Blood pressure estimation device, computer program, and recording medium

Abstract

To estimate blood pressure with higher accuracy.SOLUTION: A database of a three-dimensional response surface model is constructed by analyzing time-series data of instantaneous heart rates of a subject group, obtaining a first circulatory dynamics index related to a long-term blood pressure adjustment mechanism and a second circulatory dynamics index related to a short-term blood pressure adjustment mechanism in an arterial pressure-related frequency band, using the first circulatory dynamics index and the second circulatory dynamics index as explanatory variables, and using measured data of systolic blood pressure of the subject group as an objective variable, and the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation of the blood pressure estimation subject are applied to the three-dimensional response surface model to estimate the systolic blood pressure of the blood pressure estimation subject.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technique for estimating blood pressure based on information about heart rate. [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 pulse waves (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 waveform data obtained from an electrocardiograph at the same time to identify the waveform components from the ventricular filling period to the isovolumic contraction period. The Lorenz plot method is then 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 focuses on the fact that pulse wave velocity (PWV) depends on blood pressure and the elastic properties of arteries, and discloses a technique for measuring blood pressure by assuming that blood pressure is proportional to PWV. It also describes that PWV is calculated from PAT (Pulse Arrival Time). Because PWV is inversely proportional to the derivative of PAT, blood pressure is estimated by calculating the derivative value of PAT. It also describes that heart rate and oxygen saturation are measured using optical sensors used in mobile computers such as mobile phones.

[0005] Both Patent Documents 1 and 2 have in common the point that they are techniques capable of estimating blood pressure without using a cuff-based sphygmomanometer, i.e., an upper arm sphygmomanometer. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 2019-122502 [Patent Document 2] US2015 / 0320328A1 publication [Non-patent literature]

[0007] [Non-Patent Document 1] Guyton Physiology, 13th edition, published March 20, 2018, author John E. Hall, general translation: Yoshihiro Ishikawa et al., publisher: Elsevier Japan Co., Ltd. Summary of the Invention [Problem to be solved by the invention]

[0008] In Patent Document 1, the fractal slope is used as an index showing the fluctuation of heart rate variability. However, arterial pressure is not regulated by a single pressure control system, but by an integrated, multifaceted system that is interconnected. However, the fractal slope is an index that shows the characteristics of the analyzed waveform using only a single regression line, and it is difficult to say that it fully reflects the characteristics of the pressure control system for arterial pressure.

[0009] In Patent Document 2, blood pressure is calculated by detecting pulse wave velocity. Although pulse wave velocity is said to be correlated with blood pressure, the degree of correlation is not very high. Therefore, even if pulse wave velocity or pulse wave propagation delay time, which is used as a substitute for pulse wave velocity, is simply estimated by comparing it with blood pressure, there are concerns about its accuracy.

[0010] In view of the above, an object of the present invention is to provide a technique that can determine blood pressure (particularly systolic blood pressure) with higher accuracy.

[0011] In order to solve this problem, the present invention focuses on the following points. First, as mentioned above, arterial pressure is not regulated by a single pressure control system, but by an integrated, multifaceted system that is interconnected. Specifically, it is determined by the cooperative action of the renin-angiotensin-aldosterone system, i.e., the kidney-body fluid system, which constitutes the long-term blood pressure regulation mechanism (LTM), and the neural regulatory mechanisms responsible for short-term blood pressure regulation (STM) (Interaction with Additional Systems (IAAS)). For example, baroreceptor resetting, a short-term regulation, affects renal sympathetic nerve activity, which in turn contributes to long-term blood pressure regulation. A slight increase in cardiac output leads to an increase in total peripheral vascular resistance through some kind of autoregulation, resulting in a rise in blood pressure. Therefore, we focused on Guyton's theory that "if pulse pressure control is sufficiently strong and there is a delay between the excitation of the feedback function and the subsequent arterial pressure response, the feedback gain will increase, causing a delay in the reaction time of the pressure control mechanism and generating fluctuations in the low-frequency band. In other words, arterial pressure oscillates due to the excitation of the feedback function of the pressure control mechanism, operating on the same principle as mechanical and electrical systems" (see Non-Patent Document 1).

[0012] Next, we focused on chaotic time series analysis using a delayed coordinate system as a method for capturing reaction time delays (phase information), and with regard to fluctuations in the low-frequency band, we focused on the relationship between global and local fluctuations used in stochastic differential equations, as well as the fact that fluctuations in the arterial pressure regulation mechanism are reflected in fluctuations in heart rate variability.

[0013] To facilitate consideration of the mechanical and electrical system, the period of the arterial pressure regulation mechanism was converted to frequency. Frequency-related regulatory factors include the fundamental period of arterial pressure fluctuations (60 seconds, 1 / 60 ≒ 0.017 Hz) described in Non-Patent Document 1 and the baroreflex recovery time (21 seconds, 1 / 21 ≒ 0.0476 Hz) described in Guyton's previous study. The fundamental period of arterial pressure fluctuations is 0.017 Hz when linearity is maintained. However, when nonlinearity increases in hypotension or hypertension, harmonic components of 0.017 Hz, such as 0.034 Hz, 0.051 Hz, 0.068 Hz, 0.085 Hz, and 0.102 Hz, appear. Furthermore, while the frequency of the LF component is generally defined as 0.04 to 0.15 Hz, actual measurements have shown that it is primarily distributed between 0.06 and 0.11 Hz. Therefore, the operating band for short-term blood pressure control (STM) was defined as the frequency band up to 0.06 Hz, which includes the fundamental frequency of arterial pressure fluctuations of 0.017 Hz, its harmonic components of 0.034 Hz ​​and 0.051 Hz, and 0.0476 Hz related to the baroreceptor reflex.

[0014] On the other hand, frequencies below 0.017 Hz are the frequency band in which aldosterone functions, and are the operating band of the long-term blood pressure regulation mechanism (LTM). When arterial pressure drops, the baroreceptor reflex activates the sympathetic nervous system, which then increases aldosterone secretion within a few minutes (1 / 60 ≒ 0.017 Hz to 1 / 180 ≒ 0.0056 Hz), modifying the pressure control characteristics of the renal-humoral system. Fluctuations in the chemical reaction of aldosterone in the renal-humoral system are reflected in the frequency gradient of the frequency band below 0.017 Hz.

[0015] For these reasons, fluctuations in the long-term blood pressure regulation mechanism (LTM) will be examined mainly in the frequency band of 0.006 to 0.017 Hz, and fluctuations in the short-term blood pressure regulation mechanism (STM) will be examined mainly in the frequency band of 0.017 to 0.06 Hz. However, because this operating band is not constant due to individual differences, the frequency bands examined will be broadened as shown in Figure 9, i.e., LTM will be examined between 0.006 Hz and IP2 (IP2 is 0.017 Hz or higher), and STM will be examined between IP1 and 0.06 Hz (IP1 is 0.017 Hz or lower). [Means for solving the problem]

[0016] That is, the blood pressure estimation device of the present invention comprises: a database construction unit that analyzes time-series data of instantaneous heart rates of a predetermined number of subjects, determines a first circulatory dynamics index related to the long-term blood pressure regulation mechanism (LTM) and a second circulatory dynamics index related to the short-term blood pressure regulation mechanism (STM) in an arterial pressure-related frequency band, and constructs a database of a three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index as explanatory variables and the measured data of the systolic blood pressure of each of the subjects as a response variable; an estimation index calculation unit that analyzes time series data of the instantaneous heart rate of the subject whose blood pressure is to be estimated, calculates the first circulatory dynamics index and the second circulatory dynamics index of the subject whose blood pressure is to be estimated, and sets the first circulatory dynamics index and the second circulatory dynamics index for estimation, respectively; a systolic blood pressure estimation unit that applies the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation to the three-dimensional response surface model constructed by the database construction unit, and estimates the systolic blood pressure of the subject whose blood pressure is to be estimated; It has.

[0017] The database construction unit A two-dimensional delay coordinate system is reconstructed by applying a Lorenz plot to the time series data of the instantaneous heart rate of the predetermined number of subjects, an attractor in a reference time range is created, reference angle information (θ0) which is the slope of the regression line of the attractor, and individual attractors in the two-dimensional delay coordinate system in individual time ranges shorter than the reference time range are created, and individual angle information (θ i ) and the difference (θ i-0 ) is calculated sequentially while moving the individual time range, and the difference (θ i-0 ) time series data analysis unit for obtaining time series data; The difference (θ i-0 ) time series data and displays a spectrum diagram on a double logarithmic graph; an approximation curve calculation unit that sets two types of approximation curves, a quartic function and a quintic function, for the arterial blood pressure-related frequency band in the spectrogram; a best approximation curve selection unit that selects a best approximation curve to be used for analysis from the two types of approximation curves, the quartic function and the quintic function; an inflection point and intersection point extraction unit that extracts inflection points and intersection points of tangents for the best approximation curve; an approximate gradient curve setting unit that sets two types of approximate gradient curves, a cubic function and a quartic function, as approximate gradient curves for each frequency band related to all combinations of two frequencies selected from the frequency of the inflection point, the frequency of the intersection of the tangent lines, and the lower and upper limits of the arterial pressure-related frequency band, in each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism within the arterial pressure-related frequency band; an optimized approximate gradient curve selection unit that selects an optimized approximate gradient curve to be used for analysis from the two types of approximate gradient curves, the cubic function and the quartic function, in each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism; a circulatory dynamics index determining unit that determines the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the long-term blood pressure regulation mechanism as the first circulatory dynamics index and the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the short-term blood pressure regulation mechanism as the second circulatory dynamics index; a three-dimensional response surface model creation unit that creates the three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index; It is preferable to have a configuration having the above.

[0018] The estimation index calculation unit It is preferable that the time series data of the instantaneous heart rate of the subject whose blood pressure is to be estimated is analyzed by the time series data analysis unit, the frequency analysis unit, the approximation curve calculation unit, the best approximation curve selection unit, the inflection point / intersection extraction unit, the approximation gradient curve setting unit, the optimized approximation gradient curve selection unit, and the circulatory dynamics index determination unit to determine a first circulatory dynamics index and a second circulatory dynamics index of the subject whose blood pressure is to be estimated, and these are set as the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation, respectively.

[0019] The circulatory dynamics index determination unit When the optimized approximate curve is an approximate gradient curve represented by the cubic function, the gradient of the tangent to the inflection point is defined as the frequency gradient; When the optimized approximate curve is an approximate gradient curve represented by the quartic function, it is preferable that the gradient of the bitangent thereof is used as the frequency gradient.

[0020] The best approximation curve selection unit The following evaluation indexes are related to the residuals between the two types of approximation curves of the quartic function and the quintic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; and Distribution shape of the residuals It is preferable that the best approximation curve is selected using at least one of the above. The optimized approximate gradient curve selection unit The following evaluation indexes are related to the residuals between the two types of approximation curves of the cubic function and the quartic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; The distribution shape of the residuals, and Correlation between the standard deviation of the residuals and the best approximation error It is preferable to use at least one of the above to select the optimized approximate gradient curve. The optimized approximate gradient curve selection unit It is preferable to determine whether an overlap band (OLF) modified by both the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) exists at a predetermined position in the selected optimized approximate gradient curve, and if it does not exist, to recalculate the spectrum diagram by excluding outliers included in the long-term blood pressure regulation mechanism (LTM).

