Circulatory dynamics index calculation device, computer program, and recording medium

Abstract

To estimate blood pressure with greater accuracy. [Solution] Using time-series data of instantaneous heart rate, residual information including the frequency transfer function and residual function is applied to the spectral diagram of a log-log graph with frequency on the horizontal axis and power spectrum on the vertical axis, and a local approximation curve for calculating the frequency gradient can be set to an appropriate frequency band for each data point. Consequently, the accuracy of the frequency gradients that serve as the first and second circulatory dynamics indices obtained from this local approximation curve is improved, and the accuracy of blood pressure estimation using these frequency gradients is also improved.

Description

[Technical Field]

[0001] This invention relates to a technique for estimating blood pressure based on information regarding heart rate. [Background technology]

[0002] In Patent Document 1, the applicant discloses a blood pressure estimation device that analyzes biosignal data obtained from a biosignal detection sensor using a three-dimensional knitted fabric, captures in vivo vibration information caused by blood flow fluctuations from the ventricular filling phase to the isovolumetric contraction phase, determines an index showing these fluctuations by applying the Lorentz plot method, and estimates blood pressure values ​​from that index.

[0003] Specifically, this technique involves capturing biological signals (acoustic pulse waves: APW) from the body surface of the back using a biosignal detection sensor, filtering the time-series waveform within a predetermined frequency band to obtain a filtered waveform that reveals the cardiac cycle, comparing this filtered waveform with electrocardiogram waveform data obtained from an electrocardiograph simultaneously, identifying waveform components from the ventricular filling phase to the isovolumetric contraction phase, then applying the Lorentz plot method to construct a new time series based on the angle difference between the slope of a large number of points plotted over a predetermined measurement time and the slope of a point group over a shorter measurement time, performing frequency analysis on this time-series waveform, displaying the frequency analysis results on a log-log axis, finding a regression line for the range from VLF to LF in the analyzed waveform, defining the slope of this regression line as the fractal slope, and estimating blood pressure from the fractal slope.

[0004] Patent Document 1 uses the fractal slope described above as an indicator of fluctuations in heart rate variability. However, arterial pressure is not regulated by a single pressure control system, but rather by an integrated, multifaceted system that is interconnected. In contrast, the fractal slope is an indicator that shows the characteristics of the analyzed waveform with only one regression line, and it cannot be said to adequately reflect the characteristics of the arterial pressure control system.

[0005] Therefore, the applicant of the present application proposed, as Patent Document 2, a technique that uses the heart rate, acoustic pulse wave, or pulse wave propagation delay time, frequency-analyzes the time waveform data of the fluctuations obtained from the measurement data thereof, displays the data on a double logarithmic graph, approximately represents the point group of the power spectrum displayed on the spectrum diagram with a graph of a fourth-order function, and uses the slope between inflection points or the slope of the tangent line of the inflection point as a circulatory dynamic fluctuation index.

Prior Art Documents

Patent Documents

[0006]

Patent Document 1

Patent Document 2

Non-Patent Documents

[0007]

Non-Patent Document 1

Non-Patent Document 2

Non-Patent Document 3

Summary of the Invention

Problems to be Solved by the Invention

[0008] According to the technique of Patent Document 2, the characteristics of the physiological index related to complexity control can be captured more accurately than the technique that represents the characteristics only by the regression line of Patent Document 1. As a result, the accuracy of the estimated blood pressure value estimated using the obtained circulatory dynamic fluctuation index also increases. However, the higher the estimation accuracy of blood pressure, the more desirable it is.

[0009] In view of the above, an object of the present invention is to provide a technique capable of obtaining blood pressure (particularly, systolic blood pressure) with higher accuracy.

[0010] In order to solve such problems, the present invention focused on the following points. First, arterial blood pressure is not regulated by a single pressure control system as described above, but is regulated by an integrated and multifaceted system related to each other. It shifts from the short-term blood pressure regulation mechanism, which is a neural regulation mechanism, to an intermediate pressure control mechanism, and stabilizes by a long-term blood pressure regulation mechanism by the renin-angiotensin system, that is, the renal-body fluid pressure control mechanism. That is, the mechanism of long-term stabilization includes various interactions by factors having various regulatory capabilities including the renal-body fluid pressure control mechanism and the neural regulation mechanism. Among these, the present inventor focused on the concept of the non-neural intermediate pressure control mechanism (Intermediate pressure control mechanism (IPCM)) mentioned in Non-Patent Document 1.

[0011] The regulation by the renal-body fluid pressure control mechanism mainly functions in the frequency band of 0.006 to 0.017 Hz, and the regulation by the neural regulation mechanism mainly functions in the frequency band of 0.017 to 0.06 Hz. The intermediate pressure control mechanism operates on both the regulation by the renal-body fluid pressure control mechanism and the regulation by the neural regulation mechanism. That is, 0.006 to 0.06 Hz obtained by combining the frequency bands of both becomes the main frequency band related to arterial blood pressure control, and the intermediate pressure control mechanism affects each of the above regulations in this wide frequency band (global frequency band).

[0012] Incidentally, regarding Guyton's circulatory system model (Guyton model) shown in Non-Patent Document 1, there is the finding that "the endocrine and nervous systems are modified by fluctuations according to probability distributions, which are reflected in heart rate variability according to their respective reaction times (see Non-Patent Document 2), and the fluctuations in heart rate variability are represented by global and local fluctuations (see Non-Patent Document 3)." The renal-hydraulic pressure control mechanism and the nervous regulatory mechanism, which are responsible for the action and compensation of the relay pressure control mechanism, are thought to cause fluctuations in the fluctuations of the relay pressure control mechanism (global fluctuations) across the entire global frequency band due to the difference in the degree of excitation of the two. It is thought that by detecting the degree of excitation of each, it is possible to grasp the fluctuations of the relay pressure control mechanism (local fluctuations) to each. Therefore, it is important to distinguish between intermediate pressure control mechanisms that operate in the frequency band where regulation by the renal-hydraulic pressure control mechanism occurs (referred to as "Long-Term Mechanisms for Arterial Pressure Regulation (LTM)" in this specification) and intermediate pressure control mechanisms that operate in the frequency band where regulation by the nervous regulatory mechanism occurs (referred to as "Short-Term Mechanisms for Arterial Pressure Regulation (STM)" in this specification) in order to accurately understand hemodynamics.

[0013] However, these two intermediate pressure control mechanisms are not clearly distinguishable by a certain frequency boundary; rather, there is a frequency band in which both are affected (referred to as the "overlap band (OLB)" in this specification), and they are loosely separated by this overlap band. Moreover, the frequency band in which the long-term blood pressure regulation mechanism operates, the frequency band in which the short-term blood pressure regulation mechanism operates, and the overlap band are not constant even for the same person, as they vary depending on their physical condition and measurement timing, as well as individual differences such as age, presence or absence of disease, and physical condition.

[0014] Therefore, the inventor focused on setting these frequency bands for each data set to be analyzed in order to improve the accuracy of understanding the circulatory dynamics of the person whose blood pressure is to be estimated, and thereby improve the accuracy of blood pressure estimation.

[0015] On the other hand, in Non-Patent Document 1, Guyton states regarding arterial pressure control by the nervous system that "any reflex pressure control mechanism will oscillate if the feedback is sufficiently strong and there is a delay between the excitation of baroreceptors and the subsequent pressure response. Vasomotor waves exhibit the characteristics of arterial pressure control by nerve reflexes and operate on the same principles as mechanical and electrical systems. If the feedback gain is large and there is a delay in the reaction time of the arterial pressure control mechanism, oscillations with lower frequencies and larger amplitudes than under normal conditions will occur."

[0016] A response with a large gain over a short period of time and a delay in reaction time is an impulse response. The action and compensation of arterial pressure control responses performed by long-term and short-term blood pressure regulatory mechanisms result in a step response with a phase difference. The Laplace transform is an extension of the Fourier transform, and the derivative at the inflection point of a frequency transfer function displayed on a log-log scale with the time axis changed to the frequency axis is the logarithmic value of the frequency ratio ω / ω0 expressed on a logarithmic scale. 10 The frequency gradient on the Bode plot, which has a phase lag φ drawn on ω / ω0, is qualitatively correlated with the frequency gradient. The fluctuating frequency gradient on the Bode plot is considered to qualitatively coincide with the fluctuation of the damping ratio in the vibration model system due to the biological system and the applied force, and the biological system and the compensatory force. The damping ratio is the ratio of the damping coefficient to the critical damping coefficient.

[0017] Furthermore, the Guyton model explains the above-mentioned non-neuronal relay pressure control mechanisms in terms of (1) the renin-angiotensin vasoconstriction system (RAVS) that operates on a minute or hourly basis, (2) the stress-relaxation mechanism of the vasculature (SRMV), and (3) the capillary fluid shift mechanism (CFSM). Of these, (1) RAVS has a clearly defined frequency, but the frequencies of (2) SRMV and (3) CFSM have not been clearly defined. Therefore, it cannot be said that the transient response of the relay pressure control mechanism involved in the short-term blood pressure regulation mechanism, which is a neuronal regulatory mechanism, and the long-term blood pressure regulation mechanism by the renal-hydraulic pressure regulation mechanism has not been sufficiently investigated in the past.

