave difference β(t) determined to be appropriate by the determination unit 33. An example of a method for changing the amount of inspired oxygen is a method such as breath-holding. The change in the amount of inspired oxygen referred to here refers to a change that affects the blood oxygen concentration over at least several seconds and does not include slight changes due to a normal breathing state (e.g., breathing at an average frequency or depth). In other words, in a normal breathing state, there is no change in the amount of inspired oxygen. However, a change in the amount of inspired oxygen is determined to have occurred when the normal breathing state is changed by, for example, stopping breathing, weakening breathing, or inhaling gas with a high oxygen concentration.

[0112] The determination unit 35 determines the time from the point when the amount of inhaled oxygen of the living body 8 changes to the inflection point of the blood oxygen concentration detected by the detection unit 34. The point when the amount of inhaled oxygen changes is, for example, the point when breathing resumes after a state of respiratory arrest. In this embodiment, the time determined by the determination unit 35 is referred to as the LFCT.

[0113] The estimation unit 36 ​​estimates the stroke volume from the LFCT identified by the identification unit 35. For example, the cardiac output, which is an example of the stroke volume, is estimated using the above equation (6).

[0114] Here, the IR light signal and the red light signal each contain a component that represents a change in blood volume due to pulsation, neural activity, etc., and a component that represents a change in oxygen concentration due to a change in the amount of oxygen inhaled. According to the pulse wave difference β(t), by multiplying IR(t) by a coefficient α (= ΔRed / ΔIR) and using the difference between α × IR(t) and Red(t), the component that represents a change in arterial blood volume is canceled out, and only the component that represents a change in oxygen concentration is extracted.

[0115] In the above, 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 calculated using the following equation (8).

[0116] (Number 8) β(t)=IR(t)-α×Red(t)...(8)

[0117] In the above, we have described a case where one of the IR light signal and the red light signal, which are examples of two pulse wave signals, is corrected, but it is also possible to correct both the IR light signal and the red light signal. Also, in the above, we have described a case where the red light signal is subtracted from the IR light signal, but it is also possible to subtract the IR light signal from the red light signal. In this case, the direction of the inflection point that appears in β(t) will be different.

[0118] Here, the pulse wave signal used to calculate coefficient α is time-shifted from the pulse wave signal to which the calculated coefficient α is applied. In other words, the correction is performed by multiplying coefficient α, which is expressed as the amplitude ratio between ΔIR and ΔRed before the amount of inspired oxygen is changed, by IR(t) or Red(t) after the amount of inspired oxygen is changed. For example, it is desirable to use the pulse wave signal obtained at rest before breathing arrest when calculating coefficient α.

[0119] Next, the operation of the biological information measuring device 10 according to the first embodiment will be described with reference to Fig. 18. Fig. 18 is a flowchart showing an example of the processing flow of the biological information measuring program according to the first embodiment.

[0120] First, when the power supply of the biological information measurement device 10 is turned on by the subject or the person in charge of measurement, the biological information measurement program is started and the following steps are executed.

[0121] 18, the CPU 20A acquires the amplitude (ΔIR) of the IR light signal obtained from the light receiving element 3, and acquires the amplitude (ΔRed) of the red light signal obtained from the light receiving element 3. In this step 100, first, while the subject is in a resting state, the CPU 20A acquires each of ΔIR and ΔRed as the pulse wave amplitude.

[0122] 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.

[0123] As shown in FIG. 19, the CPU 20A obtains ΔIR from IR(t), which is time-series data of the IR light signal value, and obtains ΔRed from Red(t), which is time-series data of the red light signal value.

[0124] In step 102, the CPU 20A derives a coefficient α expressed as the amplitude ratio between ΔIR and ΔRed based on ΔIR and ΔRed acquired in step 100. As an example, the coefficient α is derived by the following method. (a) Use the amplitude ratio obtained at any timing. In this case, it may be after the start of LFCT measurement. (b) The average value of multiple amplitude ratios obtained over a certain period of time is used. This method allows for a coefficient α that is more suitable for measurement than when coefficient α is derived using the amplitude ratio at only one point. (c) After the measurement is completed, as shown in FIG. 20, for example, the coefficient α is varied between 0 and 1, and the value that minimizes the frequency component of the pulsation appearing in the pulse wave difference β(t) is adopted. However, if the coefficient α is set to (ΔRed / ΔIR), the condition ΔIR > ΔRed must be satisfied. With this method, there is no need to derive the coefficient α during measurement, which, for example, shortens the measurement time.