[0021] The computer program of the present invention comprises: A computer program that causes a computer to function as a blood pressure estimation device, a step of analyzing time series data of instantaneous heart rates of a predetermined number of subjects, determining a first circulatory dynamics index related to the long-term blood pressure regulation mechanism (LTM) and a second circulatory dynamics index related to the short-term blood pressure regulation mechanism (STM) in the arterial pressure-related frequency band, and constructing a database of a three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index as explanatory variables and the actual measured data of the systolic blood pressure of each of the subjects as a response variable; a step of analyzing time series data of the instantaneous heart rate of the subject whose blood pressure is to be estimated, determining the first circulatory dynamics index and the second circulatory dynamics index of the subject whose blood pressure is to be estimated, and setting the first circulatory dynamics index and the second circulatory dynamics index for estimation, respectively; applying the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation to the three-dimensional response surface model to estimate the systolic blood pressure of the subject whose blood pressure is to be estimated; The computer is caused to execute the following.

[0022] In the step of constructing the database, A two-dimensional delay coordinate system is reconstructed by applying a Lorenz plot to the time series data of the instantaneous heart rate of the predetermined number of subjects, an attractor in a reference time range is created, reference angle information (θ0) which is the slope of the regression line of the attractor, and individual attractors in the two-dimensional delay coordinate system in individual time ranges shorter than the reference time range are created, and individual angle information (θ i ) and the difference (θ i-0 ) is calculated sequentially while moving the individual time range, and the difference (θ i-0 ) and the procedure for obtaining time series data. The difference (θ i-0 ) time series data and display the spectrum diagram on a log-log graph. a step of setting two types of approximation curves, a quartic function and a quintic function, for the arterial blood pressure-related frequency band in the spectrogram; a step of selecting a best approximation curve to be used for analysis from the two types of approximation curves, the quartic function and the quintic function; extracting inflection points and tangent intersection points for the best approximation curve; a step of setting two types of approximate curves, a cubic function and a quartic function, as approximate gradient curves for each frequency band relating to all combinations of two frequencies selected from the frequency of the inflection point, the frequency of the intersection of the tangent lines, and the lower and upper limits of the arterial pressure-related frequency band, in each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism within the arterial pressure-related frequency band; selecting an optimized approximate gradient curve to be used for analysis from the two approximate gradient curves, the cubic function and the quartic function, for each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism; a step of setting the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the long-term blood pressure regulation mechanism as the first circulatory dynamics index, and setting the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the short-term blood pressure regulation mechanism as the second circulatory dynamics index; creating the three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index; It is preferable to perform the following.

[0023] In the step of calculating the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation of the blood pressure estimation subject, The method executes steps of obtaining time series data of the instantaneous heart rate of the subject for whom blood pressure is to be estimated, performing the frequency analysis and displaying a spectrogram, setting the approximation curve, selecting the best approximation curve, extracting the inflection points and tangent intersections, setting the approximation gradient curve, and selecting the optimized approximation gradient curve, It is preferable to determine a first circulatory dynamics index and a second circulatory dynamics index of the subject whose blood pressure is to be estimated, and use these as a first circulatory dynamics index for estimation and a second circulatory dynamics index for estimation, respectively.

[0024] In the step of determining the first circulatory dynamics index and the second circulatory dynamics index, When the optimized approximate curve is an approximate gradient curve represented by the cubic function, the gradient of the tangent to the inflection point is defined as the frequency gradient; When the optimized approximate curve is an approximate gradient curve represented by the quartic function, it is preferable that the gradient of the bitangent thereof is used as the frequency gradient.

[0025] In the step of selecting the best approximation curve, The following evaluation indexes are related to the residuals between the two types of approximation curves of the quartic function and the quintic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; The distribution shape of the residuals, and Correlation between the standard deviation of the residuals and the best approximation error It is preferable to select the best approximation curve using at least one of the following:

[0026] In the step of selecting the optimized approximate gradient curve, The following evaluation indexes are related to the residuals between the two types of approximation curves of the cubic function and the quartic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; and Distribution shape of the residuals It is preferable to select the optimized approximate gradient curve using at least one of the following: In the step of selecting the optimized approximate gradient curve, It is preferable to determine whether an overlap band (OLF) modified by both the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) exists at a predetermined position in the selected optimized approximate gradient curve, and if it does not exist, to recalculate the spectrum diagram by excluding outliers included in the long-term blood pressure regulation mechanism (LTM).

[0027] The present invention also provides a computer-readable recording medium on which the computer program is recorded. [Effects of the Invention]

[0028] According to the present invention, time-series data of instantaneous heart rates of a group of subjects are analyzed to determine a first circulatory dynamics index associated with the long-term blood pressure regulatory mechanism and a second circulatory dynamics index associated with the short-term blood pressure regulatory mechanism in the arterial pressure-related frequency band. A database of three-dimensional response surface models is constructed using the first and second circulatory dynamics indexes as explanatory variables and the measured systolic blood pressure data of each of the group of subjects as a response variable. The first and second circulatory dynamics indexes for estimation of blood pressure of a subject are applied to this three-dimensional response surface model to estimate the systolic blood pressure of the subject. Because the first circulatory dynamics index (first circulatory dynamics index for estimation) and the second circulatory dynamics index (second circulatory dynamics index for estimation) correspond to the long-term blood pressure regulatory mechanism and the short-term blood pressure regulatory mechanism, respectively, the estimated systolic blood pressure reflects the influence of both mechanisms, improving the accuracy of blood pressure estimation. [Brief explanation of the drawings]

[0029] [Figure 1] FIG. 1 is a block diagram showing the overall configuration of a blood pressure estimation device according to a first embodiment of the present invention. [Figure 2] FIG. 2 is a block diagram showing the configuration of the database construction unit. [Figure 3] FIG. 3 is a flowchart illustrating the analysis procedure of the time-series data analysis unit. [Figure 4] Figure 4(a) is a diagram showing electrocardiogram waveform data to be analyzed by the time series data analysis unit, Figures 4(b) to 4(d) are diagrams showing output data obtained according to the analysis procedure of the time series data analysis unit, and Figure 4(e) is a spectrum diagram obtained by the frequency analysis unit. [Figure 5] FIG. 5 is a flowchart for explaining the procedure for setting an approximation curve on a spectrogram, obtaining a best approximation curve, and extracting inflection points and tangent intersections. [Figure 6]FIG. 6 is a flowchart for explaining the procedure from the procedure in FIG. 5 until the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) are obtained. [Figure 7] 7(a) to 7(d) are diagrams showing an example of data obtained by setting an approximation curve on a spectrogram, obtaining a best approximation curve, and extracting inflection points and tangent intersections in accordance with the flowchart of FIG. 5 in Experimental Example 1. [Figure 8] 8(a) to 8(d) are diagrams showing other examples of data obtained by setting an approximation curve on a spectrogram, obtaining a best approximation curve, and extracting inflection points and tangent intersections in accordance with the flowchart of FIG. 5 in Experimental Example 1. [Figure 9] Figure 9 shows the frequency band of the long-term blood pressure regulation mechanism (LTM) from which the first circulatory dynamics index (GT of LTM) is obtained, and the frequency band of the short-term blood pressure regulation mechanism (STM) from which the second circulatory dynamics index (GT of STM) is obtained. [Figure 10] 10(a) and (b) show data obtained by determining an optimized approximate gradient curve for the long-term blood pressure regulation mechanism (LTM) from the best approximate curve obtained in FIG. 7(d). [Figure 11] 11(a) and (b) show data obtained by determining an optimized approximate gradient curve for the short-term blood pressure regulation mechanism (STM) from the best approximate curve obtained in FIG. 7(d). [Figure 12] FIG. 12 is a SD-εn correlation diagram for the residuals of the approximate gradient curves obtained in FIGS. 10(a), (b) and 11(a), (b). [Figure 13] 13(a) and (b) show data obtained by determining an optimized approximate gradient curve for the long-term blood pressure regulation mechanism (LTM) from the best approximate curve obtained in FIG. 8(d). [Figure 14] 14(a) and (b) show data obtained by determining an optimized approximate gradient curve for the short-term blood pressure regulation mechanism (STM) from the best approximate curve obtained in FIG. 8(d). [Figure 15]FIG. 15 is a SD-εn correlation diagram for the residuals of the approximate gradient curves obtained in FIGS. 13(a) and 13(b) and FIGS. 14(a) and 14(b). [Figure 16] Figure 16(a) shows the best approximation curve obtained in Figure 7(d), as well as the optimized approximation slope curve for the long-term blood pressure regulation mechanism (LTM) and the optimized approximation slope curve for the short-term blood pressure regulation mechanism (STM) obtained in Figures 10(a), (b) and 11(a), (b). Figure 16(b) shows the frequency slope (GT of LTM), which is the first circulatory dynamics index obtained from the optimized approximation slope curve for the long-term blood pressure regulation mechanism (LTM), and the frequency slope (GT of STM), which is the second circulatory dynamics index obtained from the optimized approximation slope curve for the short-term blood pressure regulation mechanism (STM). [Figure 17] Figure 17(a) shows the best approximation curve obtained in Figure 8(d), as well as the optimized approximation slope curve for the long-term blood pressure regulation mechanism (LTM) and the optimized approximation slope curve for the short-term blood pressure regulation mechanism (STM) obtained in Figures 13(a), (b) and 14(a), (b). Figure 17(b) shows the frequency slope (GT of LTM), which is the first circulatory dynamics index obtained from the optimized approximation slope curve for the long-term blood pressure regulation mechanism (LTM), and the frequency slope (GT of STM), which is the second circulatory dynamics index obtained from the optimized approximation slope curve for the short-term blood pressure regulation mechanism (STM). [Figure 18] FIG. 18 is a diagram showing the three-dimensional response surface model obtained in Experimental Example 1. [Figure 19] 19(a) is a correlation diagram between GT of LTM and systolic blood pressure in the three-dimensional response surface model of FIG. 18, and FIG. 19(b) is a correlation diagram between GT of STM and systolic blood pressure in the three-dimensional response surface model of FIG. [Figure 20] Figure 20(a) is a correlation diagram between GT of LTM and GT of STM, and Figure 20(b) is a correlation diagram between the estimated systolic blood pressure (eSBP) estimated using the three-dimensional response surface model of Figure 18 and each measured systolic blood pressure (SBP). [Figure 21]FIG. 21(a) shows data from a 34-year-old subject in Experimental Example 2, and FIG. 21(b) shows data from a 67-year-old subject in Experimental Example 2. [Figure 22] 22(a) to 22(c) are diagrams showing an image of the relationship between the long-term blood pressure regulation mechanism (LTM), the short-term blood pressure regulation mechanism (STM), and the overlap band (OLF). [Figure 23] FIG. 23 is a diagram showing a configuration in which an OLF requirement determination unit 171 according to the second embodiment is provided. [Figure 24] FIG. 24 is a flowchart showing the processing procedure of the second embodiment. [Figure 25] 25(a) to 25(c) are diagrams showing an example of a processing procedure according to the second embodiment. [Figure 26] Figures 26(a) to (f) show examples of approximation curves (approximation gradient curves) of a cubic or quartic function in the operating band of the long-term blood pressure regulation mechanism (LTM) among the best approximation curves of the quintic function in Figure 25(c). [Figure 27] Figures 27(g) to (m) show examples of the best approximation curves of the quintic functions in Figure 25(c), in which the cubic or quartic function approximation curves (approximation gradient curves) were obtained in the operating band of the short-term blood pressure regulation mechanism (STM). [Figure 28] FIG. 28 is a SD-εn correlation diagram for each of the approximate gradient curves in FIGS. [Figure 29] Figure 29(a) shows the approximate gradient curve of a cubic function for the frequency band to be analyzed from 0.006 Hz to IP2 for LTM, the approximate gradient curve of a quartic function for IT2 to 0.06 Hz for STM, and the best approximate curve of a quintic function, and Figure 29(b) shows the frequency band to be analyzed for LTM, the frequency band to be analyzed for STM, and the frequency band for OLF. [Figure 30] FIG. 30 is a diagram showing the three-dimensional response surface model obtained in Experimental Example 3. [Figure 31] 31(a) is a correlation diagram between GT of LTM and systolic blood pressure in the three-dimensional response surface model of FIG. 30, and FIG. 31(b) is a correlation diagram between GT of STM and systolic blood pressure in the three-dimensional response surface model of FIG. [Figure 32] Figure 32(a) is a correlation diagram between GT of LTM and GT of STM, and Figure 32(b) is a correlation diagram between the estimated systolic blood pressure (eSBP) estimated using the three-dimensional response surface model of Figure 30 and each measured systolic blood pressure (SBP). [Figure 33] 33(a) to (c) are diagrams showing examples of analysis of other subjects in Experimental Example 3. In FIG. [Figure 34] FIG. 34 shows an example of analysis of the subject (c-1) in Experimental Example 3. [Figure 35] FIG. 35 shows an example of analysis of subject (c-2) in Experimental Example 3. [Figure 36] FIG. 36 shows an example of analysis of the subject (c-3) in Experimental Example 3. [Figure 37] FIG. 37 shows an example of analysis of subject (c-5) in Experimental Example 3. [Figure 38] FIG. 38 shows an example of analysis of subject (c-6) in Experimental Example 3. [Figure 39] FIG. 39 shows data for a 67-year-old subject in Experimental Example 4. DETAILED DESCRIPTION OF THE INVENTION