[0018] Therefore, the inventors also focused on examining the transient response of these intermediate pressure control mechanisms by applying a vibration model based on Guyton's theory mentioned above.

[0019] In other words, the present invention is A frequency analysis unit performs frequency analysis on time-series data related to instantaneous heart rate and obtains a spectral diagram on a log-log graph with frequency on the horizontal axis and power spectrum on the vertical axis. A frequency transfer function calculation unit that determines the frequency transfer function of the spectral diagram in the arterial pressure-related frequency band related to arterial pressure control, and determines a global approximation curve set by the frequency transfer function as an indicator showing the characteristics of the relay pressure control mechanism that affects both renal-body fluid pressure control and neuronal regulation included in arterial pressure control, A residual information calculation unit that calculates residual information including a residual function showing the relationship between the global approximation curve and the power spectrum and the frequency, A frequency band setting unit that uses the residual information to determine three frequency bands: a first effective blood pressure regulation band in which the long-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect the renal-body fluid pressure control is effectively operated; a second effective blood pressure regulation band in which the short-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect the nervous regulation is effectively operated; and an overlap band in which both the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism are in operation. A frequency gradient calculation unit that determines the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the first effective blood pressure regulation band as a first circulatory dynamics index reflecting the degree of excitation of the long-term blood pressure regulation mechanism, and determines the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the second effective blood pressure regulation band as a second circulatory dynamics index reflecting the degree of excitation of the short-term blood pressure regulation mechanism. The present invention provides a device for calculating circulatory dynamics indicators.

[0020] The residual information calculation unit is, As the residual information mentioned above, The residual function, which shows the distribution state of the residual plot with respect to the frequency, The mean residual approximation curve, which is an approximation curve of the mean residual, and Based on the aforementioned mean residual approximation curve, two residual probability distribution curves are used to approximate either a group of high residual plots consisting of residuals distributed in a region where the residual values ​​are above a predetermined level, or a group of low residual plots consisting of residuals distributed in a region where the residual values ​​are below a predetermined level. Preferably, the process includes a step to determine the result.

[0021] The frequency band setting unit is, Using the residual information, the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism are determined. The overlap band is identified based on the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism. The frequency band below the lower frequency limit is identified as the first effective blood pressure regulation band, and the frequency band above the upper frequency limit is identified as the second effective blood pressure regulation band. It is preferable that it has

[0022] The frequency band setting unit is, It is preferable to select the residual plots that determine the upper limit frequency of the long-term blood pressure regulation mechanism and the residual plots that determine the lower limit frequency of the short-term regulation mechanism from the group of low residual plots or the group of high residual plots.

[0023] The time-series data relating to the instantaneous heart rate is as follows: A Lorentz plot is applied to the time-series data of instantaneous heart rate to reconstruct a two-dimensional delayed coordinate system, and reference angle information (θ0) is obtained, which is the slope of the ensemble mean regression line in the reference time range, and individual angle information (θ) is obtained, which is the slope of the individual ensemble mean regression line of the two-dimensional delayed coordinate system in individual time ranges shorter than the reference time range. i ) difference (θ i-0 The difference (θ) obtained by the time series data analysis unit, which sequentially determines the difference (θ) while moving the individual time ranges, is obtained. i-0 This is time-series data of ) It is preferable.

[0024] The frequency transfer function calculation unit is, The global best approximation curve selection unit determines two types of frequency transfer functions, a quartic function and a quintic function, and selects the global best approximation curve to be used for analysis from among the global approximation curves represented by the respective quartic and quintic functions. has It is preferable.

[0025] The aforementioned global best approximation curve has an inflection point / intersection extraction unit that extracts inflection points and tangent intersection points. It is preferable.

[0026] The frequency band setting unit is, From among the residual plots belonging to the group of high residual plots or the group of low residual plots, the plots closest to the frequency corresponding to the inflection point or the intersection of the tangent lines are selected as candidates for the residual plot that determines the upper limit frequency of the long-term blood pressure regulation mechanism and the residual plot that determines the lower limit frequency of the short-term regulation mechanism. It is preferable.

[0027] The frequency band setting unit is, With respect to the first candidate band of the first effective blood pressure regulation band and the second candidate band of the second effective blood pressure regulation band, defined using the candidate plots of the residuals, the first local residual function and the second local residual function are determined, respectively. If the modification frequency that indicates the peak amplitude value of the second local residual function obtained for the second candidate band exists within the second candidate band, and the modification frequency that indicates the peak amplitude value of the first local residual function obtained for the first candidate band exists outside the second candidate band, The first candidate band and the second candidate band are determined to be the first effective blood pressure regulation band and the second effective blood pressure regulation band, respectively. It is preferable.

[0028] Furthermore, the database construction unit obtains the first and second hemodynamic indices with respect to time-series data of instantaneous heart rate for a predetermined number of subject groups, uses these as explanatory variables, and constructs a database of a three-dimensional response surface model with the measured systolic blood pressure data of each subject group as the dependent variable. An estimation index calculation unit that determines the first and second hemodynamic indices with respect to time-series data of the instantaneous heart rate of a subject for blood pressure estimation, and uses them as the first and second hemodynamic indices for estimation, respectively. A systolic blood pressure estimation unit applies the first and second circulatory dynamic indicators for estimation to the three-dimensional response surface model constructed by the database construction unit to obtain an estimated value of the systolic blood pressure of the subject for blood pressure estimation. has It is preferable.

[0029] If the estimated systolic blood pressure falls within the range of blood pressure abnormalities, including hypertension, the system includes a factor investigation unit that uses time-series data of the time period containing abnormal signs and time-series data of the time period not containing such abnormal signs, and performs analysis on each using the frequency analysis unit, frequency transfer function calculation unit, residual information calculation unit, frequency band setting unit, and frequency gradient calculation unit to calculate and compare the estimated systolic blood pressure values ​​and investigate the factors causing the blood pressure abnormality. It is preferable.

[0030] Furthermore, the present invention is A computer program that makes a computer function as a device for calculating circulatory dynamics indicators, The procedure for performing frequency analysis on time-series data related to instantaneous heart rate and obtaining a spectral diagram on a log-log graph with frequency on the x-axis and power spectrum on the y-axis, and A procedure for determining the frequency transfer function of the spectral diagram in the arterial pressure-related frequency band associated with arterial pressure control, and for determining the global approximation curve set by the frequency transfer function as an indicator showing the characteristics of the relay pressure control mechanism that affects both renal-body fluid pressure control and neuronal regulation included in arterial pressure control, A procedure for obtaining residual information including a residual function that shows the relationship between the residuals between the global approximation curve and the power spectrum and the frequency, A procedure for determining three frequency bands using the residual information, namely a first effective blood pressure regulation band in which the long-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect renal-body fluid pressure control is effectively operated, a second effective blood pressure regulation band in which the short-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect neuronal regulation is effectively operated, and an overlap band in which both the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism are in operation, The procedure involves determining the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the first effective blood pressure regulation band as a first circulatory dynamics index reflecting the degree of excitation of the long-term blood pressure regulation mechanism, and determining the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the second effective blood pressure regulation band as a second circulatory dynamics index reflecting the degree of excitation of the short-term blood pressure regulation mechanism. The present invention provides a computer program that causes the aforementioned computer to execute the following.

[0031] As the residual information mentioned above, The residual function, which shows the distribution state of the residual plot with respect to the frequency, The mean residual approximation curve, which is an approximation curve of the mean residual, and Based on the aforementioned mean residual approximation curve, two residual probability distribution curves are used to approximate either a group of high residual plots consisting of residuals distributed in a region where the residual values ​​are above a predetermined level, or a group of low residual plots consisting of residuals distributed in a region where the residual values ​​are below a predetermined level. Perform a procedure that includes the process of determining the value of the value. It is preferable.

[0032] Using the residual information, the procedure is performed to determine the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism, identify the overlap band based on the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism, and identify the frequency band below the lower frequency limit as the first effective blood pressure regulation band and the frequency band above the upper frequency limit as the second effective blood pressure regulation band. It is preferable.

[0033] The procedure involves selecting a residual plot that determines the upper frequency limit of the long-term blood pressure regulation mechanism and a residual plot that determines the lower frequency limit of the short-term regulation mechanism from either the group of low residual plots or the group of high residual plots. It is preferable.

[0034] The time-series data relating to the instantaneous heart rate is as follows: A Lorentz plot is applied to the time-series data of instantaneous heart rate to reconstruct a two-dimensional delayed coordinate system, and reference angle information (θ0) is obtained, which is the slope of the ensemble mean regression line in the reference time range, and individual angle information (θ) is obtained, which is the slope of the individual ensemble mean regression line of the two-dimensional delayed coordinate system in individual time ranges shorter than the reference time range. i ) difference (θ i-0 The difference (θ) obtained by the time series data analysis unit, which sequentially determines the difference (θ) while moving the individual time ranges, is obtained. i-0 This is time-series data of ) It is preferable.