[0125] 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. 20, the vertical axis represents the pulse wave difference β(t). In this example, α=ΔRed / ΔIR, and β(t)=α×IR(t)−Red(t).

[0126] The upper graph in Figure 20 shows the full waveform and enlarged waveform of the pulse wave difference β(t) when the coefficient α is 0.2. The left graph is the full waveform, and the right graph is the enlarged waveform.

[0127] The center diagram in Figure 20 shows the full waveform and the enlarged waveform of the pulse wave difference β(t) when the coefficient α is 0.3583. The left diagram is the full waveform, and the right diagram is the enlarged waveform.

[0128] The bottom diagram in Figure 20 shows the full waveform and enlarged waveform of the pulse wave difference β(t) when the coefficient α is 0.6. The left diagram is the full waveform, and the right diagram is the enlarged waveform.

[0129] From the above, it can be seen that the pulsation frequency components appearing in the pulse wave difference β(t) are smallest when the coefficient α = 0.3583. Therefore, according to method (c) above, by adopting coefficient α = 0.3583, a pulse wave difference β(t) with the oxygen concentration inflection point at the correct position can be obtained.

[0130] In step 104, the CPU 20A receives an instruction to start LFCT measurement while the subject is in a resting state. This instruction to start measurement is, for example, given by the subject or the person in charge of measurement via the touch panel of the display unit 22 or the like.

[0131] In step 106, CPU 20A instructs the subject to start breath-holding. Specifically, this may be done by displaying a message such as "Please hold your breath" on display unit 22, or by giving a voice instruction.

[0132] In step 108, CPU 20A instructs the subject to restart breathing after a certain period of time has elapsed (e.g., 20 seconds) since the start of breath-holding. Specifically, this may be done by displaying a countdown message instructing the subject to restart breathing on display unit 22, or by voice instruction. Alternatively, the subject may input that they have restarted breathing by operating the device themselves (such as by pressing a button).

[0133] In step 110, CPU 20A determines whether a predetermined time has elapsed since respiration resumed. This predetermined time is set in advance as a time for follow-up observation, and is, for example, 60 seconds. Note that since the time it takes for oxygen to reach the measurement site varies depending on the measurement site, it is advisable to set in advance a time for follow-up observation appropriate for the measurement site. If it is determined that the predetermined time has elapsed (in the case of a positive 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), the process waits in step 110.

[0134] In step 112, the CPU 20A performs correction by multiplying IR(t) or Red(t) obtained by the measurement by the coefficient α derived in step 102. Note that in this embodiment, correction is performed by multiplying IR(t) by the coefficient α (ΔRed / ΔIR), but when correcting Red(t), the coefficient α may be set to ΔIR / ΔRed.

[0135] Fig. 21 is a graph showing an example of time series data of the IR optical signal and time series data of the red optical 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 the time series data of the IR optical signal. Graph g2 represents Red(t), which is the time series data of the red optical signal.

[0136] Fig. 22 is a graph showing an example of time series data of the IR light signal and time series data of the red light signal after correction according to this 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, graph g3 represents α × IR(t), which is obtained by multiplying IR(t) by a coefficient α to adjust the offset. Graph g4 represents Red(t), which is the time series data of the red light signal.

[0137] The instruction to restart breathing in step 108 may be given when a drop in blood oxygen concentration is detected.

[0138] Fig. 23 is a graph showing an example of the results of monitoring the difference in pulse waves according to this embodiment. In Fig. 23, the vertical axis represents the pulse wave difference β(t) and the horizontal axis represents time. As can be seen from Fig. 23, changes in oxygen saturation due to breath holding are clearly evident.

[0139] Next, in step 114, the CPU 20A calculates the pulse wave difference β(t) using the above equation (7) from α×IR(t) and Red(t) obtained by correction in step 112. Note that if Red(t) has been corrected, the pulse wave difference β(t) can be calculated using the above equation (8).

