Biometric state estimation device, computer program, and recording medium

Abstract

To provide a technique for estimating a medication effect.SOLUTION: A biometric state estimation device compares correlation data for estimation relating to a standard deviation and an average value of distances between a reference plane of an attractor of a subject having a cardiovascular disease and plotted points of instantaneous heart rates with correlation data of a group of healthy subjects stored in a database in advance, and correlation data of a group of subjects having a cardiovascular disease, and can estimate a medication effect on the basis of whether the correlation data for estimation approximate any correlation data or a positional relationship thereof. Since acoustic vibration data or electrocardiogram data from a body surface from which the instantaneous heart rate can be easily obtained in a sitting or lying posture can be used, the heart rate can be measured easily at home and is useful for remote medical treatment and home nursing, enabling more appropriate pharmaceutical prescription.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for estimating the effects of medication on a person with cardiovascular disease. [Background technology]

[0002] The applicant has disclosed in Patent Document 1 a blood pressure estimation device that analyzes biosignal data obtained from a biosignal detection sensor using a three-dimensional knitted fabric, captures vibration information within the body caused by blood flow fluctuations from the ventricular filling period to the isovolumic contraction period, obtains an index showing the fluctuations by applying the Lorenz plot method, and estimates blood pressure values ​​from the index (Patent Document 1).

[0003] Specifically, the technology captures biosignals (acoustic vibration data (Acoustic Pulse Wave: APW) from the surface of the body on the back using a biosignal detection sensor, filters the time-series waveform in a specified frequency band, and obtains a filtered waveform that reveals the cardiac cycle. This filtered waveform is then compared with electrocardiogram data (ECG) obtained from an electrocardiograph at the same time, and the waveform components from the ventricular filling period to the isovolumic contraction period are identified. After that, the Lorenz plot method is applied, and using the slope of a large number of point clouds plotted at a specified measurement time as a reference, a new time series is constructed for the angle difference with the slope of point clouds at shorter measurement times. The time-series waveform is frequency-analyzed, and the frequency analysis results are displayed on a double logarithmic axis to find a regression line for the range from VLF to LF in the analyzed waveform. The slope of this regression line is defined as the fractal slope, and blood pressure is estimated from the fractal slope.

[0004] Patent Document 2 applies a predetermined time width to the time series waveform of the dorsal surface pulse wave near 1 Hz extracted from acoustic vibration data, performs sliding calculations to determine the time series waveform of the frequency gradient, and estimates the biological condition from the tendency of the change, for example, whether the amplitude is tending to increase or decrease. It also discloses that the biological signal is frequency analyzed to determine the power spectrum of each frequency corresponding to the function adjustment signal, fatigue acceptance signal, and activity adjustment signal belonging to a predetermined ULF band (ultra-low frequency band) to VLF band (very low frequency band), and the human condition is determined from the time series change of each power spectrum.

[0005] Patent Documents 3 and 4 disclose means for determining the homeostatic function level, and Patent Document 5 discloses a technique for easily extracting the waveform of the apex beat by determining the boundary frequency between the vibration caused by the apex beat and the vibration caused by the heart sound from acoustic vibration data. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2019-122502 [Patent Document 2] Japanese Patent Application Laid-Open No. 2011-167362 [Patent Document 3] Japanese Patent Application Laid-Open No. 2014-117425 [Patent Document 4] Japanese Patent Application Laid-Open No. 2014-223271 [Patent Document 5] Japanese Patent Publication No. 2022-71786 Summary of the Invention [Problem to be solved by the invention]

[0007] The present applicant has developed a technology that uses APW and ECG to capture, as described above, human conditions related to bioregulation, such as blood pressure, sleep onset prediction phenomenon, impending sleep phenomenon, state of drowsiness, homeostatic function level, apical pulsation, etc. However, the conditions of bioregulation are not limited to these and are diverse, and it is desirable to be able to capture as many conditions as possible using APW and ECG.