[0035] The procedure involves determining two types of frequency transfer functions, a quartic function and a quintic function, and then selecting the best global approximation curve to be used for analysis from among the global approximation curves represented by the respective quartic and quintic functions. It is preferable.

[0036] The procedure for extracting inflection points and tangent intersection points of the aforementioned global best approximation curve is then executed. It is preferable.

[0037] The procedure involves selecting, from among the residual plots belonging to the group of high residual plots or the group of low residual plots, the plots closest to the frequency corresponding to the inflection point or the intersection of the tangent lines, as candidates for the residual plot that determines the upper limit frequency of the long-term blood pressure regulation mechanism and the residual plot that determines the lower limit frequency of the short-term regulation mechanism. It is preferable.

[0038] With respect to the first candidate band of the first effective blood pressure regulation band and the second candidate band of the second effective blood pressure regulation band, defined using the candidate plots of the residuals, the first local residual function and the second local residual function are determined, respectively. If the modification frequency that indicates the peak amplitude value of the second local residual function obtained for the second candidate band exists within the second candidate band, and the modification frequency that indicates the peak amplitude value of the first local residual function obtained for the first candidate band exists outside the second candidate band, The procedure is performed to determine the first candidate band and the second candidate band as the first effective blood pressure regulation band and the second effective blood pressure regulation band, respectively. It is preferable.

[0039] Furthermore, the procedure involves determining the first and second hemodynamic indices with respect to time-series data of instantaneous heart rate for a predetermined number of subject groups, using these as explanatory variables, and constructing a database of a three-dimensional response surface model with the measured systolic blood pressure data of each subject group as the dependent variable. The procedure involves determining the first and second hemodynamic indicators with respect to time-series data of the instantaneous heart rate of a subject for blood pressure estimation, and using them as the first and second hemodynamic indicators for estimation, respectively. The procedure involves applying the aforementioned first and second circulatory dynamic indicators for estimation to the three-dimensional response surface model constructed by the database construction unit, and then obtaining an estimated value of the systolic blood pressure of the subject for blood pressure estimation. It is preferable.

[0040] If the estimated systolic blood pressure falls within the range of blood pressure abnormalities, including hypertension, the procedure involves using the time-series data of the instantaneous heart rate data for the time period containing abnormal signs and the time-series data of the time period without such abnormal signs, and performing analyses on each using the frequency analysis unit, frequency transfer function calculation unit, residual information calculation unit, frequency band setting unit, and frequency gradient calculation unit to calculate and compare the estimated systolic blood pressure values, thereby investigating the cause of the blood pressure abnormality. It is preferable.

[0041] Furthermore, the present invention provides a computer-readable recording medium on which the above-mentioned computer program is recorded. [Effects of the Invention]

[0042] According to the present invention, by using time-series data of instantaneous heart rate and applying residual information including the frequency transfer function and residual function to the spectral diagram of a log-log graph with frequency on the horizontal axis and power spectrum on the vertical axis, a local approximation curve for determining the frequency gradient can be set to an appropriate frequency band for each data point. Consequently, the accuracy of the frequency gradients that serve as the first and second circulatory dynamics indices obtained from this local approximation curve is improved, and the accuracy of blood pressure estimation using these frequency gradients is also improved. [Brief explanation of the drawing]

[0043] [Figure 1] Figure 1 is a block diagram showing the overall configuration of a circulatory dynamics index calculation device according to one embodiment of the present invention. [Figure 2] Figure 2 is a block diagram showing the configuration of the frequency analysis unit. [Figure 3] Figure 3 is a flowchart showing the calculation process in the frequency analysis unit. [Figure 4] Figure 4 is a diagram showing the correspondence according to the flowchart in Figure 3. [Figure 5] Figure 5 is a flowchart showing the calculation process in the frequency transfer function calculation unit. [Figure 6] Figure 6 is a flowchart showing the process of identifying the upper limit of the long-term blood pressure regulation mechanism (LTM) and the lower limit of the short-term blood pressure regulation mechanism (STM) from the spectral diagram. [Figure 7] Figure 7 is a diagram showing the correspondence according to the flowchart in Figure 6. [Figure 8] Figure 8 is a diagram illustrating the inflection point and the intersection of the tangent lines. [Figure 9] Figure 9 is a flowchart showing the process from determining the first and second effective blood pressure regulation bands to determining the first and second hemodynamic indices. [Figure 10]Figure 10 is a diagram showing the correspondence according to the flowchart in Figure 9. [Figure 11] Figure 11 shows an example of a three-dimensional response surface model. [Figure 12] Figure 12 shows the results of Experimental Example 1. [Figure 13] Figure 13 shows the results of Experimental Example 2. [Figure 14] Figures 14(A) and (B) show the Lorentz plots reconstructing heart rate variability over a 360-second period, while Figure 14(C) shows the mean and standard deviation of the residuals. [Figure 15] Figures 15(A) and (B) show 10 cases in which frequency gradients were extracted using the data selected in Figures 14(A) and (B), and Figure 15(C) shows the distribution of the intersection points of frequency gradients on the contour lines of systolic blood pressure. [Figure 16] Figures 16(a) and 16(b) show how the residual function calculated by the residual information calculation unit 30 varies depending on the measured value of systolic blood pressure. [Figure 17] Figure 17 shows various data from a case study that provides clues for treatment planning in hypertensive patients. [Modes for carrying out the invention]

[0044] [Circulatory dynamics index calculation device 1] Figure 1 shows a schematic configuration of the hemodynamic index calculation device 1 of this embodiment. The hemodynamic index calculation device 1 of this embodiment consists of a computer (the type of computer is not limited and includes personal computers, microcomputers, mobile information terminals such as smartphones, etc.) and performs predetermined processing.

[0045] Specifically, the hemodynamic index calculation device 1 is configured such that a computer program is stored in a storage unit (including recording media such as a hard disk built into the hemodynamic index calculation device 1, which is composed of a computer, as well as various removable recording media and recording media of other computers connected by communication means). This program causes the device to perform procedures that function as a frequency analysis unit 10, a frequency transfer function calculation unit 20, a residual information calculation unit 30, a frequency band setting unit 40, a frequency gradient calculation unit 50, a database construction unit 60, an estimation index calculation unit 70, and a systolic blood pressure estimation unit 80. The hemodynamic index calculation device 1 can also be realized using an electronic circuit having one or more storage circuits into which the above computer program is incorporated. That is, it can be composed of a processing circuit that functions as a frequency analysis unit 10, a frequency transfer function calculation unit 20, a residual function calculation unit 30, a frequency band setting unit 40, a frequency gradient calculation unit 50, a database construction unit 60, an estimation index calculation unit 70, and a systolic blood pressure estimation unit 80.

[0046] Furthermore, computer programs can be provided by storing them on a recording medium. The recording medium storing the computer program may be a non-transient recording medium. While non-transient recording media are not particularly limited, examples include flexible disks, hard disks, CD-ROMs, MO (magneto-optical disks), DVD-ROMs, and memory cards. It is also possible to transmit and install computer programs to a computer via a communication line.

[0047] [Overall image of this embodiment] First, the overall function of the hemodynamic index calculation device 1 of this embodiment will be explained based on Figure 1. The frequency analysis unit 10 obtains time-series data of instantaneous heart rate using, for example, electrocardiogram waveform data. Next, this time-series data is analyzed, and a spectral diagram is obtained, which is displayed as a log-log graph plotting the power spectrum against frequency.

[0048] The frequency transfer function calculation unit 20 uses the frequency transfer function to obtain a global approximation curve in the frequency band related to arterial pressure control (global frequency band) that serves as an indicator of the operating state of the entire relay pressure control mechanism from the obtained spectral diagram. Furthermore, the residual information calculation unit 30 uses residual information, including the residual function, to identify the upper limit of the long-term blood pressure control mechanism (LTM) and the lower limit of the short-term blood pressure control mechanism (STM).

[0049] Next, the frequency band setting unit 40 identifies the interval between the upper frequency limit of the long-term blood pressure regulation mechanism (LTM) and the lower frequency limit of the short-term blood pressure regulation mechanism (STM) as the overlap band (OLB). Furthermore, within the range of the global frequency band, the interval below the lower frequency limit of the short-term blood pressure regulation mechanism (STM) is determined as the first effective blood pressure regulation band (Lower side Band: LSB) of the long-term blood pressure regulation mechanism (LTM), and the interval above the upper frequency limit of the long-term blood pressure regulation mechanism (LTM) is determined as the second effective blood pressure regulation band (Upper side Band: USB).