[0030] (First embodiment) [Blood Pressure Estimation Device 1] FIG. 1 shows a schematic configuration of a blood pressure estimation device 1 according to this embodiment. The blood pressure estimation device 1 according to 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, and the like) and executes predetermined processes. Specifically, the blood pressure estimation device 1 is configured by storing computer programs in a storage unit (including a storage medium such as a hard disk built into the blood pressure estimation device 1, various removable storage media, and storage media of other computers connected via communication means) that execute procedures functioning as a database construction unit 10, an estimation index calculation unit 20, and a systolic blood pressure estimation unit 30. The blood pressure estimation device 1 can also be realized using an electronic circuit having one or more storage circuits incorporating the computer programs. That is, the blood pressure estimation device 1 can be configured by processing circuits functioning as the database construction unit 10, the estimation index calculation unit 20, and the systolic blood pressure estimation unit 30.

[0031] 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.

[0032] [Database Construction Department 10] The database construction unit 10 analyzes the time series data of instantaneous heart rates of a predetermined number of subjects, calculates a first circulatory dynamics index (GT of LTM) related to the long-term blood pressure regulation mechanism (LTM) and a second circulatory dynamics index (GT of STM) related to the short-term blood pressure regulation mechanism (STM) in the arterial pressure-related frequency band, and constructs a database of a three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index as explanatory variables and the actual measured data of the systolic blood pressure of each of the above subject groups as the objective variable.

[0033] More specifically, the computer program functioning as the database construction unit 10 executes procedures functioning as a time-series data analysis unit 11, a frequency analysis unit 12, an approximation curve calculation unit 13, a best approximation curve selection unit 14, an inflection point / intersection extraction unit 15, an approximation gradient curve setting unit 16, an optimized approximation gradient curve selection unit 17, a circulatory dynamics index determination unit 18, and a three-dimensional response surface model creation unit 19, as shown in FIG. 2 .

[0034] (Time Series Data Analysis Section 11) The time series data analysis unit 11 reconstructs a two-dimensional delay coordinate system for the time series data of the instantaneous heart rate (HR=60 / Δt) using a Lorenz plot, and finds 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 (ECG) (S1 in Fig. 3, Fig. 4(a)). The Lorenz plot uses two points from the time series data of the instantaneous heart rate (HR), and plots them while moving the range. Specifically, the x-axis is plotted with HR i+1 (HR i+2 , H.R. i+3 ), HR on the y-axis i (HR i+1 , H.R. i+2 ...) are taken, and their intersections are plotted sequentially. This is performed for a reference time range (360 seconds in this embodiment), and an attractor for the reference time range formed by the plots is created. Next, a regression line of the attractor for this reference time range is obtained. This regression line indicates the ensemble average of the plots for the reference time range, and the slope of the regression line with respect to the x-axis is obtained as reference angle information (θ0) as an index showing its characteristics (S2 in FIG. 3, FIG. 4(b)).

[0035] Next, the time series data analysis unit 11 reconstructs a two-dimensional delay coordinate system using the Lorenz plot in the same manner as above for an individual time range (30 seconds in this embodiment) shorter than the reference time range, and creates an individual attractor. Then, as an index showing the characteristics of the slope of the regression line, which is the ensemble average of the plot group that constitutes the individual attractor, the slope of the regression line with respect to the x-axis is calculated as individual angle information (θ i) is calculated as individual angle information (θ i ) are sequentially obtained while moving the individual time range (30 seconds) at a predetermined overlap rate (for example, 90%) (S2 in FIG. 3, FIG. 4(c)). Next, the time series data analysis unit 11 calculates the reference angle information (θ0) and each individual angle information (θ i ) and the difference (θ i-0 ) is calculated sequentially, and the difference (θ i-0 ) time series data (θ i-0 (time series data) is calculated (S3 in Fig. 3, Fig. 4(d)). i-0 The time series data can be considered as an index showing the general fluctuation of individual attractors obtained every 3 seconds in the case of 90% overlap rate.

[0036] θ i-0 As shown in Figure 4(d), the time series data has the horizontal axis as time and the vertical axis as angle information of increasing or decreasing tendency (- display indicates an increasing tendency of instantaneous heart rate and an increasing tendency of sympathetic nervous system, and + display indicates a decreasing tendency of instantaneous heart rate and an inhibiting tendency of sympathetic nervous system). In the case of 90% overlap rate, the difference (θ i-0 ) is plotted as a time series graph. i-0 ) should preferably be plotted every 3 seconds. This is the average time it takes for the effects of parasympathetic nervous system activation and inhibition to be reflected, and 30 seconds is approximately twice the average time (12.5 seconds) of 10-15 seconds, over which the effects of sympathetic nervous system activation and inhibition are reflected. 360 seconds corresponds to 1 / 4 cycle of fluctuations in alertness, drowsiness, and mental work capacity in the ultradian cycle. Note that a -+ display indicates a tendency to suppress blood pressure, while a + indicates a tendency to suppress blood pressure.

[0037] (Frequency analysis unit 12) The frequency analysis unit 12 analyzes the θ i-0 This is a means of frequency analysis of time series data and displaying it on a double logarithmic axis (S4, S5 in Fig. 3, Fig. 4(e)). Fig. 4(e) is an example of a double logarithmic graph display (spectral diagram) of the power spectrum as a result of frequency analysis, with many points plotted.

[0038] In this embodiment, frequency analysis was performed with a sampling frequency of 1000 Hz and a time resolution of 0.001 seconds. Therefore, the effective band in the calculation is up to 1000 / 2.5 = 400 Hz. Spectral analysis is performed in 2 n When performing spectral analysis using 360 seconds of time series data, 7 =128 is close to this condition. As mentioned above, the moving average in the time series data analysis unit 11 is performed with a 30-second window shifted by 3 seconds, and the first point can be calculated after 30 seconds has passed. The heart rate regulation function and respiratory regulation function are reflected in the moving average performed at 30-second intervals. Therefore, (360-30) / 3+1=111 data are output in 360 seconds. In order to perform the spectrum calculation, 2 7 Since 128 data points are required, the missing 17 data points are folded back. Assuming that the response of the sympathetic nervous system to stimulation is captured in 12.5 seconds, low-pass filtering is performed at 1 / 12.5 = 0.08 Hz. The result is 1 / 360 = 0.0028 Hz, but since the power spectrum near 0.0028 Hz contains drift and offset components, high-pass filtering is performed at 0.006 Hz, above 0.0028 Hz x 2 = 0.0056 Hz. As a result, the above frequency band related to the long-term blood pressure regulation mechanism (LTM) covers approximately 70% of the VLF band, which is the frequency band from 0.003 to 0.04 Hz.

[0039] The spectrum diagram obtained by the frequency analysis unit 12 is expressed as a logarithmic graph with the horizontal axis representing frequency and the vertical axis representing power spectrum, so the graph slopes downward to the right. n The periodic component creates a large spectrum and produces extreme values ​​on the graph.

[0040] (Approximate curve calculation unit 13) The approximation curve calculation unit 13 performs a calculation to fit an approximation curve to the spectrum diagram of the power spectrum plotted on a double logarithmic graph obtained by the frequency analysis unit 12. Here, the frequency analysis unit 12 performs processing using a high-pass filter at 0.006 Hz and a low-pass filter at 0.08 Hz. However, as described above, the operating band of the long-term blood pressure regulation mechanism (LTM) is centered on the frequency band of 0.006 to 0.017 Hz, and the operating band of the short-term blood pressure regulation mechanism (STM) is centered on the frequency band of 0.017 to 0.06 Hz, so 0.006 to 0.06 Hz is defined as the "arterial pressure-related frequency band."

[0041] The approximation curve calculation unit 13 calculates and sets two types of approximation curves, a quartic function and a quintic function, for the spectrogram in the arterial pressure-related frequency band of 0.006 to 0.06 Hz (S11 in FIG. 5). In the analysis example described below, approximation curve A in FIGS. 7(a) and 8(a) is an example of an approximation curve represented by a quartic function, and approximation curve B (B-1, B-2) is an example of an approximation curve represented by a quintic function. The arterial pressure-related frequency band of 0.006 to 0.06 Hz covers the operating bands of the long-term blood pressure regulating mechanism (LTM) and the short-term blood pressure regulating mechanism (STM), and this range indicates the overall tendency of arterial pressure control. Furthermore, the operating bands of the long-term blood pressure regulating mechanism (LTM) and the short-term blood pressure regulating mechanism (STM) are not constant due to individual differences and the influence of the patient's physical condition at the time of measurement. The lower limit frequency of 0.006 Hz and the upper limit frequency of 0.06 Hz can be limited in consideration of the accuracy of frequency analysis as described above, but the boundary between the two operating bands is not necessarily 0.017 Hz. Therefore, within each operating band of the long-term blood pressure regulation mechanism (LTM) or short-term blood pressure regulation mechanism (STM), the frequency band to be analyzed for determining each circulatory dynamics index is not constant due to differences between individuals and differences in physical condition.

[0042] Therefore, the approximation curve calculation unit 13 sets two types of approximation curves, a quartic function and a quintic function, for the entire range of 0.006 to 0.06 Hz, which is the arterial pressure-related frequency band, so that a more appropriate band can be selected as the analysis target frequency band for determining each circulatory dynamics index for each data to be analyzed, and selects one of them as the best approximation curve.

[0043] This is because two or more inflection points appear, making it easy to identify characteristic points (inflection points and tangent intersections, described below) that limit the frequency band to be analyzed. The basic form of the approximation curve is a quartic function, in which the coefficient term to the fourth power is negative; when the graph shape is reversed, the coefficient becomes positive. When the coefficient is positive, the slope of the tangent line to the inflection point (frequency gradient) becomes positive, suggesting enhanced accommodation function. Furthermore, the graph of a quartic function has diagonal symmetry, with three extrema being the basic form. It can be divided into cases where two extrema are close together and the number of extrema decreases, and cases where two extrema are outside the frequency band to be analyzed. Generally, if the approximation curve of an nth-order function does not follow a normal distribution in terms of the residuals described below, increasing the order increases the likelihood that it will follow a normal distribution. Furthermore, if outliers occur in the residuals between the power spectrum and the approximation curve, the frequency band to be analyzed is changed or the order is increased to 5th, and an approximation curve is created, and the inflection points IP and intersection points IT are calculated.