[0140] Next, in step 116, the CPU 20A calculates a value (for example, a coefficient of determination R 2 If it is determined that the value representing the degree of correlation is less than the threshold (in the case of a positive determination), the pulse wave difference β(t) calculated in step 114 is determined to be appropriate, and the process proceeds to step 118. If it is determined that the value representing the degree of correlation is equal to or greater than the threshold (in the case of a negative determination), the pulse wave difference β(t) calculated in step 114 is determined to be inappropriate, and the process proceeds to step 122.

[0141] In step 118, the CPU 20A detects, from the pulse wave difference β(t) calculated in step 114, an inflection point in the blood oxygen concentration that accompanies a change in the amount of inspired oxygen of the subject.

[0142] In step 120, the CPU 20A determines the time from the point when the amount of inspired oxygen of the subject changes to the inflection point detected in step 118 as the LFCT, and ends a series of processes by this biological information measurement program. Note that in this embodiment, the process is limited to determining the LFCT, but the determined LFCT may be further applied to the above equation (6) to calculate cardiac output, which is an example of stroke volume.

[0143] 24 is a graph showing an example of an LFCT determined 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.

[0144] As shown in FIG. 24, the time from the point when breathing resumes to the inflection point indicated by the maximum value of the pulse wave difference β(t) (=α×IR(t)−Red(t)) is defined as LFCT.

[0145] In Figure 24, graph g5 shows the pulse wave difference β(t) as a moving average of n-segment data (n=64 in this example). Graph g6 shows the pulse wave difference β(t) when coefficient α=0.3583. By using the pulse wave difference β(t) as a moving average of n-segment data, residual pulse wave components due to differences in blood oxygen concentration are eliminated, resulting in a more accurate LFCT.

[0146] 24, it can be seen that immediately after the breath-holding period ends and breathing resumes, the value of pulse wave difference β(t) increases, reaches a peak, and then decreases. Because pulse wave difference β(t) increases as the blood oxygen concentration decreases, the point at which it peaks indicates the lowest blood oxygen concentration, and the inflection point at which it begins to decrease indicates that oxygen has begun to be taken up into the blood upon resumption of breathing. Therefore, the time from resumption of breathing to the peak is identified as the LFCT.

[0147] On the other hand, in step 122, the CPU 20A issues a warning to urge the user to perform re-measurement and terminates the series of processes by this biological information measurement program. This warning is issued, for example, by displaying on the display unit 22 a message urging the user to exhale sufficiently, hold their breath, and then perform re-measurement.

[0148] 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 a coefficient of determination R 2 The correspondence between these two will be specifically explained below.

[0149] Figure 25(A) is a graph showing time series data of IR light signal and red light signal for the first inappropriate pattern. Figure 25(B) is a graph showing the first inappropriate pattern of pulse wave difference β(t). Figure 25(C) is a scatter plot showing the correlation between IR light output voltage and red light output voltage for the first inappropriate pattern.

[0150] The first inappropriate pattern is a pattern in which there are two or more minimum values, and the values ​​are so similar that it is impossible to determine which one should be the minimum, as shown in Figure 25(B). In the existing algorithm, when the difference between the first smallest minimum value and the median is set to 1, the ratio of the difference between the second smallest minimum value and the median is greater than a threshold value (0.5), which determines the first inappropriate pattern. In contrast, as shown in Figure 25(C), the coefficient of determination R, which represents the degree of correlation between the IR light signal and the red light signal, is 2 is 0.8907, which is equal to or greater than the threshold value (for example, 0.8). 2 If is equal to or greater than the threshold, the pulse wave difference β(t) is determined to be inappropriate, and therefore the first inappropriate pattern can be more easily determined compared to existing algorithms. Note that the direction of the peak in FIG. 25(B) is opposite to the direction of the peak in FIG. 24. Also, while the pulse wave difference β(t) was defined above as α×IR(t)−Red(t), here, the pulse wave difference β(t) is defined as Red(t)−α×IR(t). The same applies to FIGS. 26(B), 27(B), and 28(B) described below.