[0050] The frequency gradient calculation unit 50 recalculates approximation curves (local approximation curves) for the power spectra in the first effective blood pressure regulation band (LSB) and the second effective blood pressure regulation band (USB), respectively, and calculates the respective frequency gradients as the first and second hemodynamic indices (GT of LTM, GT of STM).

[0051] The database construction unit 60 measures the systolic blood pressure of a subject using an upper arm blood pressure monitor when instantaneous heart rate data is obtained, collects this measured systolic blood pressure data and first and second hemodynamic indices (GT of LTM, GT of STM) from a predetermined number of subjects, and constructs a database including a three-dimensional response surface model that shows the relationship between them. The estimation index calculation unit 70 determines the estimation first and second hemodynamic indices for the subject to be estimated, and the systolic blood pressure estimation unit 80 applies these estimation first and second hemodynamic indices to the three-dimensional response surface model to obtain an estimated value of the systolic blood pressure (eSBP) for the subject to be estimated. Hereinafter, each component that executes each of the above processes will be specifically described.

[0052] [Frequency Analysis Unit 10] As shown in FIG. 2, the frequency analysis unit 10 includes a time series data analysis unit 11 and a spectrum calculation unit 12. (Time Series Data Analysis Unit 11) The time series data analysis unit 11 includes a time series data analysis unit 11 that analyzes time series data of the instantaneous heart rate. The time series data analysis unit 11 reconstructs a two-dimensional delay coordinate system using the time series data of the instantaneous heart rate (HR = 60 / Δt) and obtains an attractor related to heart rate variability. The time series data of the instantaneous heart rate can be obtained, for example, using electrocardiogram data obtained from an electrocardiograph (ECG) (S1 in FIG. 3, FIG. 4(a)).

[0053] The Lorenz plot uses two points from the time series data of the instantaneous heart rate (HR) and plots while moving that range. Specifically, HR is on the x-axis i+1 (HR i+2 、HR i+3 ···), and HR is on the y-axis i (HR i+1 、HR i+2 ···), and their intersections are sequentially plotted. This is done for a reference time range (in this embodiment, 360 seconds), and the primary approximation line in the rectangular coordinate system calculated from the ensemble average of the plot group in the reference time range is defined as the main axis. The inclination of this main axis with respect to the x-axis is obtained as reference angle information (θ0) (S2 in FIG. 3, FIG. 4(b)).

[0054] 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 shorter than the reference time range (in this embodiment, 30 seconds), obtains a primary approximation line (center line) for the ensemble average of the plot group, and obtains the inclination of the line with respect to the x-axis as individual angle information (θ i ). The individual angle information (θ i) are sequentially determined by moving individual time ranges (30 seconds) with a predetermined overlap rate (e.g., 90%) (S2 in Figure 3, Figure 4(c)). Next, the time series data analysis unit 11 uses the reference angle information (θ0) and each individual angle information (θ i ) Difference angle information (θ i-0 ) is calculated sequentially, and further, the difference angle information (θ i-0 ) time series data (θ i-0 The time-series data is obtained (S3 in Figure 3, Figure 4(d)) and reconstructed in dot product representation. That is, θ i-0 The time-series data is displayed as the general angle between the principal axis and the midline, by converting it into the principal axis angle and the midline angle information over a 30-second period.

[0055] θ i-0 As shown in Figure 4(d), the time series data uses time on the horizontal axis and angle information of increase / decrease trend on the vertical axis (a + indicates a decreasing trend in instantaneous heart rate and a suppressive trend in sympathetic nerve activity when above the main axis, and a - indicates a increasing trend in instantaneous heart rate and a hyperactive trend in sympathetic nerve activity when below the main axis), with a difference (θ) calculated every 3 seconds at a 90% overlap rate. i-0 The graph will show the time series plotted by the difference (θ). i-0 The plot of θ is preferably at 3-second intervals. This is the average time that reflects the effects of parasympathetic nervous system activation and deactivation, and 30 seconds is approximately twice the average time of 12.5 seconds (10-15 seconds) that reflects the effects of sympathetic nervous system activation and deactivation. 360 seconds represents 1 / 4 of an ultradian cycle of fluctuations in alertness, drowsiness, and mental work capacity. i-0 The spectral diagram described later, obtained using time-series data, shows the global fluctuations of the arterial barodefence mechanism (10 -3 Information regarding baseline fluctuations at the Hz level and local fluctuations (10 -2 This will reflect information regarding vibrations at the Hz level.

[0056] (Spectrum calculation unit 12) The spectral calculation unit 12 calculates the θ obtained from the time series data analysis unit 11. i-0This method involves performing frequency analysis on time-series data and displaying it on a log-log graph with frequency on the horizontal axis and power spectrum on the vertical axis (Figures 3S4, S5, and 4(e)). Figure 4(e) is an example showing a log-log graph (spectrum diagram) of the power spectrum as a result of frequency analysis, with numerous points plotted. Figure 4(f) shows the RRI data, and Figure 4(g) shows the frequency analysis results. The two extreme values ​​in the spectral diagram of Figure 4(e) are absent in Figure 4(g), and the Mayer wave, respiration, and heart rate are captured, indicating that these biological signals are maintained with 1 / f fluctuations.

[0057] In this embodiment, frequency analysis was performed with a sampling frequency of 1000 Hz and a time resolution of 0.001 seconds. Therefore, the calculated effective bandwidth is up to 1000 / 2.5 = 400 Hz. Spectral analysis was performed in 2 n This becomes possible with the data. When performing spectral analysis using 360 seconds of time series data, 2 7 =128 comes close to this condition. In the moving average of the time series data analysis unit 11, as described above, the 30-second window is shifted by 3 seconds, and the first point becomes available for calculation after 30 seconds. 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 points are output over 360 seconds. In order to perform spectral calculation 2 7 Since 128 data points are needed, the missing 17 data points are obtained by folding the data. Assuming that the response to sympathetic nervous system stimulation is captured in 12.5 seconds, a low-pass filter is applied at 1 / 12.5 = 0.08 Hz. This results in 1 / 360 = 0.0028 Hz, but since the power spectrum near 0.0028 Hz contains drift and offset components, a high-pass filter is applied at 0.006 Hz, assuming a threshold of 0.0028 Hz × 2 = 0.0056 Hz or higher.

[0058] [Frequency transfer function calculation unit 20] The frequency transfer function calculation unit 20 performs a procedure to find an approximate curve for the arterial pressure-related frequency bands related to arterial pressure control. Specifically, as shown in Figure 5, it comprises a global approximate curve setting unit 21, a global best approximate curve selection unit 22, and an inflection point / intersection extraction unit 23. Before describing these individual components, we will first explain the frequency modifiers that determine the frequency bands to be analyzed, such as the arterial pressure-related frequency bands and the frequency bands of the long-term blood pressure regulation mechanism (LTM) and short-term blood pressure regulation mechanism (STM) mentioned above.

[0059] The frequencies that act as regulatory factors determining these frequency bands include the basic period of arterial pressure fluctuations, 60 seconds (1 / 60 ≈ 0.017 Hz), as described in Non-Patent Literature 1, and the recovery time of baroreceptor reflexes, 21 seconds (1 / 21 ≈ 0.0476 Hz), as described in Guyton's prior research. The basic period of arterial pressure fluctuations is 0.017 Hz when linearity is maintained, but when nonlinearity becomes strong in hypotension or hypertension conditions, frequency components of 0.034 Hz, 0.051 Hz, 0.068 Hz, 0.085 Hz, 0.102 Hz, etc., which are harmonic components of 0.017 Hz, are generated.

[0060] Considering the frequencies in order, we have the fundamental frequency of arterial pressure fluctuations at 0.017 Hz, the harmonic component of arterial pressure fluctuations at 0.034 Hz, the baroreceptor reflex at 0.0476 Hz, the harmonic component of arterial pressure fluctuations at 0.051 Hz, the LF component at 0.06 Hz, and the harmonic components of arterial pressure fluctuations at 0.068 Hz, 0.085 Hz, and 0.102 Hz. Although the frequency of the LF component is generally defined as 0.04 to 0.15 Hz, there is evidence that actual measurements mainly show a distribution between 0.06 and 0.11 Hz.

[0061] Therefore, the frequency band up to 0.06 Hz, which includes the fundamental frequency of arterial pressure fluctuations (0.017 Hz), its harmonic components (0.034 Hz ​​and 0.051 Hz), and 0.0476 Hz related to baroreceptor reflexes, was defined as the operating band of the short-term blood pressure regulation mechanism (STM).

[0062] 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 decreases, the sympathetic nervous system is stimulated by the baroreceptor reflex, followed by an increase in aldosterone secretion within minutes, modifying the pressure control characteristics of the renal-humoral mechanism. Fluctuations in the chemical reaction of aldosterone in the renal-humoral mechanism appear in the frequency gradient in the frequency band below 0.017 Hz. To capture the modification by aldosterone secretion, the basic time range in the time-series data analysis unit 11 is set to 360 seconds (1 / 180 ≈ 0.0056 Hz ≥ 0.0028 Hz ≈ 1 / 360) as described above. Then, a high-pass filter is set to 0.006 Hz to capture fluctuations in the renal-humoral mechanism. Furthermore, as mentioned above, a low-pass filter is set to 0.08 Hz to remove respiratory fluctuations, and the time resolution is set to 3 seconds because heart rate responds more quickly to parasympathetic nervous system stimulation than to sympathetic nervous system stimulation.