[0044] However, in this embodiment, instead of fitting an approximate curve of a quartic function and then finding an approximate curve of a quintic function in light of the distribution of residuals, the two types of functions, a quartic function and a quintic function, are compared from the beginning to quickly obtain an approximate curve (best approximate curve) appropriate for capturing the overall trend of arterial blood pressure control.

[0045] (Best approximation curve calculation unit 14) The best approximation curve selection unit 14 selects an approximation curve (best approximation curve) to be used for analysis from two types of approximation curves, a quartic function and a quintic function, calculated by the approximation curve calculation unit 13 (S12 in FIG. 5). The selection criteria preferably use an evaluation index related to the residual between each of the two types of approximation curves, a quartic function and a quintic function, and the spectrogram (the difference between the power spectrum plot and each approximation curve). The evaluation index related to the residual can be at least one of the following: the standard deviation (SD) of the residuals calculated for each power spectrum plot in the spectrogram; the mean value (Mean(abs)) of the absolute values ​​of the residuals; the best approximation error (εn), which is the difference (SD-Mean(abs)) between the standard deviation (SD) of the residuals and the mean value (Mean(abs)) of the absolute values ​​of the residuals; and the distribution shape of the residuals (whether the distribution is symmetrical or has a wider tail on either the left or right).

[0046] In this embodiment, among the evaluation indices, the standard deviation (SD) of the residuals, the best approximation error (εn), and the distribution shape of the residuals are used as selection criteria. Specifically, first, an SD-εn correlation diagram showing the correlation between the standard deviation (SD) of the residuals and the best approximation error (εn) is created, and a histogram of the residuals is created. Then, as shown in S12 and S13 of FIG. 5, an approximation curve of either a quartic or quintic function whose histogram has a symmetrical distribution or a distribution with a broad tail on either the left or right, and whose best approximation error εn → 0 (in this specification, εn means 0 or close to 0), is selected as the best approximation curve (see FIGS. 7(b), (c), and 8(b), (c)).

[0047] Here, the reason why the best approximation error εn is included in the selection criteria will be explained. In the interval (a, b), the polynomial approximation function g(x) satisfies the normal distribution and is easy to handle numerically. i} i=1 n The value at {gx i} i=1 n Select an n-th order function f(x) for

[0048] For a function g(x) that is continuous in the interval (a, b), the k-th degree polynomial f k Made using (x)

number

[0049] Also, if the random variable X follows a normal distribution, then the probability density of the best approximation function f(x) is

number

[0050] A function that is continuous on a closed interval follows the Weierstrass theorem, a fundamental theorem of approximation theory, and the normal distribution, n-th order function f(x) and selection {x i} i=1 n From the residuals, which are the errors between the two, the standard deviation (SD) of the residuals and the mean value of the absolute values ​​of the residuals (Mean(abs)) were calculated, and SD-Mean(abs) was set as εn. The selection criteria for the best approximation curve were that εn → 0 and SD be as small as εn.

[0051] (Inflection point / intersection point extraction part 15) The inflection point and intersection extraction unit 15 extracts inflection points and tangent intersections for the best approximation curve. That is, the selected best approximation curve is differentiated to calculate inflection points (IP), and the intersections of tangents (IT) at the inflection points are obtained. If it is determined in S14 of FIG. 5 that a quartic function has been selected as the best approximation curve, IP and IT will be IP1, IT1, and IP2 in order from the low frequency side (see S15 of FIG. 5 and FIG. 8(d)). If it is determined in S14 of FIG. 5 that a quintic function has been selected as the best approximation curve, IP and IT will be IP1, IT1, IP2, IT2, and IP3 in order from the low frequency side (see S16 of FIG. 5 and FIG. 7(d)).

[0052] (Approximate gradient curve setting unit 16) The approximate gradient curve setting unit 16 calculates two types of approximate curves, a cubic function and a quartic function, for each frequency band corresponding to all combinations of the frequency of the inflection point (IPn), the frequency of the tangent intersection (ITn), and two frequencies selected from the lower limit 0.006 Hz and the upper limit 0.06 Hz of the arterial pressure-related frequency band in each of the operating bands of the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) (S21 in FIG. 6). These approximate curves are referred to herein as "approximate gradient curves."

[0053] As mentioned above, the operating bands of the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) are bounded by approximately 0.017 Hz, but this is not constant due to individual differences and physical condition. Therefore, the operating band of the long-term blood pressure regulation mechanism (LTM) is defined as the range from the low-frequency side to the second inflection point (IP2) in the case of a quartic function, which is 0.006 Hz or higher and includes 0.017 Hz. In the case of a quintic function, the range considered is from the low-frequency side to the second tangent intersection point (IT2) located next to the second inflection point (IP2). The operating band of the short-term blood pressure regulation mechanism (STM) is defined as the range from the lowest-frequency inflection point (IP1) to 0.06 Hz for both the quartic and quintic functions, which includes 0.017 Hz.

[0054] Figure 9 is a conceptual diagram showing the relationship between the inflection points (IPn) and the tangent intersection points (ITn) in the operating bands of the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM). The specific frequency bands to be considered for application of the approximate gradient curves represented by cubic and quartic functions are as follows:

[0055] (1) Operating band of long-term blood pressure regulation mechanism (LTM) (1-a) When the best fitted curve is a quartic function: 0.006Hz~IP1, 0.006Hz~IT1, 0.006Hz~IP2, IP1~IT1, IP1~IP2, IT1~IP2 (Number of frequency bands considered: 6) (1-b) When the best fitted curve is a quintic function: 0.006Hz~IP1, 0.006Hz~IT1, 0.006Hz~IP2, 0.006Hz~IT2, IP1~IT1, IP1~IP2, IP1~IT2, IT1~IP2, IT1~IT2, IP2~IT2 (Number of frequency bands considered: 10)

[0056] (2) Operating range of short-term blood pressure regulation mechanism (STM) (2-a) When the best fitted curve is a quartic function: IP1~IT1, IP1~IP2, IP1~0.06Hz, IT1~IP2, IT1~0.06Hz, IP2~0.06Hz (number of frequency bands considered: 6) (2-b) When the best fitted curve is a quintic function: IP1~IT1, IP1~IP2, IP1~IT2, IP1~IP3, IP1~0.06Hz, IT1~IP2, IT1~IT2, IT1~IP3, IT1~0.06Hz, IP2~IT2, IP2~IP3, IP2~0.06Hz, IT2~IP3, IT2~0.06Hz, IP3~0.06Hz (Number of frequency bands considered: 15)

[0057] (Optimization approximate gradient curve selection unit 17) The optimized approximate gradient curve selection unit 17 selects the optimized approximate gradient curve, which is the approximate gradient curve for calculating the first circulatory dynamics index and the second circulatory dynamics index, from all of the two types of approximate gradient curves, the cubic function and the quartic function, calculated in each of the above frequency bands (S22 in Figure 6).

[0058] As a selection criterion for the optimized approximate gradient curve, it is preferable to use an evaluation index related to the residual between each approximate gradient curve and the spectrogram (the difference between the power spectrum plot and each approximate gradient curve). As an evaluation index related to the residual, similar to the case of selecting the best approximate curve, at least one of the following can be used: the standard deviation (SD) of the residuals obtained for each power spectrum plot in the spectrogram, the mean value (Mean(abs)) of the absolute values ​​of the residuals, the best approximation error (εn) which is the difference (SD-Mean(abs)) between the standard deviation (SD) of the residuals and the mean value (Mean(abs)) of the absolute values ​​of the residuals, and the distribution shape of the residuals (whether the distribution is symmetrical or has wider tails on either the left or right side).

[0059] In this embodiment, among the above evaluation indices, the standard deviation (SD) of the residuals, the best approximation error (εn), and the distribution shape of the residuals are used as selection criteria. Specifically, first, an SD-εn correlation diagram showing the correlation between the standard deviation (SD) of the residuals and the best approximation error (εn) is created, and a histogram of the residuals is also created. Then, an approximation gradient curve of either a cubic or quartic function where the histogram has a symmetrical distribution or a distribution with a wider tail on either the left or right and where εn → 0 is selected as the optimized approximation gradient curve (S22 in FIG. 6).

[0060] (Circulatory dynamics index determination unit 18) The circulatory dynamics index determination unit 18 determines the frequency gradient of the optimized approximate gradient curve selected in the operating band of the long-term blood pressure regulation mechanism (LTM) and sets it as the first circulatory dynamics index (GT of LTM), and determines the frequency gradient of the optimized approximate gradient curve selected in the operating band of the short-term blood pressure regulation mechanism (STM) and sets it as the second circulatory dynamics index (GT of STM) (S23 to S26 in FIG. 6). Note that, for the frequency gradient, the slope of the tangent is used when a cubic function is selected as the optimized approximate gradient curve in the double logarithmic graph of frequency and power spectrum (S23 and S24 in FIG. 6), and the slope of the bitangent is used when a quartic function is selected as the optimized approximate gradient curve (S23 and S25 in FIG. 6).

[0061] (3D response surface model creation part 19) The three-dimensional response surface model creation unit 19 uses the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) determined by the circulatory dynamics index determination unit 18 as explanatory variables, and applies the measured systolic blood pressure data of each of the subject groups as objective variables to create a three-dimensional response surface model (see Figure 2). Figure 18 shows an example of a three-dimensional response surface model created in this way, represented by a fourth-order polynomial. The database construction unit 10 stores the obtained three-dimensional response surface model as a database in the memory unit of the blood pressure estimation device 1.

[0062] [Estimation index calculation unit 20] 1, the estimation index calculation unit 20 obtains and analyzes time-series data of the instantaneous heart rate of a person whose blood pressure is to be estimated (a subject whose blood pressure is to be estimated), calculates a first circulatory dynamics index (GT of LTM) and a second circulatory dynamics index (GT of STM) of the subject whose blood pressure is to be estimated, and designates these as the first circulatory dynamics index for estimation (GT of LTM for estimation) and the second circulatory dynamics index for estimation (GT of STM for estimation), respectively. The first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM for estimation) are calculated by the time-series data analysis unit 11, the frequency analysis unit 12, the approximation curve calculation unit 13, the best approximation curve selection unit 14, the inflection point / intersection extraction unit 15, the approximation gradient curve setting unit 16, the optimized approximation gradient curve selection unit 17, and the circulatory dynamics index determination unit 18, which are all components of the database construction unit 10. That is, time series data of the instantaneous heart rate of the subject for whom blood pressure is to be estimated is sent to the time series data analysis unit 11, and processing is executed in sequence from the frequency analysis unit 12 to the circulatory dynamics index determination unit 18, and the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) are determined.

[0063] [Systolic blood pressure estimation unit 30] As shown in Figure 1, the systolic blood pressure estimation unit 30 applies the first circulatory dynamics index for estimation (estimated GT of LTM) and the second circulatory dynamics index for estimation (estimated GT of STM) obtained by the estimation index calculation unit 20 to the three-dimensional response surface model constructed by the database construction unit 10, estimates the systolic blood pressure of the subject whose blood pressure is to be estimated, and outputs the estimated value.