[0008] In recent years, the advent of a super-aging society, the resulting shortage of medical professionals, and advances in information technology have combined to promote the widespread use of telemedicine and online consultations. APWs can be easily measured by simply placing a sensor on the back. For example, if a sensor like the one shown in Figure 2 is installed in a seat like the one shown in Figure 3, measurements can be taken while the elderly are sitting or lying down, making it convenient for the elderly. Acoustic vibration data can be collected simply by the elderly sitting or lying down on the seat, and receiving this data can facilitate telemedicine and other services. It also facilitates data collection in home nursing. Since the target population for telemedicine and home nursing is the elderly, many suffer from cardiovascular diseases such as hypertension and heart disease. Therefore, many patients are prescribed vasopressors and cardiac inotropes, which often have severe side effects. Therefore, it is desirable to be able to objectively evaluate the efficacy and severity of side effects of prescribed medications as part of in vivo information. In particular, since telemedicine reduces face-to-face encounters, collecting these evaluations as information will enable more appropriate medication prescriptions. It is also useful when people are unable to effectively explain their condition due to dementia or other reasons.

[0009] The present invention has been made in view of the above, and an object of the present invention is to provide a technique that can estimate medication effects from time-series data of instantaneous heart rate. [Means for solving the problem]

[0010] In order to solve the above problem, the inventors noticed that there was a significant difference between a group of healthy subjects and a group of subjects with cardiovascular disease in the relationship between the average value and standard deviation of the distance between the reference plane of an attractor reconstructed into a three-dimensional delay coordinate system using time series data of instantaneous heart rate and the plotted points of each instantaneous heart rate, and thus completed the present invention.

[0011] That is, in the present invention, an attractor construction unit that reconstructs a three-dimensional delay coordinate system from the time series data of the instantaneous heart rate using a Lorenz plot to obtain an attractor; a reference plane calculation unit that calculates a reference plane by performing plane approximation of the attractor using a least squares method; a correlation data calculation unit that calculates an average value of distances and a standard deviation of distances from the reference plane for each point of the instantaneous heart rate plotted on the three-dimensional delay coordinate system, and obtains correlation data between the average value of distances and the standard deviation of distances for a group of healthy subjects and a group of subjects with cardiovascular disease; an estimation unit that calculates correlation data for estimation regarding the average value of the distances and the standard deviation of the distances by the correlation data calculation unit, compares the correlation data for estimation with the correlation data, and estimates the medication effect on the subject; The present invention provides a biological state estimation device having the above configuration.

[0012] It is preferable that the estimation unit is configured to estimate that the medication effect is high if the subject to be estimated is a subject with cardiovascular disease and the correlation data for estimation is within an approximate range of the correlation data of the group of healthy subjects. When the subject to be estimated is a subject with a cardiovascular disease, and the correlation data for estimation is within an approximate range of the correlation data of the group of subjects with cardiovascular disease, the estimation unit is preferably configured to estimate the state of the pharmacological action caused by medication based on the position of the correlation data for estimation. It is preferable that the estimation unit is configured to estimate that the medication effect is low if the subject to be estimated is a subject with cardiovascular disease and the correlation data for estimation is not included in the approximate range of the correlation data of the group of subjects with cardiovascular disease.

[0013] As the time series data of the instantaneous heart rate, it is preferable to use electrocardiogram data or acoustic vibration data obtained from the body surface.

[0014] The time series data of the instantaneous heart rate are made up of electrocardiogram data and acoustic vibration data obtained from the body surface, the attractor construction unit finds an attractor for each of the attractors, the reference plane calculation unit finds a reference plane for each of the attractors, and the correlation data calculation unit creates correlation data for each of the attractors, The estimation unit is preferably configured to estimate the effect of administration to the subject based on each correlation data.

[0015] In addition, in the present invention, A computer program that causes a computer to function as a biological state estimation device related to medication effects, A procedure for reconstructing a 3D delayed coordinate system using Lorenz plots of time series data of instantaneous heart rate to obtain an attractor. a step of finding a plane as a reference plane by approximating the attractor to a plane using a least squares method; a step of calculating an average value of distances and a standard deviation of distances from the reference plane for each point of the instantaneous heart rate plotted on the three-dimensional delay coordinate system, and obtaining correlation data between the average value of distances and the standard deviation of distances for a group of healthy subjects and a group of subjects with cardiovascular disease; a step of obtaining correlation data for estimation regarding the average value of the distance and the standard deviation of the distance for the subject to be estimated, comparing the correlation data for estimation with the correlation data, and estimating the effect of medication on the subject to be estimated; A computer program is provided that causes the computer to execute the above.