[0063] Based on these considerations, fluctuations in the long-term blood pressure regulation mechanism (LTM) will be examined primarily in the frequency band of 0.006 to 0.017 Hz, and the short-term blood pressure regulation mechanism (STM) will be examined primarily in the frequency band of 0.017 to 0.06 Hz. The arterial pressure-related frequency band related to arterial pressure control that will be analyzed in the frequency transfer function calculation unit 20 will be the combined frequency band of 0.006 to 0.06 Hz, encompassing both the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM).

[0064] (Global approximation curve setting unit 21) The frequency transfer function calculation unit 20 has a global approximation curve setting unit 21 that calculates the frequency transfer function in the arterial pressure-related frequency band in the spectrum diagram obtained by the spectrum calculation unit 12 of the frequency analysis unit 10 and sets a global approximation curve using this frequency transfer function. The global approximation curve setting unit 21 calculates a frequency transfer function that shows an approximation curve (the approximation curve relating to the entire arterial pressure-related frequency band is called the "global approximation curve") for the frequency band of 0.006 to 0.06 Hz among the plots of the power spectrum shown in the spectrum diagram. That is, the frequency transfer function is a function that shows a curve that approximates the distribution of each plot of the power spectrum by curve fitting in the spectrum diagram where frequency is the horizontal axis and power spectrum is the vertical axis, and is expressed as a fourth- or fifth-order function. This global approximation curve is a candidate index that shows the characteristics of the transient response by the relay pressure control mechanism that affects both renal-body fluid pressure control and neuronal regulation included in arterial pressure control.

[0065] (Global best approximation curve selection unit 22) The frequency transfer function calculation unit 20 further includes a global best approximation curve selection unit 22 and an inflection point / intersection point extraction unit 23. Of these, the global best approximation curve selection unit 22 selects the best approximation curve (global best approximation curve) to be used for analysis from among two types of global approximation curves represented by a quartic function and a quintic function, respectively, as determined by the global approximation curve setting unit 21. In other words, the two types of global approximation curves are candidates for indicators that show the characteristics of the intermediate pressure control mechanism, and the one corresponding to the relevant indicator is determined as the global best approximation curve. Since the global best approximation curve shows the characteristics of the intermediate pressure control mechanism (IPCM), it will be referred to as the "IPCM approximation curve" below and in the drawings as appropriate.

[0066] As selection criteria, it is preferable to use evaluation metrics related to the residuals (the difference along the vertical axis between the power spectrum plot and each approximation curve) between the global approximation curves represented by quartic and quintic functions and the spectral diagram. As evaluation metrics related to residuals, at least one of the following can be used: "the standard deviation (SD) of the residuals obtained for each power spectrum plot in the spectral diagram, the mean value of the absolute values ​​of the residuals (Mean(abs)), the best approximation error (εn), which is the difference between the standard deviation (SD) of the residuals and the mean value of the absolute values ​​of the residuals (Mean(abs)) (SD-Mean(abs)), and the distribution shape of the residuals (whether it is a symmetrical distribution or a distribution that spreads to one side or the other)."

[0067] In this embodiment, the best approximation error (εn) is used as the selection criterion. Specifically, a global approximation curve of either a quartic or quintic function with a best approximation error εn → 0 (in this specification, εn is 0 or close to 0, and this is referred to as the "latent factor (LF1)") is selected as the global best approximation curve (see S12 in Figure 6 and Figure 7(b)).

[0068] Here, we will explain why we included the best approximation error εn as a selection criterion. Let g(x) be a polynomial approximation function that satisfies the normal distribution in the interval (a,b) and is easy to handle numerically. i} i=1 n The value {gx} i} i=1 n We select an nth-degree polynomial function f(x) for this.

[0069] For a function g(x) that is continuous on the interval (a,b), the k-th degree polynomial f k Created using (x)

number

[0070] Furthermore, the fact that the random variable X follows a normal distribution means that the probability density of the best approximation function f(x) is as follows:

number

[0071] Based on the fact that a continuous function on a closed interval follows Weierstrass's theorem, a fundamental theorem of approximation theory, and a normal distribution, we have an nth-degree function f(x) and selected {x}. i} i=1 n From the residuals, which represent the error between the two points, we calculated the standard deviation (SD) of the residuals and the mean of the absolute values ​​of the residuals (Mean(abs)). We defined SD-Mean(abs) as εn, and the condition for selecting the best approximation curve was that εn→0, meaning that SD is as small as εn.

[0072] Here, if the homeostatic function performs a response that gives a large gain to the arterial pressure control mechanism, resulting in a delay in reaction time, in a short time, it becomes an impulse response. If we define the response of arterial pressure control as a unit impulse, the excitation of the feedback function of the pressure control mechanism can be represented by a delta function. Using the delta function, we obtain a weight function for the unit impulse. If we call the Laplace transform of the weight function the transfer function, then the transfer function is the ratio of the Laplace transforms of the output and the input. The Laplace transform of the input (unit impulse) is 1, and the Laplace transform of the response and the transfer function are equal. The transfer function is,

number

[0073] Therefore, by displaying the frequency analysis results of the transient response on a log-log axis and calculating the frequency gradient, the frequency transfer function can be estimated. Conversely, by setting the frequency transfer function and determining the frequency gradient, the operating state of the transient response of the intermediate pressure control mechanism can be determined.

[0074] (Inflection point / intersection point extraction part 23) The inflection point / intersection point extraction unit 23 extracts inflection points and tangent intersections for the global best approximation curve. That is, it differentiates the selected global best approximation curve to calculate the inflection point (IP) and finds the intersection of tangents (IT) to the inflection point. In S12 and S13 of Figure 6, if it is determined that a quintic function has been selected as the global best approximation curve, then IP and IT will be IP1, IT1, IP2, IT2, and IP3 in order from the low frequency side (see Figure 7(b)). If it is determined that a quartic function has been selected as the global best approximation curve, then IP and IT will be IP1, IT1, and IP2 in order from the low frequency side. The method for finding the intersection of tangents IT to the inflection point is as follows. As shown in Figure 8, the inflection points IP1, IP2, and IP3 are found in the global best approximation curve. The intersection of tangent a to IP1 and tangent b to IP2 is IT1, and the intersection of tangent b and tangent c to IP3 is IT2.

[0075] Here, we will explain the significance of inflection points and intersection points. First, the gradient of y=f(x) changes, and the gradient of the curve at an inflection point becomes the gradient of the tangent line at that point. The average gradient between two inflection points on the function y=f(x) is expressed in the form of a difference coefficient. The gradient in the limit as this difference becomes infinitely small is the derivative, or differential coefficient, of the function y=f(x) at an inflection point. If we draw a graph with the gradient of the curve y=f(x) as the vertical coordinate y', we obtain the first derivative of the original curve y=f(x), the gradient curve y'=f'(x'). Furthermore, from the derivative of y'=f'(x'), we obtain the second derivative of the original curve y=f(x), y''=f''(x). Since 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 (IP) is the point where the two adjustment mechanisms, LTM and STM, intersect. Furthermore, the intersection point (IT) where the tangents to the inflection points intersect represents the frequency at which the sign of the gradient curve changes. IP and IT determine the modification frequency of the arterial pressure regulation mechanism and the shape of the curve that reflects its control characteristics. The graph of the quartic function has oblique symmetry and three extrema. In some cases, two extrema are close together, reducing the number of extrema; these points of proximity are also points where the curves defined by the two regulation mechanisms intersect.

[0076] [Residual information calculation unit 30] The residual information calculation unit 30 obtains various information about the residual (residual information) in a frequency-residual correlation diagram with frequency on the horizontal axis and the residual on the vertical axis. As shown in Figure 7(b), the residual is the difference along the axis of the power spectrum between the global best approximation curve and each plot showing the power spectrum, and as shown in Figure 7(c), the frequency-residual correlation diagram is a figure with frequency on the horizontal axis and residual on the vertical axis. The residual information is used as an element to detect the frequency band containing information on the long-term blood pressure regulation mechanism (LTM), the frequency band containing information on the short-term blood pressure regulation mechanism (STM), and the overlap band (OLB) where the information of both overlaps, among the global best approximation curve obtained as an indicator of the relay pressure control mechanism in the global frequency band related to arterial pressure control. In this embodiment, the residual information includes the residual function, the residual mean approximation curve, and the residual probability distribution curve.