[0064] In the above description, data obtained from an electrocardiograph (ECG) is used as the time-series data of instantaneous heart rate analyzed by the time-series data analysis unit 11. However, this is not limited to this, as long as similar data can be obtained. For example, a wristwatch-type electrocardiograph known as a smart watch or other wearable device can also be used. Furthermore, time-series waveform data showing a period approximating the RR interval (RRI) can also be used, which is obtained by capturing biosignals (acoustic vibration data (Acoustic Pulse Wave: APW)) from the back of the body surface using a biosignal detection sensor built into a Sleep Buster product manufactured by Delta Tooling Co., Ltd. and filtering the time-series waveform in a predetermined frequency band.

[0065] (Experimental Example 1) The subjects were in their 20s to 90s and sat in a resting position with natural breathing. The experiment consisted of a total of 171 subjects, including a group of healthy individuals (95 individuals) who were not receiving medical care, and a group of outpatients (76 individuals) with cardiovascular diseases or risk factors for these diseases, such as heart disease, high blood pressure, gout, diabetes, and dyslipidemia.

[0066] Heart rate data for 360 seconds was collected from 171 subjects (260 data points) in a seated, resting position using an electrocardiograph, and systolic blood pressure (SBP) was measured using a brachial blood pressure monitor. Measurements were conducted at three locations: Delta Tooling's research facility, Shiga University of Medical Science, and Fukui Prefectural Saiseikai Hospital. Measurements were conducted using a car seat at Delta Tooling's research facility and Shiga University of Medical Science, and a relaxation chair at Fukui Prefectural Saiseikai Hospital. The experimenters, subjects, analysts, and seasons were all different. Brachial systolic blood pressure was recorded as the average of two measurements. However, if the difference between the two measurements was 5 mmHg or more, measurements were taken three or four times, and the average of the two closest measurements was recorded.

[0067] 260 pieces of heart rate data (time series data of instantaneous heart rate) were analyzed by the time series data analysis unit 11 and frequency analyzed by the frequency analysis unit 12. For the obtained spectrogram, approximate curves of a quartic function and an approximate curve of a quintic function were calculated by the approximate curve calculation unit 13. Approximate curves A in Fig. 7(a) and approximate curve A in Fig. 8(a) are examples of approximate curves of quartic functions, and approximate curves B-1 and B-2 in Fig. 7(a) and approximate curve B in Fig. 8(a) are examples of approximate curves of quintic functions.

[0068] The best approximation curve selection unit 14 selects the best approximation curve from each of the approximation curves shown in FIGS. 7(a) and 8(a). In both cases, the best approximation error εn → 0 and the residual histogram distribution shape are selected according to S12 in FIG. 5, with either a symmetrical distribution or a distribution that is slanted to the left or right. For the approximation curve shown in FIG. 7(a), as shown in FIG. 7(b), the residual histogram approximation curve A, which is a quartic function, has a symmetrical distribution, while the quintic function approximation curves B-1 and B-2 both have slanted leftward and slanted to the left. Therefore, both histogram distribution shape conditions are met. Next, comparing the best approximation errors εn, the SD-εn correlation diagram in FIG. 7(c) shows that the εn value closest to 0 is 0.0137. As a result, the quintic function approximation curve B-2 is selected as the best approximation curve.

[0069] Next, since the selected approximation curve B-2 is a quintic function, the inflection point / intersection extraction unit 15 extracts inflection points IP1 and IP2 and tangent intersection points IT1 and IT2 as shown in Fig. 7(d) in accordance with S14 and S16 of Fig. 5. Note that since the approximation curve B-2 in Fig. 7(d) is a quintic function, an inflection point IP3 also exists, but it is not shown because it is outside the range of the graph.

[0070] On the other hand, in the case of the example of the approximation curve A of the quartic function and the approximation curve B of the quintic function in Fig. 8(a), the histograms of the residuals both have a symmetrical distribution, as shown in Fig. 8(b). Therefore, by referring to the SD-εn correlation diagram shown in Fig. 8(c), it is determined that the εn value closest to 0 is 0.0221, and the approximation curve A of the quartic function is selected as the best approximation curve. Because the selected approximation curve A is a quartic function, the inflection point and intersection extraction unit 15 extracts the inflection points IP1 and IP2 and the intersection point IT1 of the tangents, as shown in Fig. 8(d).

[0071] Next, the approximation gradient curve setting unit 16 sets cubic and quartic function approximation curves for the best approximation curve based on the characteristic points of the inflection points IPn (n=1, 2 for a quartic function, n=1, 2, 3 for a quintic function), the tangent intersection points ITn (n=1 for a quartic function, n=1, 2 for a quintic function), and the frequency bands corresponding to combinations of two frequencies from the lower limit of 0.006 Hz and the upper limit of 0.06 Hz for the arterial pressure-related frequency band. Figure 10(a) shows an example of a cubic function approximation curve (approximation gradient curve) obtained within the operating band of the long-term blood pressure regulation mechanism (LTM) from the best approximation curve for the quintic function in Figure 7(d). Figure 10(b) shows an example of a quartic function approximation curve (approximation gradient curve) obtained within the operating band of the long-term blood pressure regulation mechanism (LTM) from the best approximation curve for the quintic function in Figure 7(d).

[0072] The approximate gradient curves shown in Figures 10(a) and (b) show only data that can be candidates for the optimized approximate gradient curve, excluding data in the frequency band under consideration where the residual histogram does not show a symmetrical distribution or a distribution with a wide tail on either the left or right, and where the best approximation error εn is large.

[0073] Figure 11(a) is an example of an approximation curve (approximation slope curve) of a cubic function in the operating range of the short-term blood pressure control mechanism (STM) among the best approximation curves of the quintic function in Figure 7(d), and Figure 11(b) is an example of an approximation curve (approximation slope curve) of a quartic function in the operating range of the short-term blood pressure control mechanism (STM) among the best approximation curves of the quintic function in Figure 7(d).

[0074] As in the case of Figures 10(a) and 10(b), the approximate gradient curves shown in Figures 11(a) and 11(b) show only data that can be candidates for the optimized approximate gradient curve, excluding data in the frequency band under consideration where the histogram of residuals is neither symmetrical nor spreads out to either the left or right, and where the best approximation error εn is large.

[0075] Figure 12 is a diagram showing the SD-εn correlation between Figures 10(a) and 10(b) and Figures 11(a) and 11(b). From Figure 12, the optimized approximate gradient curve selection unit 17 selects data that satisfies the following conditions: the standard deviation SD is low, the best approximation error εn is close to 0, and the data is close to the regression line in the operating band of the long-term blood pressure regulation mechanism (LTM). As a result, in Figure 10(a), 0.006 Hz to IP2, where the best approximation error εn is 0.005, is selected as the frequency band to be analyzed, and the approximate gradient curve of the cubic function found in this analysis frequency band is selected as the optimized approximate gradient curve.

[0076] 12, data is selected from Figures 11(a) and (b) that satisfy the conditions that the standard deviation SD is low, the best approximation error εn is close to 0, and the data is close to the regression line in the operating band of the short-term blood pressure control mechanism (STM). As a result, the frequency band from IT2 to 0.06 Hz in Figure 11(a), where the best approximation error εn is 0.017, is selected as the frequency band to be analyzed, and the approximate gradient curve of the cubic function found in this analysis frequency band is selected as the optimized approximate gradient curve.

[0077] 13 and 14 show examples in which the approximation curve A of a quartic function is selected as the best approximation curve in FIG. 8(d), and the approximation gradient curve setting unit 16 sets approximation curves of a cubic function and a quartic function in the above frequency bands relating to the combination of the inflection points IP1 and IP2, the intersection point IT1 of the tangent lines, and two of the frequencies of the lower limit 0.006 Hz and the upper limit 0.06 Hz of the arterial pressure-related frequency band.

[0078] Figure 13(a) is an example of a best fit approximation curve for the quartic function in Figure 8(d), where the approximation curve (approximation slope curve) of a cubic function is obtained in the operating range of the long-term blood pressure regulation mechanism (LTM). Figure 13(b) is an example of a best fit approximation curve for the quartic function in Figure 8(d), where the approximation curve (approximation slope curve) of a quartic function is obtained in the operating range of the long-term blood pressure regulation mechanism (LTM). Figure 14(a) is an example of a best fit approximation curve for the quartic function in Figure 8(d), where the approximation curve (approximation slope curve) of a cubic function is obtained in the operating range of the short-term blood pressure regulation mechanism (STM). Figure 14(b) is an example of a best fit approximation curve for the quartic function in Figure 8(d), where the approximation curve (approximation slope curve) of a quartic function is obtained in the operating range of the short-term blood pressure regulation mechanism (STM).

[0079] The approximate gradient curves shown in Figures 13(a) and 13(b) and Figures 14(a) and 14(b) show only data that can be candidates for the optimized approximate gradient curve, excluding data in the frequency band under consideration where the histogram of residuals is neither symmetrical nor spreads out to the left or right and where the best approximation error εn is large.

[0080] Figure 15 shows the SD-εn correlation diagram for Figures 13(a) and 13(b) and Figures 14(a) and 14(b). From Figure 15, the optimized approximate gradient curve selector 17 selects data that meets the following conditions: a low standard deviation SD, a best approximation error εn close to 0, and a regression line close to the operating band of the long-term blood pressure regulation mechanism (LTM). As a result, the frequency band from 0.006 Hz to IP2 in Figure 13(b), where the best approximation error εn is 0.0107, is selected as the frequency band to be analyzed, and the approximate gradient curve of the cubic function found in this analysis frequency band is selected as the optimized approximate gradient curve. Similarly, for the operating band of the short-term blood pressure regulation mechanism (STM), the frequency band from IT1 to 0.06 Hz in Figure 14(b), where the best approximation error εn is 0.0096, is selected as the frequency band to be analyzed, and the approximate gradient curve of the cubic function found in this analysis frequency band is selected as the optimized approximate gradient curve.

[0081] Figure 16(a) is a graph showing the best approximation curve of the quintic function shown in Figure 7(d), the optimized approximate gradient curve of the long-term blood pressure regulation mechanism (LTM) with the frequency band of 0.006 Hz to IP2 analyzed shown in Figure 10(a), and the optimized approximate gradient curve of the short-term blood pressure regulation mechanism (STM) with the frequency band of IT2 to 0.06 Hz analyzed shown in Figure 11(a).The optimized approximate gradient curves of the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) are all expressed as cubic functions.

[0082] Therefore, the circulatory dynamics index determination unit 18 determines the gradient of the tangent to each inflection point as the frequency gradient (first circulatory dynamics index, second circulatory dynamics index). Figure 16(b) is a diagram showing the gradient of the tangent to each inflection point, and the frequency gradient (first circulatory dynamics index): GT of LTM of the long-term blood pressure regulation mechanism (LTM) in the frequency band to be analyzed from 0.006 Hz to IP2 is determined to be 1.048, and the frequency gradient (second circulatory dynamics index): GT of STM of the short-term blood pressure regulation mechanism (STM) in the frequency band to be analyzed from IT2 to 0.06 Hz is determined to be 1.9366.

[0083] Figure 17(a) is a graph showing the best approximation curve of the quartic function shown in Figure 8(d), the optimized approximate gradient curve of the long-term blood pressure regulation mechanism (LTM) with the frequency band of 0.006 Hz to IP2 analyzed shown in Figure 13(b), and the optimized approximate gradient curve of the short-term blood pressure regulation mechanism (STM) with the frequency band of IT1 to 0.06 Hz analyzed shown in Figure 14(b). The optimized approximate gradient curves of the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) are all expressed by quartic functions.