[0016] The present invention also provides a computer-readable recording medium on which the computer program is recorded. [Effects of the Invention]

[0017] According to the present invention, correlation data for estimation relating to the average value and standard deviation of the distance between the reference plane of the attractor of a subject with cardiovascular disease and the plotted points of each instantaneous heart rate can be compared with correlation data of a group of healthy subjects and correlation data of a group of subjects with cardiovascular disease that have been previously compiled into a database, and the effectiveness of medication can be estimated based on which correlation data the correlation data for estimation approximates or their positional relationship. Instantaneous heart rate can be measured easily at home using acoustic vibration data from the body surface, which can be easily obtained while sitting or lying down, or electrocardiogram data, of which small models are widely available. This makes it useful for remote medical care and home nursing, and can lead to more appropriate medication prescriptions. [Brief explanation of the drawings]

[0018] [Figure 1] FIG. 1 is a block diagram showing the configuration of a biological state estimating device according to an embodiment of the present invention. [Figure 2] 2(a) to 2(c) are diagrams for explaining the configuration of the APW sensor. [Figure 3] FIG. 3 shows an example of a seat with an APW sensor built into the backrest. [Figure 4] 4(a) to 4(c) are diagrams for explaining the relationship between the APW waveform and the ECG waveform. [Figure 5] FIG. 5 is a graph showing the correlation between the heart rate obtained from the ECG and the heart rate obtained from the APW. [Figure 6] Figures 6(a) to (c) show the attractors obtained using the instantaneous heart rate obtained from the ECG. (a) shows the data of male subject B in his 20s, (b) shows the data of male subject C in his 70s, and (c) shows the data of female subject D in her 80s. [Figure 7]Figures 7(a) to (c) show the attractors calculated using the instantaneous heart rate obtained from the APW. (a) shows data from male subject B in his 20s, (b) from male subject C in his 70s, and (c) from female subject D in her 80s. [Figure 8] Figures 8(a)-(b) are correlation diagrams of the mean distance (Mean) and the standard deviation (SD) of the distance between the reference plane of the attractor obtained using ECG and each point, where (a) is the correlation data for subjects in the healthy group, and (b) is the correlation data for subjects in the outpatient group. [Figure 9] Figures 9(a)-(b) are correlation diagrams of the mean distance (Mean) and the standard deviation (SD) of the distance between the reference plane of the attractor obtained using APW and each point, where (a) is the correlation data for subjects in the healthy group, and (b) is the correlation data for subjects in the outpatient group. [Figure 10] FIG. 10(a) is a correlation diagram between the mean distance and the standard deviation (SD) of the distance for 171 subjects (260 cases), and FIG. 10(b) is an enlarged view of the correlation diagram. DETAILED DESCRIPTION OF THE INVENTION

[0019] (Biological condition estimation device) 1 shows a schematic configuration of a biological state estimating device 1 according to one embodiment. The biological state estimating device 1 of this embodiment is configured by a computer (the type of computer is not limited, and includes a personal computer, a microcomputer, a mobile information terminal such as a smartphone, etc.), receives instantaneous heart rate data, which is biological signal data, and executes predetermined processing.

[0020] Specifically, the biological state estimation device 1 has an attractor construction unit 11, a reference plane calculation unit 12, a correlation data calculation unit 13, and an estimation unit 14. The biological state estimation device 1 is composed of a computer, and a computer program for executing procedures that function as the attractor construction unit 11, the reference plane calculation unit 12, the correlation data calculation unit 13, and the estimation unit 14 is stored in a storage unit (memory). Note that the storage unit (memory) includes not only a recording medium such as a hard disk built into the biological state estimation device 1 composed of a computer, but also various removable recording media, recording media of other computers connected via communication means, and the like. The biological state estimation device 1 can also be realized using an electronic circuit having a memory in which the above-mentioned computer program is embedded.