[0151] Figure 26(A) is a graph showing time series data of the IR light signal and the red light signal for the second inappropriate pattern. Figure 26(B) is a graph showing the second inappropriate pattern of the pulse wave difference β(t). Figure 26(C) is a scatter plot showing the correlation between the IR light output voltage and the red light output voltage for the second inappropriate pattern.

[0152] The second inappropriate pattern is a pattern in which a minimum value is present but is broad, as shown in Figure 26(B). In the existing algorithm, when the maximum value of the pulse wave difference β(t) after respiration is set to 1 and the minimum value to 0, the pattern is judged to be the second inappropriate pattern if the length of time during which the minimum value and both sides of the minimum value cross 0.2 is equal to or longer than the threshold value (20 seconds). In contrast, as shown in Figure 26(C), the coefficient of determination R, which represents the degree of correlation between the IR light signal and the red light signal, 2 is 0.8587, which is equal to or greater than the threshold value (for example, 0.8). 2 If is equal to or greater than the threshold, the pulse wave difference β(t) is determined to be inappropriate, and therefore the second inappropriate pattern can be easily determined in comparison with existing algorithms.

[0153] Figure 27(A) is a graph showing time series data of IR light signal and red light signal for the third inappropriate pattern. Figure 27(B) is a graph showing the third inappropriate pattern of pulse wave difference β(t). Figure 27(C) is a scatter plot showing the correlation between IR light output voltage and red light output voltage for the third inappropriate pattern.

[0154] The third inappropriate pattern is a pattern in which the value of the pulse wave difference β(t) at the time of respiration is equal to or less than the minimum value, as shown in Figure 27(B). In the existing algorithm, the value at the time of respiration is compared with the minimum value, and if the value at the time of respiration is less than the minimum value, it is determined to be the third inappropriate pattern. In contrast, as shown in Figure 27(C), the coefficient of determination R 2 is 0.9844, which is equal to or greater than the threshold value (for example, 0.8). 2 If is equal to or greater than the threshold, the pulse wave difference β(t) is determined to be inappropriate, and therefore the third inappropriate pattern can be easily determined in comparison with existing algorithms.

[0155] Figure 28(A) is a graph showing time series data of the IR light signal and the red light signal relating to the fourth inappropriate pattern. Figure 28(B) is a graph showing the fourth inappropriate pattern of the pulse wave difference β(t). Figure 28(C) is a scatter plot showing the correlation between the IR light output voltage and the red light output voltage for the fourth inappropriate pattern.

[0156] The fourth inappropriate pattern is a pattern in which the point where the pulse wave difference β(t) reaches its minimum value after respiration is not a local minimum, as shown in Figure 28(B). Existing algorithms compare the minimum value of the pulse wave difference β(t) with the local minimum and minimum values, and determine that the pattern is the fourth inappropriate pattern if the minimum value and the local minimum value do not match. In contrast, as shown in Figure 28(C), the coefficient of determination R, which represents the degree of correlation between the IR light signal and the red light signal, 2 is 0.9758, which is equal to or greater than the threshold value (for example, 0.8). 2 If is equal to or greater than the threshold, the pulse wave difference β(t) is determined to be inappropriate, and therefore the fourth inappropriate pattern can be easily determined in comparison with existing algorithms.

[0157] As described above, according to this embodiment, when the correlation between the two pulse waves is high, the waveform pattern representing the change in blood oxygen concentration is determined to be inappropriate, and when the correlation is low, the waveform pattern representing the change in blood oxygen concentration is determined to be appropriate. Therefore, compared to existing algorithms, inappropriate patterns can be easily determined. Furthermore, since the state of change in blood oxygen concentration due to breath holding can be determined, a warning is issued when the change in blood oxygen concentration is small, and a remeasurement is encouraged, thereby improving the reliability of the data.

[0158] [Second embodiment] In the first embodiment, the coefficient α used for correction is the amplitude ratio between the amplitude of the IR optical signal and the amplitude of the red optical signal. In this embodiment, the coefficient α is calculated from the slope of a regression line obtained from the IR optical signal and the red optical signal.

[0159] Since the biological information measuring device of this embodiment has the same components as the biological information measuring device 10 described in the first embodiment above, repeated explanations will be omitted and only the differences in the correction unit 31 will be described with reference to Figure 16.