[0084] Therefore, the circulatory dynamics index determination unit 18 determines the gradient of each bitangent as the frequency gradient (first circulatory dynamics index, second circulatory dynamics index). Figure 17(b) is a diagram showing the gradient of each bitangent, and the frequency gradient (first circulatory dynamics index): GT of LTM of the long-term blood pressure regulation mechanism (LTM) in the frequency band to be analyzed from 0.006 Hz to IP2 is determined to be -0.508, and the frequency gradient (second circulatory dynamics index): GT of STM of the short-term blood pressure regulation mechanism (STM) in the frequency band to be analyzed from IT1 to 0.06 Hz is determined to be -0.3648.

[0085] Fluctuations in the frequency band being analyzed occur due to factors such as aging and stress. When noise such as aging and stress is added to the chaotic data in the two-dimensional delay coordinate system described above, the data becomes more orderly, which becomes a source of error. The frequency gradients of the long-term blood pressure regulation mechanism (LTM) and short-term blood pressure regulation mechanism (STM) exhibit a white noise state when close to 0, a pink noise state exhibiting 1 / f fluctuations when close to -1, and a Brownian noise state with strong linearity when -2 or above.

[0086] Once the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) have been obtained for all of the data for the subjects, the three-dimensional response surface model creation unit 19 creates a three-dimensional response surface model using the first circulatory dynamics index (GT of LTM) on the X-axis, the second circulatory dynamics index (GT of STM) on the Y-axis, and the systolic blood pressure (SBP) values ​​corresponding to the Z-axis, and stores the model in a database (DB) 40. The resulting three-dimensional response surface model is shown in FIG. 18. FIG. 19(a) is a correlation diagram showing the three-dimensional response surface model of FIG. 18 with the GT of LTM on the horizontal axis and the systolic blood pressure on the vertical axis, and FIG. 19(b) is a correlation diagram showing the three-dimensional response surface model of FIG. 18 with the GT of STM on the horizontal axis and the systolic blood pressure on the vertical axis.

[0087] The obtained three-dimensional response surface model was approximated by the following fourth-order polynomial: f(x,y) = -5.8097x 4 -3.32x 3 y -5.5567x3 -0.91069x 2 y 2 -4.4718x 2 y + 6.0328x 2 -0.14457xy 3 +2.6996xy 2 +3.6018xy +15.1034x -0.31076y 4 -1.3092y 3 + 5.8705y 2 + 23.4022y + 135.6673

[0088] Figures 18 and 19 show that a saddle point was formed on the three-dimensional response surface model. The saddle point was located within the normal blood pressure range of 100 to 120 mmHg. In the normal blood pressure state located on the saddle point, both LTM and STM regulation is thought to function efficiently with minimal energy and accompanied by 1 / f fluctuations. A sudden change in the contour of the response surface appears near 80 mmHg, and this sudden change is thought to indicate a breakdown in homeostasis.

[0089] The distribution of the plots is white noise in the correlation diagram between SBP and GT of LTM in Figure 19(a), and R 2 =0.4541, which was a moderate correlation.

[0090] Figure 20(a) shows the correlation diagram between GT of LTM and GT of STM. However, different plot types are used for each blood pressure range in accordance with the classification stipulated in the "Hypertension Treatment Guidelines 2019 (JSH2019)" (published by the Japanese Society of Hypertension). In accordance with the WHO criteria, systolic blood pressure of 100 mmHg or less was classified as "hypotension." The distribution of the plot is generally in a white noise state, but the hypertension and normotension states are approximated by a quadratic function curve, while hypotension, high-normal blood pressure, and high-normal blood pressure are approximated by a linear line. There is a moderate correlation, R 2=0.3806-0.4858. Therefore, it can be said that SBP is determined by GT of LTM and GT of STM, and shows a correlation with a three-dimensional response surface. Furthermore, it is under integrated regulation by the long-term blood pressure regulatory mechanism (LTM) and the short-term blood pressure regulatory mechanism (STM). In normotensive individuals, the principle of least energy action applies when the GT of LTM value is near -0.5, and in hypertensive individuals, the GT of LTM value is near 0, suggesting the possibility of an efficient frequency gradient (fluctuation) for arterial pressure regulation.

[0091] Figure 20(b) is a correlation diagram between each estimated systolic blood pressure (eSBP) calculated by substituting x = first circulatory dynamics index: GT of LTM and y = second circulatory dynamics index: GT of STM into the fourth-order polynomial equation representing the three-dimensional response surface model of Figure 18 for each of the 171 subjects, and each measured systolic blood pressure (SBP). 2 =0.7468, showing a high correlation between the two.

[0092] As described above, the estimation index calculation unit 20 obtains time series data of the instantaneous heart rate of the subject for blood pressure estimation, and executes the computer program comprising the time series data analysis unit 11 to the circulatory dynamics index determination unit 18 constituting the database construction unit 10 to determine the first circulatory dynamics index: GT of LTM and the second circulatory dynamics index: GT of STM, respectively. The systolic blood pressure estimation unit 30 applies these to the above-mentioned three-dimensional response surface model (a polynomial representing the three-dimensional response surface model) as the first circulatory dynamics index for estimation: GT of LTM and the second circulatory dynamics index for estimation: GT of STM, respectively, thereby obtaining a highly accurate estimate of systolic blood pressure for the subject for blood pressure estimation.

[0093] (Experimental Example 2) A blood pressure measurement experiment was conducted over a one-year period on a 34-year-old subject who maintained normal blood pressure and a 67-year-old subject who maintained normal to elevated blood pressure through medication. The 34-year-old subject was measured 38 times, and the 67-year-old subject was measured 121 times. Systolic blood pressure (SBP) was measured using an upper arm sphygmomanometer, and estimated systolic blood pressure (eSBP) values ​​were calculated using the fourth-order polynomial equation representing the three-dimensional response surface model shown in Figure 18 by executing the estimation index calculation unit 20 and the systolic blood pressure estimation unit 30. The results are shown in Figures 21(a) and 21(b).

[0094] As shown in Figure 21(a) and (b), for a 34-year-old subject, R 2 =0.7667, for a 67-year-old subject, R 2 = 0.9013, which showed a high correlation. Therefore, it was found that the three-dimensional response surface model shown in Figure 18 (the polynomial that represents the three-dimensional response surface model) can be used in place of actual measurement data when estimating fluctuations in systolic blood pressure for a specific individual over a specified period of time.

[0095] (Second embodiment) In this embodiment, we focused on the existence of an overlap frequency (OLF) between the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM), where the frequency bands of the two mechanisms overlap. Figures 22(a)-(c) show images of the overlap frequency (OLF), and Figure 22(b) is the same as Figure 9. However, as shown in Figure 22(a), the modification levels (vertical axis) of the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) from the frequency (horizontal axis) decrease in opposite directions from the center of 0.017 Hz. The long-term blood pressure regulation mechanism (LTM) has an upper limit (Upper Limit of LTM), and the short-term blood pressure regulation mechanism (STM) has a lower limit (Lower Limit of STM). More specifically, the inflection point IP on the approximate slope curve is the point of change caused by the modification by the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM), and the intersection point IT is related to the overlap frequency (OLF).

[0096] Therefore, it is essential that an overlap band (OLF) exists between the frequency band related to the long-term blood pressure regulation mechanism (LTM) and the frequency band related to the short-term blood pressure regulation mechanism (STM); if this does not occur, the data is presumed to contain outliers. Here, "an overlap band (OLF) exists between the frequency band related to the long-term blood pressure regulation mechanism (LTM) and the frequency band related to the short-term blood pressure regulation mechanism (STM) (OLF requirement)" specifically refers to the following: within the combined frequency band related to the LTM and STM, the LTM and STM frequency bands are contiguous and straddle the boundary between the two frequency bands (see the analysis example in (c-1) of Figure 39(c)); the OLF frequency band exists between the boundary with the LTM or STM frequency band and either the LTM or STM frequency band (see the analysis example in (c-2) and (c-3) of Figure 39(c)); or the OLF frequency band exists contiguous between the LTM and STM frequency bands (see the analysis example in (c-4) of Figure 39(c)). For example, if the OLF frequency band is not contiguous with the LTM frequency band and is separated from it, the above OLF requirement is not met.

[0097] If a GT of LTM and a GT of STM that do not satisfy the OLF requirements are selected, the GT of STM is used as a reference and the frequency band to be analyzed for the GT of LTM is reselected so that it is a frequency band that leads to OLF. Specifically, if the OLF requirements are not satisfied, the approximation curve of a quartic or quintic function calculated by the approximation curve calculation unit 13 may be inappropriate, or the approximation curve of a cubic or quartic function calculated by the approximation gradient curve setting unit 16 may be inappropriate. This is due to the inclusion of outliers in the plot display of the spectrogram obtained by the frequency analysis unit 12. In the spectrogram of the double logarithmic graph of 0.006 to 0.06 Hz used in the first and second embodiments, outliers are likely to occur on the low-frequency side. In particular, outliers are likely to occur at the three end points on the low-frequency side. When the three end points are connected by a parabola, no outliers are generated. However, when the approximation curve is drawn as a parabola passing through the two end points (such as the example of approximation curve B-1 in Figure 38), the lowest-frequency end point is likely to be an outlier. If the approximation curve passing through the two end points is almost a straight line, it is not an outlier, but may reflect the action of a lower frequency regulatory system than LTM. Therefore, among the plots displayed in the spectrogram, the plot located on the lowest frequency side, which is likely to be an outlier, is excluded, and processing to obtain an approximation curve is performed by the approximation curve calculation unit 13 or the approximation gradient curve setting unit 16.

[0098] Specifically, as shown in Fig. 23, after the optimization approximate gradient curve selection unit 17 selects the frequency band to be analyzed for the optimized approximate gradient curve, or after the circulatory dynamics index determination unit 18 determines the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM), it is confirmed whether or not the overlap band (OLF) exists at a predetermined position. That is, whether or not the above-mentioned OLF requirement is met is determined by an OLF requirement determination unit 171 provided in the optimization approximate gradient curve selection unit 17. If it is met (if the overlap band (OLF) exists at a predetermined position), that is, if Yes is determined in S30 of the flowchart in Fig. 24, the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) determined by the circulatory dynamics index determination unit 18 in S23 to S25 are used as they are. If the answer is No in S30, the process returns to S11, excluding the plot located on the lowest frequency side in the spectrogram, and the approximate curve calculation unit 13 calculates approximate curves for a quartic function and a quintic function, or returns to S22, excluding the plot located on the lowest frequency side in the spectrogram, and the approximate gradient curve setting unit 16 calculates approximate curves for a cubic function and a quartic function. Thereafter, the process is performed in the same manner as in the above embodiment, and the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) are calculated by the circulatory dynamics index determination unit 18, and it is again determined whether the recalculation requirement is met, and the same process is repeated thereafter. Note that in the flowchart of FIG. 24, S11 to S16 indicate the execution of the same procedures as S11 to S16 in the flowchart of FIG. 5, and S21 to S26 indicate the execution of the same procedures as S21 to S26 in the flowchart of FIG. 6. If the result of S30 is No, it is possible to arbitrarily return to either the step of S11 where the approximate curve is calculated by the approximate curve calculation unit 13 or the step of S22 where the approximate curve is calculated by the approximate gradient curve setting unit 16. However, in terms of shortening the calculation time, it is preferable to give priority to returning to the step of S22 where the approximate curve is calculated by the approximate gradient curve setting unit 16. The second embodiment is completely similar to the first embodiment in terms of configuration other than the OLF requirement determination unit 171.