[0021] The computer program can also be provided by being stored on a recording medium. The recording medium storing the computer program may be a non-transitory recording medium. Non-transitory recording media are not particularly limited, and examples thereof include flexible disks, hard disks, CD-ROMs, MOs (magneto-optical disks), DVD-ROMs, and memory cards. The computer program can also be transmitted to a computer via a communication line and installed.

[0022] The attractor construction unit 11 reconstructs a three-dimensional delay coordinate system for the time series data of the instantaneous heart rate (HR = 60 / Δt) using a Lorenz plot to find an attractor related to heart rate fluctuations. The time series data of the instantaneous heart rate can be, for example, electrocardiogram data obtained from an electrocardiograph. Alternatively, acoustic vibration data (APW) representing biosignals from the body surface of the back, collected by placing the APW sensor 100 shown in Figures 2(a) to 2(c) against a person's back, can also be used. Figures 4(a) to 4(c) show, from top to bottom, the original APW waveform (Figure 4(a)), the APW vibration wave waveform (Figure 4(b)), and electrocardiogram data (ECG) obtained from an electrocardiograph (Figure 4(c)). The APW vibration waveform in Figure 4(b) is a waveform near 1 Hz calculated by full-wave rectification and detection of the waveform (resonant carrier wave) obtained by applying a 10 to 30 Hz band-pass filter to the original APW waveform. Comparing Figures 4(b) and 4(c), the peak interval of the vibration wave of the APW in Figure 4(b) shows a period that is close to the RR interval (RRI) shown in the ECG in Figure 4(c). Therefore, as shown in Figure 5, there is a high correlation between the APW and the ECG.

[0023] The Lorenz plot uses three points from the time series data of instantaneous heart rate (HR) and plots them while moving the range. Specifically, the x-axis is HR. n+1 , H.R. n+2 , H.R. n+3 , HR on the y-axis n+2 , H.R. n+3 , H.R. n+4 , HR on the z-axis n , H.R. n+1 , H.R. n+2 ... and plot their intersections in sequence. This reconstructs the time series data of instantaneous heart rate into a three-dimensional delayed coordinate system, and an attractor is drawn.

[0024] The reference plane calculation unit 12 approximates the attractor drawn by the attractor construction unit 11 to a plane by the least squares method, and obtains a plane that shows the characteristics of the attractor. The obtained plane is used as the reference plane.

[0025] 6(a)-(c) and 7(a)-(c) are diagrams showing examples of attractors and reference planes of heart rate fluctuations calculated by the attractor construction unit 11 and the reference plane calculation unit 12. Figures 6(a)-(c) use time series data of instantaneous heart rate obtained from ECG, while Figures 7(a)-(c) use time series data of instantaneous heart rate obtained from APW. Figures 6(a) and 7(a) show data from male subject B in his 20s who belongs to the healthy group of an experimental example described below. Figures 6(b) and 7(b) show data from male subject C in his 70s who belongs to the outpatient group of the same experimental example. Figures 6(c) and 7(c) show data from female subject D in her 80s who also belongs to the outpatient group. Male subject C suffers from lifestyle-related disease (diabetes), heart disease (supraventricular tachycardia, atrial fibrillation), and chronic hepatitis, while female subject D suffers from high blood pressure, heart disease (atrial fibrillation), and cerebral infarction.

[0026] The attractors for male subject B in the healthy group (Figures 6(a) and 7(a)) all follow a trajectory within a fixed range of 70–90 beats per minute. The attractors for male subject C in the outpatient group (Figures 6(b) and 7(b)) all follow a trajectory centered around 50 beats per minute, but show large fluctuations at several points. For female subject D in the outpatient group, the attractor using ECG data (Figure 6(c)) fluctuates between 45–116 beats per minute, and the attractor using APW data (Figure 7(c)) fluctuates between 31–112 beats per minute, both of which show large fluctuations.

[0027] From these findings, the attractor of heart rate variability using ECG or APW data shows fluctuations corresponding to the subject's health condition, characteristics of the disease, etc. Because both male subject C and female subject D in the outpatient group suffered from heart disease, it is thought that abnormalities in circulatory dynamics such as arrhythmia appeared as peculiar fluctuations in the attractor, and it is thought that circulatory dynamics can be estimated from the shape of the attractor of heart rate variability.