[0160] The correction unit 31 calculates the coefficient α from the slope of the regression line obtained from the values ​​of the IR light signal and the red light signal.

[0161] FIG. 29 is a scatter plot illustrating an example of the correlation between the IR light output voltage and the red light output voltage according to the second embodiment.

[0162] A regression line is calculated from the scatter plot in FIG. 29. In this case, the regression line is calculated as y = 0.3974x + 0.2228. The slope of this regression line, 0.3974, is set as the coefficient α. In this case, if the heart rate of the living body 8 is 1 beat, the correlation may not decrease even if there is a change in the blood oxygen concentration. For this reason, it is desirable to calculate the regression line from the values ​​of the IR light signal and the red light signal over a period of 2 beats or more of the living body 8.

[0163] The correction unit 31 also calculates a value (for example, a coefficient of determination R 2 ) is equal to or greater than a predetermined value (for example, 0.9), the coefficient α may be calculated.

[0164] FIG. 30 is a diagram showing an example of a preparation period, a breath-holding period, and a follow-up period in LFCT measurement.

[0165] In Figure 30, the coefficient α is calculated from the regression line data before the start of LFCT measurement, i.e., before breath-holding begins. During the period before breath-holding begins, changes in blood oxygen level are relatively small, and the correlation between the IR optical signal and the red optical signal is high. For subjects with a relatively short LFCT, a decrease in blood oxygen level begins to appear soon after breath-holding. For example, for a subject with a 10-second LFCT, a change in blood oxygen level appears approximately 10 seconds after breath-holding. If the breath-holding time is 20 seconds, the blood oxygen level will change for half of the time. In contrast, no significant changes in blood oxygen level appear before breath-holding begins. Therefore, the coefficient α is calculated from the regression line data for the period before breath-holding begins.

[0166] Figures 31(A), 31(B), and 32 are diagrams used to explain the correlation between the IR optical signal and the red optical signal. Figure 31(A) shows time-series data of the IR optical signal and the red optical signal, and Figure 31(B) shows the LFCT of the pulse wave difference β(t). Figure 32 is a scatter plot showing the correlation between the IR optical output voltage and the red optical output voltage for each period of the time-series data of the IR optical signal and the red optical signal shown in Figure 31(A).

[0167] In FIG. 31(A), the IR light signal and the red light signal in the region (1) have a coefficient of determination R 2 is 0.9987, which indicates 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 coefficient α can be determined in region (1). This eliminates the error in coefficient α caused by changes in blood oxygen concentration.

[0168] Similarly, in FIG. 31(A), the IR light signal and the red light signal in the region (2) have a coefficient of determination R 2 is 0.9876, which indicates a relatively high correlation. Region (2) is the region where there is a change in blood volume. In this case, the change in blood oxygen concentration is small, and the correlation of the regression line is high, so coefficient α can be determined in region (2). This eliminates the error in coefficient α caused by changes in blood oxygen concentration.

[0169] On the other hand, in FIG. 31(A), the IR light signal and the red light signal in the region (3) have a coefficient of determination R 2 is 0.8069, which indicates a relatively low correlation. Region (3) is the region near the peak of the LFCT. For example, it is desirable to determine the coefficient α before the start of 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 changes in oxygen concentration are small, that is, when the correlation is high. If the coefficient of determination is a correlation such as in regions (1) and (2), the coefficient α may be determined, but for a correlation such as in region (3), the coefficient α is not determined.

[0170] Furthermore, the correction unit 31 may divide the regression line into a systolic line and a diastolic line, and calculate the coefficient α when the difference in the slope of each regression line is within a predetermined range (for example, 20%).

[0171] Figure 33(A) is a graph showing time-series data of the IR light signal and the red light signal during systole and diastole. Figure 33(B) is a scatter plot showing the correlation between the IR light output voltage and the red light output voltage during systole and diastole. The data in Figure 33(B) represents data for one beat when the blood oxygen concentration is changing. In this case, differences are observed in the slope of the regression line between systole and diastole, resulting in a low correlation overall. Note that systole is the period when the heart contracts to pump blood and blood pressure increases, while diastole is the period when the heart expands and blood that has circulated throughout the body returns to the heart, causing blood pressure to decrease.