[0099] Here, we will discuss the relationship between inflection points, long-term vasopressor (LTM), short-term vasopressor (STM), and overlap band (OLF). First, the slope of y = f(x) changes, and the slope of the curve at an inflection point is the slope of the tangent at that point. The average slope between two inflection points on a function y = f(x) is expressed in the form of a difference coefficient. The limiting slope where this difference becomes infinitesimally small is the derivative, or differential coefficient, of the function y = f(x) at the inflection point. If we plot the slope of the curve y = f(x) with the ordinate y', we obtain the first derivative of the original curve y = f(x), the slope curve y' = f'(x'). Furthermore, from the derivative of y' = f'(x'), we obtain the second derivative y'' = f''(x) of the original curve y = f(x). Because y'' = 0 at the inflection point of the curve y = f(x), the sign of f''(x) changes before and after the inflection point. The inflection point is the switching frequency of accommodation between LTM and STM. Furthermore, the extreme values ​​on the curve represent an equilibrium state of accommodation function when y' = 0 and the distance between the extreme values ​​approaches infinity. The equilibrium state of accommodation function is captured by the frequency at which the two tangent slopes intersect, and this intersection frequency also becomes the switching frequency between LTM and STM. Starting from these switching frequencies, OLF appears between LTM and STM.

[0100] (Experimental Example 3) The data of the same subjects as in Experimental Example 1 was used for analysis. 25 to 27 show examples of analysis performed by the OLF requirement determination unit 171 of this embodiment. The subject in this analysis example is a person with high blood pressure (subject (c-4) described below). First, as shown in FIG. 25(a), frequency analysis was performed by the frequency analysis unit 12, and for the obtained spectrogram, an approximate curve of a quartic function and an approximate curve of a quintic function were calculated by the approximate curve calculation unit 13. Approximate curve A in FIG. 25(a) is the approximate curve of the quartic function, and approximate curve B in FIG. 25(a) is the approximate curve of the quintic function.

[0101] Next, the best approximation curve selection unit 14 selects the best approximation error εn→0, and the distribution shape of the residual histogram is either a bilaterally symmetrical distribution or a distribution that is inclined to widen to the left or right. For each of the approximation curves in FIG. 25(a), as shown in FIG. 25(b), the residual histograms all have bilaterally symmetrical distributions, satisfying the histogram distribution shape conditions. Next, comparing the best approximation errors εn, the SD-εn correlation diagram in FIG. 25(b) shows that the εn closest to 0 is 0.0191. As a result, the quintic function approximation curve B is selected as the best approximation curve.

[0102] Next, as shown in FIG. 25(c), the inflection point and intersection point extracting unit 15 extracts inflection points IP1, IP2, IP3 and tangent intersection points IT1, IT2 because the best approximation curve is a quintic function.

[0103] Next, the approximation gradient curve setting unit 16 sets cubic and quartic function approximation curves for the best approximation curve based on the inflection points IP1, IP2, and IP3, which are characteristic points, and the tangent intersection points IT1 and IT2, as well as two combinations of frequencies between the lower limit of 0.006 Hz and the upper limit of 0.06 Hz of the arterial pressure-related frequency band. Figures 26(a) to 26(d) show examples of cubic function approximation curves (approximation gradient curves) obtained in the operating band of the long-term blood pressure regulation mechanism (LTM) from the best approximation curve for the quintic function in Figure 25(c), and Figures 26(e) and 26(f) show examples of quartic function approximation curves (approximation gradient curves) obtained in the operating band of the long-term blood pressure regulation mechanism (LTM) from the best approximation curve for the quintic function in Figure 25(c).

[0104] Figures 27(g) to (j) are examples of the approximate curve (approximate gradient curve) of a cubic function obtained in the operating range of the short-term blood pressure regulation mechanism (STM) from the best approximation curve of the quintic function in Figure 25(c), and Figures 27(k) to (m) are examples of the approximate curve (approximate gradient curve) of a quartic function obtained in the operating range of the short-term blood pressure regulation mechanism (STM) from the best approximation curve of the quintic function in Figure 25(c).

[0105] The approximate gradient curves shown in Figs. 26 and 27 show only data that can be candidates for the optimized approximate gradient curve, excluding data in the frequency band under consideration where the residual histogram does not show a symmetrical distribution or a distribution that spreads out to either the left or right, and where the best approximation error εn is large.

[0106] Figure 28 is an SD-εn correlation diagram for each of the approximate gradient curves in Figures 26 and 27, and the optimized approximate gradient curve selection unit 17 selects an optimized approximate gradient curve using this SD-εn correlation diagram. First, the optimized approximate gradient curve selection unit 17 obtains three regression lines (No. 1) in the operating band of the long-term blood pressure regulation mechanism (LTM), the regression line (No. 2) in the operating band of the short-term blood pressure regulation mechanism (STM), and a regression line combining the two operating bands (No. 3) from the SD-εn correlation diagram, and checks whether each plot is close to any of the regression lines. Then, the optimized approximate gradient curve selection unit 17 selects data that meets the conditions of a low standard deviation SD, a best approximation error εn close to 0, and proximity to each regression line. As a result, for the operating band of the long-term blood pressure regulation mechanism (LTM), the optimized approximate gradient curve selector 17 selects the frequency band to be analyzed from 0.006 Hz to IP2 in Figure 26(a), where the best approximation error εn is 0.0154, and selects the approximate gradient curve of a cubic function found in that analysis frequency band as the optimized approximate gradient curve.For the operating band of the short-term blood pressure regulation mechanism (STM), the optimized approximate gradient curve selector 17 selects the frequency band to be analyzed from IT2 to 0.06 Hz in Figure 27(m), where the best approximation error εn is 0.0059, and selects the approximate gradient curve of a quartic function found in that analysis frequency band as the optimized approximate gradient curve.

[0107] The OLF requirement determination unit 171 determines whether the analysis target frequency band of 0.006 Hz to IP2 of LTM and the analysis target frequency band of IT2 to 0.06 Hz of STM, which are selected as the optimized approximate gradient curves, satisfy the OLF requirement. Fig. 29(a) shows the approximate gradient curve of a cubic function for the analysis target frequency band of 0.006 Hz to IP2 of LTM, the approximate gradient curve of a quartic function for IT2 to 0.06 Hz of STM, and the best approximate curve of a quintic function. Fig. 29(b) shows the analysis target frequency band of LTM, the analysis target frequency band of STM, and the frequency band of OLF. As shown in Fig. 29(b), in this analysis example, the analysis target frequency band of LTM, the frequency band of OLF, and the frequency band of STM are continuous from the low frequency side, and satisfy the above OLF requirement.

[0108] Therefore, for the long-term blood pressure regulation mechanism (LTM), the circulatory dynamics index determination unit 18 calculates the frequency gradient (first circulatory dynamics index): GT of LTM, which is the gradient of the tangent to the inflection point of the optimized approximate gradient curve represented by a cubic function, as 1.4192, and for the short-term blood pressure regulation mechanism (STM), the frequency gradient (second circulatory dynamics index): GT of STM, which is the gradient of the bitangent to the optimized approximate gradient curve represented by a quartic function, as -0.0065.

[0109] Once the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) have been obtained for all of the data for the subjects, the three-dimensional response surface model creation unit 19 creates a three-dimensional response surface model using the first circulatory dynamics index (GT of LTM) on the X-axis, the second circulatory dynamics index (GT of STM) on the Y-axis, and the systolic blood pressure (SBP) values ​​corresponding to the Z-axis, and stores the model in a database (DB) 40. The resulting three-dimensional response surface model is shown in FIG. 30. FIG. 31(a) is a correlation diagram showing the three-dimensional response surface model of FIG. 30, with GT of LTM on the horizontal axis and systolic blood pressure on the vertical axis, and FIG. 31(b) is a correlation diagram showing the three-dimensional response surface model of FIG. 30, with GT of STM on the horizontal axis and systolic blood pressure on the vertical axis.

[0110] The obtained three-dimensional response surface model was approximated by the following fourth-order polynomial: f(x,y) = -5.1399x 4 -2.6893x 3 y -4.5494x 3 -0.82825x 2 y 2 -4.827x 2 y + 4.7529x 2 + 0.091724xy 3 + 2.207xy 2 + 0.55227xy + 12.9027x -0.18196y 4 -1.0256y 3 + 5.0227y 2 + 21.9606y + 135.0414

[0111] Figure 32(a) shows a correlation diagram between GT of LTM and GT of STM. Figure 32(b) shows a correlation diagram between each estimated systolic blood pressure (eSBP) calculated by substituting x = first circulatory dynamics index: GT of LTM, y = second circulatory dynamics index: GT of STM for each of the 171 subjects into the fourth-order polynomial equation showing the three-dimensional response surface model of Figure 30, and each measured systolic blood pressure (SBP). R 2 =0.8048, indicating a high correlation between the two. Furthermore, the coefficient of determination is higher than the result of the first embodiment shown in FIG. 20(b), reflecting the effect of the process of excluding outliers by the OLF requirement determination unit 171. Furthermore, as shown in the lower column of FIG. 32, the coefficient of determination for the hypertensive subjects is also slightly higher than the result of the first embodiment shown in FIG. 20.

[0112] Figures 33(a)-(c) show other analysis examples of this embodiment used to create the response surface model. Figure 33(c) shows analysis examples for one hypotensive subject (c-1), one normotensive subject (c-2), one high-normal blood pressure subject (c-3), one high-normal blood pressure subject (c-4), and two medicated hypertensive subjects (c-5, c-6), all of whom are classified according to the JSH2019HO classification and WHO criteria. As shown in the SD-εn correlation diagram in Figure 33(b) and Figure 33(c), the data shown in Figure 33 are all data that satisfy the selection conditions of the optimized approximate gradient curve selector 17 and the OLF requirements determined by the OLF requirement determiner 171. Figure 33(a) is the SBP-eSBP correlation diagram shown in Figure 32(b), with the data for subjects (c-1)-(c-6) highlighted in Figure 33(c).

[0113] Figures 34 to 38 show detailed analysis examples of subjects (c-1), (c-2), (c-3), (c-5), and (c-6), respectively, and each shows the process of creating a spectrogram by the frequency analysis unit 12, calculating approximation curves of quartic and quintic functions by the approximation curve calculation unit 13, selecting the best approximation curve by the best approximation curve selection unit 14, extracting inflection points IPn and tangent intersection points ITn by the inflection point / intersection extraction unit 15, setting approximation curves of cubic and quartic functions in the operating bands of the long-term blood pressure regulation mechanism (LTM) and short-term blood pressure regulation mechanism (STM) by the approximation gradient curve setting unit 16, selecting an optimized approximation gradient curve by the optimized approximation gradient curve selection unit 17, and calculating each frequency gradient (GT of LTM, which is the first circulatory dynamics index, and GT of STM, which is the second circulatory dynamics index) in the long-term blood pressure regulation mechanism (LTM) and short-term blood pressure regulation mechanism (STM) by the circulatory dynamics index determination unit 18. In either case, the OLF requirement determination unit 171 determines whether the OLF requirement is satisfied or not in the optimized approximate gradient curve selection unit 17, and data that satisfies the OLF requirement is used. The analysis data of the high blood pressure subject (c-4) is the data shown in Figures 25 to 29.

[0114] (Experimental Example 4) Data from a 67-year-old subject in Experimental Example 2 was analyzed based on processing by the OLF requirement determination unit 171 of the second embodiment. The results are shown in Fig. 39. Fig. 39(c) shows an analysis example of 121 measurement data from the same subject, showing normal blood pressure (c-1), high-normal blood pressure (c-2), high blood pressure (c-3), and hypertension (c-4). This subject maintained normal to high blood pressure during drug treatment, but during drug suspension, his blood pressure dropped to a hypertensive state of approximately 150 mmHg.