[0028] The correlation data calculation unit 13 calculates correlation data for determining the characteristics of the shape of the heart rate variability attractor. As an element characterizing the shape of the attractor, the correlation data calculation unit 13 uses the distance between the reference plane calculated by the reference plane calculation unit 12 and each point of the instantaneous heart rate constituting the attractor plotted in the three-dimensional delay coordinate system. Specifically, the correlation data calculation unit 13 calculates the average value of the distance of each point from the reference plane and the standard deviation of the distance, and calculates the correlation data. In addition, correlation data is created for a group of healthy subjects (the "healthy group" in the experimental example described below) and a group of subjects with cardiovascular disease (the "outpatient group" in the experimental example described below), and is stored in a memory unit of the biological state estimation device 1, which is composed of a computer, to construct a database (DB) of the correlation data.

[0029] The estimation unit 14 obtains data on the instantaneous heart rate of the subject for whom the medication effect is to be estimated using an ECG or APW, and uses this data to calculate an attractor and its reference plane using the attractor construction unit 11 and the reference plane calculation unit 12. Furthermore, the correlation data calculation unit 13 calculates the reference plane and the average value and standard deviation of the distances between each point. This is used as correlation data for estimation, and this correlation data for estimation is compared with the correlation data stored in the database to estimate the medication effect on the subject. The comparison is performed by adding correlation data from a group of subjects with cardiovascular disease (outpatient group) to correlation data from a group of healthy subjects (healthy group), and details will be explained in the experimental example below.

[0030] (Experimental example) (1) Sensing system APW The APW sensor 100 is similar to the sensor used to measure APW in Patent Document 5, and as shown in FIGS. 2(a) to 2(c), has a laminated structure of an air pack 110 and a gel pack 120. The air pack 110 is composed of a three-dimensional knitted fabric (3D net) 111 and an elastomer film 112 that hermetically houses the three-dimensional knitted fabric (3D net) 111. The gel pack 120 has a microphone 122 fixedly disposed within a case 121, and gel 123 is filled around the microphone 122. Using two resonance mechanisms, stochastic resonance and string vibration, the APW, which includes the apex beat and heart sounds in the range of 0.5 to 80 Hz, is efficiently extracted.

[0031] The APW sensor 100 is placed on the backrest of the seat shown in FIG. 3 so that the microphone 122 corresponds to the fifth rib area, 10 cm to the left of the midline of the chest.

[0032] ECG An electrocardiograph manufactured by Nihon Kohden Corporation, product name: Bedside Monitor BSM-2301, was used.

[0033] (2) Experimental protocol To evaluate cardiac and vascular hemodynamics, subjects were individuals without underlying cardiovascular diseases (healthy group) and individuals visiting a hospital for cardiovascular diseases such as lifestyle-related diseases and heart disease (outpatient group). The subjects were 50 healthy subjects aged 20-70 (48 men, 2 women, mean age 37.8 ± 12.4 years) and 50 outpatient subjects aged 40-90 (33 men, 17 women, mean age 74.4 ± 10.7 years). Measurements included APW, electrocardiogram (ECG), and brachial blood pressure. Subjects sat in the seat shown in Figure 3 and rested for 6 minutes. APW was measured, and lead II electrocardiogram data (ECG) was recorded. Brachial blood pressure was measured before and after APW and ECG measurements.

[0034] (Experimental results) The measured APW and ECG were analyzed by the biological state estimation device 1. Using the time series data of the instantaneous heart rate obtained from the APW and the time series data of the instantaneous heart rate obtained from the ECG, an attractor of heart rate fluctuations was created by the attractor construction unit 11, a reference plane of the attractor was found by the reference plane calculation unit 12, and correlation data relating to the average value of the distance between the reference plane and each point and the standard deviation of the distance was created by the correlation data calculation unit 13, as described above.