[0077] (Residual function) The residual function, as shown in Figure 7(c), is obtained by curve fitting the approximation curve of the residual plot shown in the frequency-residual correlation diagram, and is represented as a third-, fourth-, or fifth-order function. The frequency at which the amplitude peaks, which is the maximum value of the residual function (referred to herein as the "modification frequency"), indicates the intensity of the modification by the frequency at which the long-term blood pressure regulation mechanism (LTM) and the short-term blood pressure regulation mechanism (STM) are particularly active, and by its value (magnitude of amplitude).

[0078] The residual information calculation unit 30 calculates three residual functions. The first is a residual function calculated in the global frequency band range of 0.006 to 0.06 Hz (see S13 in Figure 6 and Figure 7(c)), and this residual function indicates the operating state of the relay pressure control mechanism across the entire global frequency band related to arterial pressure control. The second is a residual function indicating the operating state of the long-term blood pressure control mechanism (LTM), and the third is a residual function indicating the operating state of the short-term blood pressure control mechanism (STM) (see S14 in Figure 6 and Figure 7(d)). However, the second and third residual functions are calculated again after the frequency bands in which the long-term blood pressure control mechanism (LTM) and the short-term blood pressure control mechanism (STM) operate are set by the frequency band setting unit 40, which will be described later.

[0079] (Mean residual approximation curve) The mean residual approximation curve is an approximation curve that shows the mean of all residuals (see Figure 7(c)).

[0080] (Residual probability distribution curve) As shown in Figure 7(c), the residual probability distribution curve is divided into a high residual plot group (high group) consisting of residuals distributed in a region where the residual values ​​are above a predetermined level, and a low residual plot group (low group) consisting of residuals distributed in a region where the residual values ​​are below a predetermined level, with the mean residual approximation curve as the reference point. The approximation curve is an approximation curve obtained for each of these plot groups. The region where the residual values ​​are above a predetermined level or below a predetermined level, with the mean residual approximation curve as the reference point, can be defined as the region excluding plots where the difference from the mean residual approximation curve is within ±10% or within ±5%, for example. In this embodiment, the high residual plot group and the low residual plot group are constructed using plots in the region excluding plots within ±5%.

[0081] [Frequency band setting unit 40] The frequency band setting unit 40 has the function of determining three frequency bands: the first effective blood pressure regulation band (LSB) in which the long-term blood pressure regulation mechanism (LTM) operates effectively, the second effective blood pressure regulation band (USB) in which the short-term blood pressure regulation mechanism (STM) operates effectively, and the overlap band (OLB) (see Figure 10).

[0082] The frequency band setting unit 40 determines the upper limit of the long-term blood pressure regulation mechanism (LTM) and the lower limit of the short-term blood pressure regulation mechanism (STM), and uses these upper and lower limits to identify the first effective blood pressure regulation band (LSB), the second effective blood pressure regulation band (USB), and the overlap band (OLB).

[0083] First, the residual plots determining the upper frequency limit of the long-term blood pressure regulation mechanism (LTM) and the residual plots determining the lower frequency limit of the short-term regulation mechanism (STM) are selected from either the low residual plot group or the high residual plot group for which residual probability distribution curves have been obtained. The selection method is as follows.

[0084] A-1: Prerequisites (a): When the local residual function set in the first effective blood pressure regulation band (LSB), which is defined by the minimum value of the global frequency band, 0.006 Hz, and the upper frequency of the long-term blood pressure regulation mechanism (LTM), is represented by a third-, fourth-, or fifth-order residual function, the upper frequency will be near the minimum value on the higher frequency side of the modification frequency, which is the frequency of the peak value (maximum value) of the local residual function. (b): When the local residual function set in the second effective blood pressure regulation band (USB), which is defined by the maximum value of the global frequency band at 0.06 Hz and the lower limit frequency of the short-term blood pressure regulation mechanism (STM), is represented by a third-, fourth-, or fifth-order residual function, the lower limit frequency will be near the minimum value on the lower frequency side of the modification frequency, which is the frequency of the peak value (maximum value) of the local residual function.

[0085] A-2: Selection of Plot Groups (a): From A-1(a), the residual plot corresponding to the upper frequency limit of the long-term blood pressure regulation mechanism (LTM) is selected from the group of low residual plots. (b)-1: From A-1(b), the residual plot corresponding to the lower limit frequency of the short-term blood pressure regulation mechanism (LTM) is selected from the group of low residual plots. (b)-2: However, if the modification frequency is determined to be closer than a predetermined threshold to the second effective blood pressure regulation band (USB) (a predetermined threshold is set based on the distance along the frequency axis from the USB to determine whether it is close or not), it is selected from the residual high value plot group. This is constrained by the rate of change of the curvature of each approximation curve near the boundary between the long-term blood pressure regulation mechanism (LTM) and the short-term regulation mechanism (STM).

[0086] A-3: Selection of candidate plots for residuals (a): From the residual plots belonging to the group of low residual plots, candidates corresponding to the upper limit frequency of the long-term blood pressure regulation mechanism (LTM) are selected in order from the plots closest to the frequency corresponding to the inflection point (IP) or the intersection of the tangent lines (IT). (b)-1: In the case of A-2(b)-1, among the residual plots belonging to the group of low residual plots, candidates corresponding to the lower limit frequency of the short-term blood pressure regulation mechanism (LTM) are selected in order from the plots closest to the frequency corresponding to the inflection point (IP) or the intersection of the tangent lines (IT). (b)-2: In the case of A-2(b)-2, among the residual plots belonging to the group of high residual plots, candidates corresponding to the lower limit frequency of the short-term blood pressure regulation mechanism (LTM) are selected in order from the plots closest to the frequency corresponding to the inflection point (IP) or the intersection of the tangent lines (IT). In this way, once candidate residual plots corresponding to the upper and lower frequency limits have been selected, these are used to determine the three frequency bands. Specifically, this is as follows:

[0087] B-1: The first candidate band of the first effective blood pressure regulation band (LSB) is defined using candidate residual plots, and the second candidate band of the second effective blood pressure regulation band (USB) is defined. In defining the first and second candidate bands, a combination of candidates is used in which the upper frequency limit is higher than the lower frequency limit. B-2: For the first candidate bandwidth and the second candidate bandwidth, calculate the first local residual function and the second local residual function, respectively. B-3: Check whether the condition is met that the modification frequency representing the peak amplitude of the second local residual function is within the second candidate band, and the modification frequency representing the peak amplitude of the first local residual function is outside the second candidate band. B-4: The Upper Limit of LTM and Lower Limit of STM used for the first and second candidate bands that satisfy the conditions of B-3 are finally adopted, and each candidate band is determined as the first effective blood pressure regulation band (LSB) and the second effective blood pressure regulation band (USB) (see Figure 7(d)). The interval between the upper and lower frequencies is then determined as the overlap band (OLB) (see S21 in Figure 9 and Figure 10(d)).

[0088] [Frequency gradient calculation unit 50] The frequency gradient calculation unit 50 first uses the log-log graph display (spectrum diagram) of the power spectrum obtained by the spectrum calculation unit 12 to determine the first effective blood pressure regulation band (LSB) and the second effective blood pressure regulation band (USB) according to the residual information described above, and recalculates the approximation curves (local approximation curves) (S22 in Figure 9, Figures 10(b), (c)). In S23 (LF3), it is determined whether or not these local approximation curves can be represented by a cubic or quartic function. If they cannot be represented by a cubic or quartic function, the lower limit of the arterial pressure-related frequency band is changed to 0.009 Hz (L1) or 0.012 Hz (L2), or the upper limit of the arterial pressure-related frequency band is changed to 0.064 Hz (U1) (S29 in Figure 9), and the calculations for each step shown in Figures 2 to 7 are performed again (S30 in Figure 9).

[0089] If, in S23 of Figure 9, the local approximation curve is determined to be represented by a cubic or quartic function, this local approximation curve is adopted as the best approximation curve. The frequency gradient of the local approximation curve showing the distribution of the power spectrum in the first effective blood pressure regulation band (LSB) is determined as the first circulatory dynamics index (GT of LTM), and the frequency gradient of the local approximation curve showing the distribution of the power spectrum in the second effective blood pressure regulation band (USB) is determined as the second circulatory dynamics index (GT of STM) (S24 of Figure 9). The first circulatory dynamics index (GT of LTM) is an index that reflects the degree of excitation of the long-term blood pressure regulation mechanism (LTM), and the second circulatory dynamics index (GT of STM) is an index that reflects the degree of excitation of the short-term blood pressure regulation mechanism (STM).

[0090] Next, as S25 in Figure 9, it is checked whether the value of the second circulatory dynamics index (GT of STM), which is the frequency gradient of the local approximation curve in the second effective blood pressure regulation band (USB), is within the range of ±3 (S25 in Figure 9). If it is not within the range, the process returns to steps S29 and S30 in Figure 9 above. If it is within the range, the first-order approximation line is further determined from the local approximation curve in the first effective blood pressure regulation band (LSB) (S26 in Figure 9, Figure 10(d)). Next, it is determined whether the sign of this first-order approximation curve matches the sign of the frequency gradient of the first effective blood pressure regulation band (LSB) (S27:LF5 in Figure 9). If they do not match, the process returns to steps S9 and S30 in Figure 9. If they do match, the frequency gradients of the first effective blood pressure regulation band (LSB) and the second effective blood pressure regulation band (USB) that satisfy all the above conditions are ultimately determined to be the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM), respectively (S28 in Figure 9).