[0115] Figure 39(a) shows a correlation diagram between the estimated systolic blood pressure (eSBP) obtained by substituting the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) obtained from Figure 39(c) into the polynomial equation of the second embodiment and each measured systolic blood pressure (SBP). The coefficient of determination of the regression line of this correlation diagram was 0.8974, indicating a significant correlation.

[0116] Figure 39(b) is an SD-εn correlation diagram using the data in Figure 39(c). These figures show that there is a difference in the distribution relationship between SD and εn during medication and during periods when medication is not being administered. In other words, when medication is being administered, the distribution of SD and εn is equivalent to that of normotensive individuals, and the best-fit curve is represented by a function with few extreme values ​​and little unevenness. On the other hand, during periods when medication is not being administered, the best-fit curve shows large unevenness. The best-fit curve for a hypertensive state, where extreme values ​​occur in the frequency band of the short-term blood pressure regulation mechanism (STM), is thought to be one in which the approximation function passes through the spectrum, resulting in εn → 0.

[0117] These findings support Guyton's hypothesis that the best fit curve changes in response to blood pressure values, and that arterial pressure operates according to the same principles as mechanical and electrical systems. Furthermore, the frequency gradients (GT of LTM, GT of STM) calculated in each of the above embodiments can be considered as mechanical and electrical parameters of the arterial pressure regulation system, and the optimized best fit gradient curve can be said to capture the general characteristics of the best fit curve in terms of its tangent gradient. Therefore, systolic blood pressure can be estimated using a fourth-order polynomial obtained from a three-dimensional response surface model, which is visualization information of the stochastic differential equation of the arterial pressure regulation system. [Explanation of symbols]

[0118] 1. Blood pressure estimation device 10 Database Construction Department 11 Time Series Data Analysis Section 12 Frequency analysis section 13 Approximate curve calculation section 14 Best Fit Curve Selection 15 Inflection point / intersection point extraction part 16 Approximate gradient curve setting section 17 Optimization approximate gradient curve setting section 171 OLF Requirements Judgment Section 18 Circulatory dynamics index determination unit 19 3D response surface model creation section 20 Estimation index calculation unit 30 Systolic blood pressure estimation unit 40 Database (DB)

Claims

1. a database construction unit that analyzes time-series data of instantaneous heart rates of a predetermined number of subjects, determines a first circulatory dynamics index related to the long-term blood pressure regulation mechanism (LTM) and a second circulatory dynamics index related to the short-term blood pressure regulation mechanism (STM) in an arterial pressure-related frequency band, and constructs a database of a three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index as explanatory variables and the actual measurement data of the systolic blood pressure of each of the subjects as a response variable; an estimation index calculation unit that analyzes time series data of an instantaneous heart rate of a subject whose blood pressure is to be estimated, calculates the first circulatory dynamics index and the second circulatory dynamics index of the subject whose blood pressure is to be estimated, and sets the first circulatory dynamics index and the second circulatory dynamics index for estimation, respectively; a systolic blood pressure estimation unit that applies the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation to the three-dimensional response surface model constructed by the database construction unit, and estimates the systolic blood pressure of the subject whose blood pressure is to be estimated; A blood pressure estimation device having the above structure.

2. The database construction unit A two-dimensional delay coordinate system is reconstructed by applying a Lorenz plot to the time series data of the instantaneous heart rate of the predetermined number of subjects, an attractor in a reference time range is created, and reference angle information (θ ０ ), and an individual attractor in the two-dimensional delay coordinate system in an individual time range shorter than the reference time range, and individual angle information (θ ｉ ) and the difference (θ ｉ－０ ) is calculated sequentially while moving the individual time range, and the difference (θ ｉ－０ a time series data analysis unit for obtaining time series data of The difference (θ ｉ－０ a frequency analysis unit that performs frequency analysis on the time series data of the signal and displays a spectrum diagram on a log-log graph; an approximation curve calculation unit that sets two types of approximation curves, a quartic function and a quintic function, for the arterial blood pressure-related frequency band in the spectrogram; a best approximation curve selection unit that selects a best approximation curve to be used for analysis from the two types of approximation curves, the quartic function and the quintic function; an inflection point and intersection point extraction unit that extracts inflection points and intersection points of tangents from the best approximation curve; an approximate gradient curve setting unit that sets two types of approximate gradient curves, a cubic function and a quartic function, as approximate gradient curves for each frequency band related to all combinations of two frequencies selected from the frequency of the inflection point, the frequency of the intersection of the tangent lines, and the lower and upper limits of the arterial pressure-related frequency band, in each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism within the arterial pressure-related frequency band; an optimized approximate gradient curve selection unit that selects an optimized approximate gradient curve to be used for analysis from the two types of approximate gradient curves, the cubic function and the quartic function, in each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism; a circulatory dynamics index determining unit that determines the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the long-term blood pressure regulation mechanism as the first circulatory dynamics index and the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the short-term blood pressure regulation mechanism as the second circulatory dynamics index; a three-dimensional response surface model creation unit that creates the three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index; 2. The blood pressure estimation device according to claim 1, further comprising:

3. The estimation index calculation unit The time series data of the instantaneous heart rate of the subject for whom blood pressure is to be estimated is analyzed by the time series data analysis unit, the frequency analysis unit, the approximation curve calculation unit, the best approximation curve selection unit, the inflection point / intersection extraction unit, the approximation gradient curve setting unit, the optimized approximation gradient curve selection unit, and the circulatory dynamics index determination unit to determine a first circulatory dynamics index and a second circulatory dynamics index of the subject for whom blood pressure is to be estimated, and these are set as a first circulatory dynamics index for estimation and a second circulatory dynamics index for estimation, respectively. The blood pressure estimation device according to claim 2.

4. The circulatory dynamics index determination unit When the optimized approximate curve is an approximate gradient curve represented by the cubic function, the gradient of the tangent to the inflection point is defined as the frequency gradient; When the optimized approximate curve is an approximate gradient curve represented by the quartic function, the gradient of the bitangent is set as the frequency gradient. The blood pressure estimation device according to claim 2.

5. The best approximation curve selection unit The following evaluation indexes are related to the residual between each of the two types of approximation curves of the quartic function and the quintic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; and Distribution shape of the residuals The best approximation curve is selected using at least one of the following: The blood pressure estimation device according to claim 2.

6. The optimized approximate gradient curve selection unit The following evaluation indexes are related to the residual between each of the two types of approximation curves of the cubic function and the quartic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; The distribution shape of the residuals, and Correlation between the standard deviation of the residuals and the best approximation error The optimized approximate gradient curve is selected using at least one of The blood pressure estimation device according to claim 2.

7. The optimized approximate gradient curve selection unit In the selected optimized approximate gradient curve, it is determined whether or not an overlap band (OLF) where both the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) are modulated exists at a predetermined position. If it does not exist, the spectrogram is recalculated by excluding outliers included in the long-term blood pressure regulation mechanism (LTM). The blood pressure estimation device according to claim 2.

8. A computer program that causes a computer to function as a blood pressure estimation device, a step of analyzing time series data of instantaneous heart rates of a predetermined number of subjects, determining a first circulatory dynamics index related to the long-term blood pressure regulation mechanism (LTM) and a second circulatory dynamics index related to the short-term blood pressure regulation mechanism (STM) in an arterial pressure-related frequency band, and constructing a database of a three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index as explanatory variables and the actual measured data of the systolic blood pressure of each of the subjects as a response variable; a step of analyzing time series data of an instantaneous heart rate of a subject whose blood pressure is to be estimated, determining the first circulatory dynamics index and the second circulatory dynamics index of the subject whose blood pressure is to be estimated, and setting the first circulatory dynamics index and the second circulatory dynamics index for estimation, respectively; applying the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation to the three-dimensional response surface model to estimate the systolic blood pressure of the subject whose blood pressure is to be estimated; A computer program that causes the computer to execute the above.

9. In the step of constructing the database, A two-dimensional delay coordinate system is reconstructed by applying a Lorenz plot to the time series data of the instantaneous heart rate of the predetermined number of subjects, an attractor in a reference time range is created, and reference angle information (θ ０ ), and an individual attractor in the two-dimensional delay coordinate system in an individual time range shorter than the reference time range, and individual angle information (θ ｉ ) and the difference (θ ｉ－０ ) is calculated sequentially while moving the individual time range, and the difference (θ ｉ－０ ) and the procedure for obtaining time series data. The difference (θ ｉ－０ ) time series data and display the spectrum diagram on a log-log graph. a step of setting two types of approximation curves, a quartic function and a quintic function, for the arterial blood pressure-related frequency band in the spectrogram; a step of selecting a best approximation curve to be used for analysis from the two types of approximation curves, the quartic function and the quintic function; extracting inflection points and tangent intersection points for the best approximation curve; a step of setting two types of approximate curves, a cubic function and a quartic function, as approximate gradient curves for each frequency band relating to all combinations of two frequencies selected from the frequency of the inflection point, the frequency of the intersection of the tangent lines, and the lower and upper limits of the arterial pressure-related frequency band, in each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism within the arterial pressure-related frequency band; selecting an optimized approximate gradient curve to be used for analysis from the two approximate gradient curves, the cubic function and the quartic function, for each of the operating bands of the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism; a step of setting the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the long-term blood pressure regulation mechanism as the first circulatory dynamics index, and setting the value of the frequency gradient of the optimized approximate gradient curve for the operating band of the short-term blood pressure regulation mechanism as the second circulatory dynamics index; creating the three-dimensional response surface model using the first circulatory dynamics index and the second circulatory dynamics index; 9. The computer program product of claim 8, wherein the computer program product executes the following steps:

10. In the step of calculating the first circulatory dynamics index for estimation and the second circulatory dynamics index for estimation of the blood pressure estimation subject, The method executes steps of obtaining time series data of the instantaneous heart rate of the subject for whom blood pressure is to be estimated, performing the frequency analysis and displaying a spectrogram, setting the approximation curve, selecting the best approximation curve, extracting the inflection points and tangent intersections, setting the approximation gradient curve, and selecting the optimized approximation gradient curve, A first circulatory dynamics index and a second circulatory dynamics index of the subject whose blood pressure is to be estimated are calculated, and the first circulatory dynamics index and the second circulatory dynamics index are used for estimation, respectively.

10. The computer program of claim 9.

11. In the step of determining the first circulatory dynamics index and the second circulatory dynamics index, When the optimized approximate curve is an approximate gradient curve represented by the cubic function, the gradient of the tangent to the inflection point is defined as the frequency gradient; When the optimized approximate curve is an approximate gradient curve represented by the quartic function, the gradient of the bitangent is set as the frequency gradient.

10. The computer program of claim 9.

12. In the step of selecting the best approximation curve, The following evaluation indexes are related to the residual between each of the two types of approximation curves of the quartic function and the quintic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; The distribution shape of the residuals, and Correlation between the standard deviation of the residuals and the best approximation error The best approximation curve is selected using at least one of the following:

10. The computer program of claim 9.

13. In the step of selecting the optimized approximate gradient curve, The following evaluation indexes are related to the residual between each of the two types of approximation curves of the cubic function and the quartic function and the spectrogram: the standard deviation of the residuals, the mean value of the absolute values ​​of the residuals; a best approximation error, which is the difference between the standard deviation of the residuals and the average value of the absolute values ​​of the residuals; and Distribution shape of the residuals The optimized approximate gradient curve is selected using at least one of 10. The computer program of claim 9.

14. In the step of selecting the optimized approximate gradient curve, In the selected optimized approximate gradient curve, it is determined whether or not an overlap band (OLF) where both the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) are modulated exists at a predetermined position. If it does not exist, the spectrogram is recalculated by excluding outliers included in the long-term blood pressure regulation mechanism (LTM).

10. The computer program of claim 9.

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

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