[0035] Figures 8(a)-(b) and Figures 9(a)-(b) are correlation diagrams of the mean distance between the reference plane of the heart rate variability attractor and each point and the standard deviation (SD) of the distance for 50 healthy subjects and 50 outpatient subjects. Figures 8(a)-(b) were obtained using ECG data, and Figures 9(a)-(b) were obtained using APW data. Figures 8(a) and 9(a) are correlation data for the healthy subject, and Figures 8(b) and 9(b) are data for the outpatient subject. The variance of each point group plotted as correlation data between the mean distance (Mean) and the standard deviation (SD) of the distance in the healthy group can be approximated by a linear approximation formula, and the coefficient of determination R 2 =0.9226, and R for the correlation data using APW 2 = 0.9134, showing a high correlation. The correlation data using ECG was plotted along a line at 54 degrees to the horizontal axis (Fig. 8(a)), while the correlation data using APW was plotted along a line at 45 degrees to the horizontal axis (Fig. 9(a)).

[0036] The variance of each point group plotted as correlation data for the outpatient group can be approximated by a quadratic approximation formula, and the coefficient of determination for correlation data using ECG is R 2 =0.909, and R for the correlation data using APW 2 =0.9352, showing a high correlation. In the correlation data using ECG in Figure 8(b), when the mean value (Mean) is 2 or less, there are more subjects plotted below the 45-degree line than in the healthy group. There are also many plots where the mean value (Mean) and standard deviation (SD) are both 4 or more. In the correlation data using APW in Figure 9(b), when the mean and standard deviation (SD) were both 10 or less, many subjects were plotted below the 45-degree line. On the other hand, when the mean and standard deviation (SD) were both 10 or more, many subjects were plotted above the 45-degree line, showing a different appearance from the plots of the healthy group.

[0037] As mentioned above, whether APW or ECG is used, there is a difference in that the healthy group shows a linear approximation and the outpatient group shows a nonlinear approximation. Therefore, the correlation data between the mean (Mean) of the distance and the standard deviation (SD) of the distance in the attractor of heart rate variability using APW and ECG data can distinguish between differences in the mechanisms of circulatory dynamics.

[0038] Looking more closely, in Figure 9(b), most of the outpatient group subjects with heart disease ("HDP" in the figure) have mean values ​​and standard deviations (SD) both above 10, or are plotted below the 45-degree line. This is thought to be due to the occurrence of arrhythmias such as bradycardia, tachycardia (such as atrial fibrillation), and premature contractions, resulting in unstable heart rate variability, which is expressed as the spatial spread (mean value) and spatial variability (standard deviation (SD)) of the heart rate variability attractor. On the other hand, several subjects were also observed plotting near the 45-degree line. The proximity of the 45-degree line is similar to the tendency of the plot positions of the subjects in the healthy group in Figure 9(a), suggesting that these subjects were able to maintain hemodynamics similar to that of the healthy group through medication. The plots for subject C shown in Figures 6(b) and 7(b) are below the 45-degree line in Figures 8(b) and 9(b), and the data for subject D shown in Figures 6(c) and 7(c) are plotted in Figures 8(b) and 9(b) where the mean and standard deviation (SD) are both greater than 10. Subject C's mean is characteristically lower than the quadratic approximation line, while subject D's mean is characteristically higher. Furthermore, the mean and standard deviation (SD) for both subjects C and D are plotted in areas where the quadratic approximation line exhibits strong nonlinearity. The plots in areas of strong nonlinearity are thought to indicate that the pharmacological effects of beta-blockers, such as reducing heart rate, are at work, and that this pharmacological effect is maintaining a balance in the physiological states of subjects C and D.

[0039] As described above, the shape and spatial structure of the attractor of heart rate variability differ depending on the pathology and medication effects. Therefore, by classifying the characteristics of the attractor shape using the mean (Mean) of the distance from the reference plane and the standard deviation (SD) of the distance, it is possible to classify the changes in circulatory dynamics due to the pathology and medication effects. Here, the following classification criteria 1 to 3 were set.

[0040] Classification criterion 1: Mean or SD is 10 or less, and the difference between the mean and SD is 1.5 or less. Classification criterion 2: Mean or SD is 10 or less, and the difference between the mean and SD is 1.5 or more. Classification criterion 3: Both mean and SD are 10 or more. Table 1 shows the classification results based on the above classification criteria for the correlation data using APW.