[0091] Furthermore, the frequency gradient is determined by the slope of the tangent line when the local approximation curves obtained from the log-log graph (spectral diagram) of the power spectrum obtained by the spectral calculation unit 12 are represented by a cubic function, and when a quartic function is selected, the slope of the double tangent line (the chord connecting the two inflection points) is used (see Figures 10(b) and (c)).

[0092] The hemodynamic index calculation device 1 of this embodiment has the function of estimating systolic blood pressure using a first hemodynamic index (GT of LTM) and a second hemodynamic index (GT of STM). Therefore, the hemodynamic index calculation device 1 has a computer program stored in its storage unit that causes it to execute procedures that function as a database construction unit 60, an estimation index calculation unit 70, and a systolic blood pressure estimation unit 80.

[0093] [Database Construction Section 60] The database construction unit 60 creates a three-dimensional response surface model using the first hemodynamic index (GT of LTM) and the second hemodynamic index (GT of STM), obtained from the time-series data of instantaneous heart rate for a predetermined number of subject groups, as explanatory variables, and the measured systolic blood pressure data for each of the subject groups as the dependent variable (see Figure 11). As shown in (1) of Figure 11, the individual first hemodynamic index (e.g., GT of LTM = 0.7991), the second hemodynamic index (e.g., GT of STM = -0.0065), and the measured systolic blood pressure (SBP = 137 mmHg) measured by an upper arm blood pressure monitor are plotted on a three-dimensional response surface with GT of LTM, GT of STM, and SBP as the X, Y, and Z axes, respectively, as shown in (2). As a result, as shown in (3), in this example, a three-dimensional response surface model represented by a fourth-order polynomial is constructed. The database construction unit 60 stores the obtained three-dimensional response surface model as a database in the storage unit of the circulatory dynamics index calculation device 1.

[0094] [Estimation index calculation unit 70] The estimation index calculation unit 70 obtains time-series data of the instantaneous heart rate of the person whose blood pressure is to be estimated (the subject for blood pressure estimation), analyzes this data, and determines the first circulatory dynamic index (GT of LTM) and the second circulatory dynamic index (GT of STM) of the subject for blood pressure estimation, which are then used as the first circulatory dynamic index (estimated GT of LTM) and the second circulatory dynamic index (estimated GT of STM), respectively. The first circulatory dynamic index (GT of LTM) and the second circulatory dynamic index (GT of STM) are determined by the frequency analysis unit 10, frequency transfer function calculation unit 20, residual information calculation unit 30, frequency band setting unit 40, and frequency gradient calculation unit 50 described above.

[0095] [Systolic blood pressure estimation unit 80] As shown in Figure 1, the systolic blood pressure estimation unit 80 applies the first estimated circulatory dynamic index (estimated GT of LTM) and the second estimated circulatory dynamic index (estimated GT of STM) obtained by the estimation index calculation unit 70 to a three-dimensional response surface model constructed by the database construction unit 10, estimates the systolic blood pressure of the subject for blood pressure estimation, and outputs the estimated value.

[0096] In the above explanation, the time-series data of instantaneous heart rate analyzed by the time-series data analysis unit 11 is based on data obtained from an electrocardiograph (ECG). However, it is not limited to this, as long as similar data can be obtained from other devices. For example, a smartwatch, which is a type of electrocardiograph, or other wearable devices can be used. Alternatively, time-series waveform data that approximates the RR interval (RRI) can be obtained by capturing biosignal detection data (acoustic pulse wave: APW) from the body surface on the back using a biosignal detection sensor incorporated in the product name: Sleep Buster manufactured by Delta Tooling Co., Ltd., and filtering the time-series waveform in a predetermined frequency band.

[0097] (Experimental Example 1) The experiment involved subjects aged 20 to 90 years old in a resting seated position with natural breathing. A total of 137 subjects were included: a healthy control group (88 subjects) who were not receiving medical treatment, and a group of outpatients (49 subjects) with cardiovascular diseases such as heart disease, hypertension, gout, diabetes, and dyslipidemia, or their risk factors. Each subject underwent only one electrocardiogram (ECG) test. For all 137 subjects (225 cases), heart rate data was collected for 360 seconds using an ECG while in a resting seated position, and systolic blood pressure (SBP) was measured using an upper arm blood pressure monitor. Measurements were taken at three locations: Delta Touring Co., Ltd., Shiga University of Medical Science, and Fukui Prefectural Saiseikai Hospital. At Delta Touring Co., Ltd. and Shiga University of Medical Science, car seats were used as experimental equipment. At Fukui Prefectural Saiseikai Hospital, a relaxation chair was used. The experimental equipment, researchers, subjects, analysts, and seasons in which the experiments were conducted all differed. Delta Tooling Co., Ltd., Shiga University of Medical Science, and Fukui Saiseikai Hospital recorded the average of two measurements for upper arm systolic blood pressure. However, if there was a difference of 5 mmHg or more between the two measurements, measurements were taken up to three or four times, and the average of the two closest measurements was recorded. Delta Tooling Co., Ltd. conducted a multi-measurement experiment with the same subject. In the multi-measurement experiment, if data with a blood pressure fluctuation range exceeding 20 mmHg was obtained, the minimum, average, and maximum values ​​were clearly indicated and analyzed.

[0098] The three-dimensional response surface model shown in Figure 11 was created using data from these 137 subjects (225 cases).

[0099] (Experimental Example 2) A one-year blood pressure measurement experiment was conducted with the cooperation of a 67-year-old subject (121 measurements) who maintained normal to high blood pressure through medication. The conditions for collecting heart rate data using an electrocardiograph and measuring systolic blood pressure using an upper arm blood pressure monitor were the same as in Experimental Example 1.

[0100] Figure 12 shows the correlation between the estimated systolic blood pressure (eSBP) obtained by the 3D response surface model in Figure 11 and the measured systolic blood pressure (SBP), using the data of the subjects in Experiment Example 1 as the first estimated circulatory dynamic index (estimated GT of LTM) and the second estimated circulatory dynamic index (estimated GT of STM). The coefficient of determination is R2 The result was 0.7733.

[0101] Here, considering the relationship between the GT of LTM and GT of STM in the 3D response surface model in Figure 11, the coefficient of determination is R 2 = 0.0136, indicating no correlation. The coefficient of determination for the relationship between SBP and GT of LTM was R 2 = 0.2065, and the coefficient of determination in the relationship between SBP and GT of STM is R 2 The correlation was 0.3486, indicating only a moderate correlation. However, when the first circulatory dynamics index (GT of LTM) and the second circulatory dynamics index (GT of STM) were applied as explanatory variables to the three-dimensional response surface model, an estimate of systolic blood pressure with a high correlation to the measured value was obtained.

[0102] Figure 13 shows the correlation between the estimated systolic blood pressure (eSBP) obtained using the 3D response surface model in Figure 11, and the measured systolic blood pressure (SBP), using the data of the subjects in Experimental Example 2 as the first estimated circulatory dynamic index (estimated GT of LTM) and the second estimated circulatory dynamic index (estimated GT of STM). The coefficient of determination is R 2 The result was 0.9, indicating an extremely high correlation. Figures 12 and 13 show frequency gradient plots for the 10 cases (a) to (j) shown in Figure 14, which will be discussed later. Both plots were shown to have statistical properties. In addition, errors occurred in the data near normal blood pressure in both cases, but the errors in Figure 12 were a combination of random and systematic errors, while the errors in Figure 13 were random errors.

[0103] (Consideration 1) Figures 14(A) and (B) show Lorentz plots reconstructing heart rate variability over a 360-second period. Figure 14(A) shows six cases selected from 137 subjects and 225 cases, while Figure 14(B) shows four cases extracted from 121 cases involving a 67-year-old subject. These Lorentz plots allowed for the classification of the sample space into discrete and continuous sample spaces. Figure 14(C) shows that the sample space could be represented as discrete and continuous random variables based on the mean and standard deviation of the attractor structure. The gray points in Figure 14(C) represent the plots for all 225 cases. The Lorenz plot demonstrated that extreme differences, such as the presence or absence of arrhythmias, could be captured. It allowed for a classification of heartbeats into normal (b)(d)(e)(g)(h)(i)(j) and abnormal (a)(c)(f).

[0104] (Consideration 2) Figures 15(A) and (B) show 10 cases in which IPCM approximation curves were created from the data selected in Figures 14(A) and (B), and the frequency gradient was extracted from the best approximation curves of the LTM and STM local approximations. By converting the dot product information into a frequency gradient via the IPCM approximation curve, residual function, and best approximation curve of the local approximations, it was suggested that each case may possess statistical properties. In the 10 cases where frequency gradients were extracted from the best approximation curves for LTM and STM, the overall shape of the graphs was different in all cases, indicating that they were independent variables. The frequency gradients of LTM and STM, which became independent variables, lay on the IPCM approximation curve. As shown in Figure 15(C), observing the intersection of the frequency gradients through the systolic blood pressure contour plot (SBP contour plot) suggested the possibility of statistical properties.