[0172] As shown in Figure 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, so the correlation is high, and when this difference is large, the blood oxygen concentration changes, so the correlation is low.

[0173] Figure 34(A) is a scatter plot showing an example of regression lines for the systolic and diastolic periods when there is no change in blood oxygen concentration, and Figure 34(B) is a scatter plot showing an example of regression lines for the systolic and diastolic periods when there is a change in blood oxygen concentration.

[0174] The scatter plot in FIG. 34(A) shows an example where the blood oxygen concentration is unchanged, and the slope of the regression line during the systolic period is approximately the same as the slope of the regression line during the diastolic period. The regression line during the systolic period is expressed as y = 0.6994x + 0.4318, and the regression line during the diastolic period is expressed as y = 0.6665x + 0.4574. On the other hand, the scatter plot in FIG. 34(B) shows an example where the blood oxygen concentration is changing, and the slope of the regression line during the systolic period is different from the slope of the regression line during the diastolic period. The regression line during the systolic period is expressed as y = 0.8266x + 0.263, and the regression line during the diastolic period is expressed as y = 0.5797x + 0.487. Therefore, as described above, the coefficient α is calculated when the difference in the slopes of the regression lines is within a predetermined range (e.g., 20%). This eliminates the error in the coefficient α caused by changes in blood oxygen concentration.

[0175] Furthermore, the correction unit 31 may calculate the coefficient α when the LF / HF ratio, which is an index indicating the state of tension of the living body 8, is equal to or less than a threshold value (for example, 4.0).

[0176] When a living body8 is in a state of tension, the sympathetic nervous system is activated, causing the heart to beat faster and peripheral blood vessels to constrict, which may change the state of blood circulation and affect LFCT measurements. For this reason, it is desirable to perform measurements in a resting state.

[0177] One example of an index for determining the state of tension is the LF / HF ratio, which is the integral ratio between the low-frequency component of the pulse wave (mainly due to Mayer waves) and the high-frequency component (mainly due to breathing). For example, the coefficient α may be determined when the LF / HF ratio is 4.0 or less. Using this index can prevent measurements from being taken when neural activity is increased.

[0178] The above describes a case where the coefficient α is determined when the conditions for the correlation between the IR light signal and the red light signal (correlation of the pulse wave), the difference in the slope of the regression line between the systole and the diastole, and the LF / HF ratio are all satisfied. However, the coefficient α may also be determined when all of these conditions are satisfied.

[0179] As described above, according to this embodiment, a stable coefficient α can be obtained from a large amount of data even when the pulsation is small. Moreover, since the coefficient α is calculated from pulse wave data, a stable coefficient α can be obtained even in a short time. Furthermore, when determining the coefficient α, the correlation of the pulse wave, the difference in the slope of the regression line between the systolic and diastolic phases, and the LF / HF ratio are used, thereby obtaining a stable coefficient α.

[0180] In each of the above embodiments, the term "processor" refers to a processor in a broad sense, and includes general-purpose processors (e.g., CPU: Central Processing Unit, etc.) and dedicated processors (e.g., GPU: Graphics Processing Unit, ASIC: Application Specific Integrated Circuit, FPGA: Field Programmable Gate Array, programmable logic device, etc.).

[0181] Furthermore, the operations of the processor in each of the above embodiments may not only be performed by a single processor, but may also be performed by multiple processors located at physically separate locations working together. Furthermore, the order of the operations of the processor is not limited to the order described in each of the above embodiments, and may be changed as appropriate.

[0182] The above describes an example of a biological information measuring device according to an embodiment. The embodiment may be in the form of a program for causing a computer to execute the functions of each unit of the biological information measuring device. The embodiment may be in the form of a non-transitory storage medium that stores the program and is readable by a computer.

[0183] Furthermore, the configuration of the biological information measurement device described in the above embodiment is merely an example, and may be changed depending on the situation without departing from the spirit of the invention.

[0184] Furthermore, the processing flow of the program described in the above embodiment is also an example, and unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged within the scope of the main idea.