[0041] [Table 1]

[0042] The majority of the healthy group had classification criterion 1, while the majority of the outpatient group had classification criterion 3. Fisher's exact test showed a P value of 7.1 × 10 -7 In the outpatient group, classification criterion 1 was more common after classification criterion 3, and it is thought that the effect of medication was reflected in the increase in classification criterion 1.

[0043] Based on the above experimental results, the estimation unit 14 estimates the medication effect as follows, using the correlation data for estimation of the subject to be estimated. The attractor in this embodiment relates to heart rate variability created using time-series data of instantaneous heart rate, and the correlation data obtained from this attractor is analyzed by comparing the correlation data of subjects with cardiovascular disease with the correlation data of a healthy group, as described above. Therefore, in this embodiment, subjects for whom medication effects can be estimated are subjects with cardiovascular diseases such as hypertension and heart disease.

[0044] The estimation unit 14 first obtains time-series data of the instantaneous heart rate from the APW or ECG of the subject to be estimated who has a cardiovascular disease. Then, as described above, the attractor construction unit 11, the reference plane calculation unit 12, and the correlation data calculation unit 13 calculate correlation data for estimation relating to the reference plane and the average value and standard deviation of the distances between each point. The obtained correlation data for estimation is plotted against the correlation data of the outpatient group stored in the database.

[0045] The correlation data for the outpatient group can be estimated from the above experiment as follows. Estimation criterion 1: The correlation data for estimation is plotted in the approximate range of the approximate line for the healthy group (54-degree line in the case of ECG-based data, 45-degree line in the case of APW data). If the data is plotted in the approximate range, it is estimated that the medication has maintained hemodynamics close to that of the healthy group, and that the medication is highly effective.

[0046] Estimation criterion 2: When the correlation data for estimation is within the approximate range of the correlation data of the outpatient group, the plot position is plotted near the area with strong nonlinearity. If it is plotted near the area of ​​strong nonlinearity, it is estimated from the results of subjects C and D above that a state of balance is maintained by pharmacological action.

[0047] Estimation criterion 3: The correlation data for estimation is not within the approximate range of the correlation data for the outpatient group. In this case, it is estimated that the medication effect is low. For example, as shown in Figures 10(a) and (b), this is the data range (data marked P1 and P2 in the figure) that deviates significantly from the quadratic approximation line for the outpatient group. When such an estimation result is output, a change in medication can be considered. Note that Figure 10(a) is a correlation diagram of the mean and standard deviation (SD) of the distance between the attractor and each point for a total of 171 subjects (260 cases), consisting of 95 subjects in the healthy group and 76 subjects in the outpatient group, obtained using data obtained with a larger number of subjects than in the above experimental example. Figure 10(b) is an enlarged view of the range in Figure 10(a) where both the mean and standard deviation (SD) are 5 or less.

[0048] In the above estimation criteria 1 to 3, whether or not something is in the "approximation range" can be determined, for example, by setting a threshold value for the distance to the approximation line, and the strength of the nonlinearity can be specified, for example, by setting a threshold value for the curvature, and whether or not something is close to a nonlinear part of a predetermined strength can be determined, for example, by setting a threshold value for the distance between the two.

[0049] Alternatively, the estimation unit 14 may be configured to obtain correlation data for estimation from both ECG and APW data for the subject to be estimated, and compare the two correlation data with the correlation data obtained from the ECG ( FIG. 8( b) ) and the correlation data obtained from the APW ( FIG. 9( b) ), respectively, to estimate the medication effect. While the ECG corresponds to information about cardiac activity itself, the APW collects sound propagating through the body surface and therefore includes acoustic information originating from sources other than the heart. Comparing FIG. 8( b) and FIG. 9( b) reveals that the correlation data for each subject does not necessarily match, and the correlation data based on the APW is more dispersed than the correlation data based on the ECG. This is due to non-cardiac information contained in the APW. Therefore, for example, when the correlation data for estimation is plotted against the correlation data based on the ECG, it may fall within the approximate range of the healthy group, but when the correlation data is plotted against the correlation data based on the APW, it may not fall within the approximate range of the healthy group. This indicates the possibility that the pharmacological effect is not sufficient, and the accuracy of the estimation can be improved by configuring the system to estimate that the medication is highly effective only when both sets of data are within the approximate range of the healthy group.

[0050] Therefore, it is preferable that the estimation unit 14 is configured to obtain correlation data for estimation using both ECG and APW data, and make an estimation by comparing it with correlation data for both ECG and APW stored in the database.

[0051] As described above, in this embodiment, focusing on the fact that an attractor formed using time-series data of instantaneous heart rate of ECG or APW differs depending on the circulatory dynamics of a subject, a configuration for capturing the characteristics of the shape of the attractor is adopted. Therefore, by comparing the correlation data for estimation of the subject to be estimated with the correlation data of a group of healthy subjects (healthy group) and a group of subjects with cardiovascular disease (outpatient group), it is possible to estimate the medication effect of the subject to be estimated. [Explanation of symbols]

[0052] 1. Biological condition estimation device 11 Attractor component 12 Reference plane calculation section 13 Correlation data calculation unit 14 Estimation part 100 APW Sensor

Claims

1. an attractor construction unit that reconstructs a three-dimensional delay coordinate system from the time series data of the instantaneous heart rate using a Lorenz plot to obtain an attractor; a reference plane calculation unit that calculates a reference plane by performing plane approximation of the attractor using a least squares method; a correlation data calculation unit that calculates an average value of distances and a standard deviation of distances from the reference plane for each point of the instantaneous heart rate plotted on the three-dimensional delay coordinate system, and obtains correlation data between the average value of distances and the standard deviation of distances for a group of healthy subjects and a group of subjects with cardiovascular disease; an estimation unit that calculates correlation data for estimation regarding the average value of the distances and the standard deviation of the distances by the correlation data calculation unit, compares the correlation data for estimation with the correlation data, and estimates the medication effect on the subject; A biological state estimation device having the above configuration.

2. When the subject to be estimated has a cardiovascular disease, the estimation unit estimates that the "medication effect is high" if the correlation data for estimation is within an approximate range of the correlation data of the group of healthy subjects. The biological state estimation device according to claim 1.

3. When the subject to be estimated is a subject with a cardiovascular disease, if the correlation data for estimation is within an approximate range of the correlation data of the group of subjects with the cardiovascular disease, the estimation unit estimates the state of the pharmacological action caused by medication based on the position of the correlation data for estimation. The biological state estimation device according to claim 1.

4. When the subject to be estimated has a cardiovascular disease, the estimation unit estimates that the medication effect is low if the correlation data for estimation is not included in the approximate range of the correlation data of the group of subjects with cardiovascular disease. The biological state estimation device according to claim 1.

5. As the time series data of the instantaneous heart rate, electrocardiogram data or acoustic vibration data obtained from the body surface is used. The biological state estimation device according to claim 1.

6. two types of time-series data of the instantaneous heart rate are used, namely, electrocardiogram data and acoustic vibration data obtained from the body surface; the attractor construction unit finds an attractor for each of the attractors; the reference plane calculation unit finds a reference plane for each of the attractors; and the correlation data calculation unit creates correlation data for each of the attractors; The estimation unit estimates the medication effect on the subject based on each correlation data. The biological state estimation device according to claim 1.

7. A computer program that causes a computer to function as a biological state estimation device related to medication effects, A procedure for reconstructing a three-dimensional delayed coordinate system using Lorenz plots of time series data of instantaneous heart rate to obtain an attractor; a step of finding a plane as a reference plane by approximating the attractor to a plane using a least squares method; a step of calculating an average value of distances and a standard deviation of distances from the reference plane for each point of the instantaneous heart rate plotted on the three-dimensional delay coordinate system, and obtaining correlation data between the average value of distances and the standard deviation of distances for a group of healthy subjects and a group of subjects with cardiovascular disease; a step of obtaining correlation data for estimation regarding the average value of the distance and the standard deviation of the distance for the subject to be estimated, comparing the correlation data for estimation with the correlation data, and estimating the effect of medication on the subject to be estimated; A computer program that causes the computer to execute the above.

8. A computer-readable recording medium on which the computer program according to claim 7 is recorded.

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