[0185] In the above embodiment, 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. The embodiment may be realized by, for example, a hardware configuration or a combination of a hardware configuration and a software configuration. [Explanation of symbols]

[0186] 1 Light-emitting element 3 Photodetector 4 arteries 5. Veins 6 Capillaries 7 blood cells 8 Living organisms 10 Biological information measuring device 12 Light emission control unit 14 Drive circuit 16 Amplification circuit 18 A / D conversion circuit 20 Control Unit 20A CPU 20B ROM 20C RAM 22 Display section 30 Acquisition Department 31 Correction unit 32 Calculation section 33 Judgment section 34 Detector 35 Specific part 36 Estimation part

Claims

1. a processor; The processor: A first signal representing a change in the amount of light of a first wavelength, which is light in a wavelength range corresponding to an infrared region detected from a living body, and a second signal representing a change in the amount of light of a second wavelength, which is light in a wavelength range corresponding to a red region detected from the living body, are obtained; correcting one of the value of the first signal and the value of the second signal by multiplying the value of the first signal or the value of the second signal by a coefficient represented by an amplitude ratio between the amplitude of the first signal and the amplitude of the second signal so that a difference between an amount of change in the first signal and an amount of change in the second signal due to a change in the arterial blood volume of the living body becomes small; calculating a waveform pattern representing a change in blood oxygen concentration in the living body, the waveform pattern being expressed as a difference between the value of the first signal, one of which has been corrected by the coefficient, and the value of the second signal; When a value representing a degree of correlation between the first signal and the second signal is less than a threshold, the waveform pattern is determined to be appropriate. Biometric information measuring device.

2. The processor detects an inflection point of the blood oxygen concentration associated with a change in the amount of inspired oxygen of the living body from the waveform pattern determined to be appropriate, Identifying the time from the point when the amount of inhaled oxygen of the living body changes to the inflection point of the detected blood oxygen concentration. The biological information measuring device according to claim 1 .

3. When the value representing the degree of correlation is equal to or greater than the threshold value, the processor determines that the waveform pattern is inappropriate and issues a warning to prompt remeasurement. The biological information measuring device according to claim 1 or 2.

4. The warning includes a message prompting the user to exhale, hold their breath and take another measurement. The biological information measuring device according to claim 3 .

5. 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 intake oxygen of the living body is changed. The biological information measuring device according to any one of claims 1 to 4.

6. The predetermined time is a period of two or more beats of the living body. The biological information measuring device according to claim 5 .

7. the coefficient is represented by an amplitude ratio between an amplitude of the first signal and an amplitude of the second signal before changing the amount of inspired oxygen of 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 amount of oxygen inhaled by the living body by the coefficient. The biological information measuring device according to any one of claims 1 to 6.

8. The processor calculates the coefficient from a slope of a regression line obtained from the values ​​of the first signal and the values ​​of the second signal. The biological information measuring device according to claim 1 .

9. The regression line is obtained from the values ​​of the first signal and the second signal over a period of two or more beats of the living body. The biological information measuring device according to claim 8 .

10. The processor calculates the coefficient when a value representing a degree of correlation between the first signal and the second signal is equal to or greater than a predetermined value. The biological information measuring device according to claim 8 or 9.

11. The processor divides the regression line into a systolic period and a diastolic period, and calculates the coefficient when a difference in slope between the respective regression lines is within a predetermined range. The biological information measuring device according to any one of claims 8 to 10.

12. 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. The biological information measuring device according to any one of claims 8 to 11.

13. A first signal representing a change in the amount of light of a first wavelength, which is light in a wavelength range corresponding to an infrared region detected from a living body, and a second signal representing a change in the amount of light of a second wavelength, which is light in a wavelength range corresponding to a red region detected from the living body, are obtained; correcting one of the value of the first signal and the value of the second signal by multiplying the value of the first signal or the value of the second signal by a coefficient represented by an amplitude ratio between the amplitude of the first signal and the amplitude of the second signal so that a difference between an amount of change in the first signal and an amount of change in the second signal due to a change in the arterial blood volume of the living body becomes small; calculating a waveform pattern representing a change in blood oxygen concentration in the living body, the waveform pattern being expressed as a difference between the value of the first signal, one of which has been corrected by the coefficient, and the value of the second signal; determining 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 value; A biometric information measurement program to be executed by a computer.

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