[0105] (Consideration 3) Figure 16 shows how the residual function calculated by the residual information calculation unit 30 varies depending on the measured value of systolic blood pressure. Figure 16(a) shows the analysis results for 6 individuals and 6 cases extracted from 137 individuals and 225 cases. Figure 16(b) shows the results for 1 individual and 4 cases in Experiment Example 2, analyzing the relationship between systolic blood pressure and the residual function within each individual subject. In Figure 16(a), the frequency of the lower limit of STM of the OLB varied between 0.018Hz and 0.033Hz, and the frequency of the upper limit of LTM varied between 0.03Hz and 0.045Hz. The OLB remained within a range of 0.012Hz. In Figure 16(b), the frequency of the lower limit of the outer line of blood pressure (OLB) at the stomata (STM) ranged from 0.018 Hz to 0.027 Hz, while the frequency of the upper limit of the long line of blood pressure (LTM) ranged from 0.026 Hz to 0.03 Hz. The OLB remained within a range of 0.009 Hz. As blood pressure increased, the OLB narrowed from 0.009 Hz to 0.003 Hz. Except for the hypertensive case in Figure 16(b), the modifier frequencies in Figures 16(a) and (b) did not fluctuate significantly, and the range of residual fluctuations was similar. The modifier frequencies occurred within 0.01–0.03 Hz in LSB and within 0.03–0.06 Hz in USB. In the case of hypertension shown in Figure 16(b), the modification frequencies of LSB and USB occurred in the range of 0.02–0.03 Hz. The way the two residual functions fluctuate depending on blood pressure may reveal a regularity between arterial pressure and the statistical properties of the residuals. Furthermore, the relative fluctuations between the residual distribution curves may potentially capture the sensitivity of the arterial pressure control response. Therefore, the best approximation curve and frequency gradient calculated from the approximation curve and residual function can serve as indicators for tracking fluctuations in arterial pressure, and it was suggested that the modification frequency of the residual function outside the OLB, which arises from the mutual modification of LTM and STM, may be involved in arterial pressure control.

[0106] (Consideration 4) [Case studies that provide clues for treatment planning in hypertensive patients] A detailed study was conducted on a hypertensive subject. This subject had been noted to exhibit ventricular premature contractions (VPBs). Analysis was performed using electrocardiogram (ECG) RRI data where VPBs regularly occurred between normal heartbeats, and also using ECG RRI data during periods when VPBs were not present. The results are shown in Figure 17. In Figure 17, (with) indicates the analysis of data during periods containing VPBs, and (without) indicates the analysis of data during periods when VPBs were not present. In the ECG RRI data in Figure 17 (1-1), VPBs appear where indicated by the arrows. (6) shows the results of examining IPCM approximation curves using fourth-order and fifth-order approximation curves for data without VPBs. These were obtained by re-examining the target frequency band for analysis because the arterial pressure-related frequency band did not satisfy each latent factor. The fifth-order approximation curve, which showed the smallest εn of 0.0221, was adopted. In Figure 17, (1-1) and (1-2) show electrocardiogram RRI data, and (2-1) and (2-2) show Lorentz plots. (3) is a figure comparing time-series data for θi-0, and (4) is a figure comparing IPCM approximation curves. (5)(a) is a figure showing the residual mean approximation curve, residual probability distribution curve, and local residual functions in LSB and USB. (5)(b) is a figure showing the best approximation curve of the local approximation curve obtained in LSB and USB, and the frequency gradient (GT of LTM, GT of STM) obtained therefrom.

[0107] By removing the variability in HRV and performing the analysis, the GT of LTM changed from 1.048 to -0.3616, the GT of STM changed from 1.683 to -0.2775, and e SBP changed from 177 mmHg (SBP: 168 mmHg) to 129 mmHg. In the θi-0 time series data with VPB, the reset of the reference level to hypertension suggests a case of secondary hypertension. That is, the presence or absence of VPB is reflected in the gain of the power spectrum, causing a difference in OLB and resulting in different frequency gradients. Even without VPB, the variance from the residual probability distribution curve and the residual approximation curve is large, but the power spectrum of the LSB in the approximation curve (6) becomes linear, the width of the OLB narrows, and a difference in statistical properties occurs. The white noise treatment of the LSB increases the gain of the residual distribution curve and flattens the frequency gradient, suggesting the possibility of renal dysfunction as the cause of hypertension.

[0108] Since chronic hypertension is thought to be caused by microvascular damage and tubular dryness damage in the kidneys, which inhibit sodium excretion, and by abnormalities in the hormonal factor: the renin-angiotensin system, this embodiment, which includes LSB as a target for investigation, is effective in investigating the causes of diseases such as hypertension. Specifically, by analyzing and comparing instantaneous heart rate data from time periods showing abnormal signs in electrocardiogram waveform data with instantaneous heart rate data from time periods without abnormal signs, a difference in the estimated systolic blood pressure can be found, which can be used as one piece of data to investigate the causes of blood pressure abnormalities such as hypertension.

[0109] Therefore, it is preferable that the hemodynamic index calculation device 1 has a configuration that, when the estimated value of systolic blood pressure falls within the range of blood pressure abnormalities including hypertension, uses time-series data of time periods containing abnormal signs and time-series data of time periods not containing abnormal signs included in the time-series data of instantaneous heart rate, performs analysis on each using the frequency analysis unit, the frequency transfer function calculation unit, the residual information calculation unit, the frequency band setting unit, and the frequency gradient calculation unit, calculates the estimated value of systolic blood pressure from each, compares them, and has a factor investigation unit that searches for the cause of the blood pressure abnormality. [Explanation of Symbols]

[0110] 1 Circulatory dynamics index calculation device 10 Frequency Analysis Unit 20 Frequency Transfer Function Calculation Unit 30 Residual information calculation section 40 Frequency Band Setting Section 50 Frequency gradient calculation unit 60 Database Construction Department 70 Estimation index calculation unit 80 Systolic blood pressure estimation unit

Claims

1. A frequency analysis unit performs frequency analysis on time-series data related to instantaneous heart rate and obtains a spectral diagram on a log-log graph with frequency on the horizontal axis and power spectrum on the vertical axis. A frequency transfer function calculation unit that determines the frequency transfer function of the spectral diagram in the arterial pressure-related frequency band related to arterial pressure control, and determines the global approximation curve set by the frequency transfer function as an indicator showing the characteristics of the relay pressure control mechanism that affects both renal-body fluid pressure control and neuronal regulation included in arterial pressure control, A residual information calculation unit that calculates residual information including a residual function showing the relationship between the global approximation curve and the power spectrum and the frequency, A frequency band setting unit that uses the residual information to determine three frequency bands: a first effective blood pressure regulation band in which the long-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect the renal-body fluid pressure control is effectively operated; a second effective blood pressure regulation band in which the short-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect the nervous regulation is effectively operated; and an overlap band in which both the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism are in operation. A frequency gradient calculation unit that calculates the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the first effective blood pressure regulation band as a first circulatory dynamics index reflecting the degree of excitation of the long-term blood pressure regulation mechanism, and the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the second effective blood pressure regulation band as a second circulatory dynamics index reflecting the degree of excitation of the short-term blood pressure regulation mechanism. A device for calculating circulatory dynamics indicators.

2. The residual information calculation unit is, As the residual information mentioned above, The residual function, which shows the distribution state of the residual plot with respect to the frequency, The mean residual approximation curve, which is an approximation curve of the mean residual, and Based on the aforementioned mean residual approximation curve, two residual probability distribution curves are used to approximate either a group of high residual plots consisting of residuals distributed in a region where the residual values ​​are above a predetermined level, or a group of low residual plots consisting of residuals distributed in a region where the residual values ​​are below a predetermined level. Includes the process of determining The circulatory dynamics index calculation device according to claim 1.

3. The frequency band setting unit is, Using the residual information, the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism are determined. The overlap band is identified based on the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism. The frequency band below the lower frequency limit is identified as the first effective blood pressure regulation band, and the frequency band above the upper frequency limit is identified as the second effective blood pressure regulation band. A circulatory dynamics index calculation device according to claim 1, having the following features.

4. The frequency band setting unit is, The residual plots determining the upper limit frequency of the long-term blood pressure regulation mechanism and the residual plots determining the lower limit frequency of the short-term regulation mechanism are selected from the group of low residual plots or the group of high residual plots. The circulatory dynamics index calculation device according to claim 3.

5. The time-series data relating to the instantaneous heart rate is as follows: A Lorentz plot is applied to the time-series data of instantaneous heart rate to reconstruct a two-dimensional delayed coordinate system, and reference angle information (θ) is the slope of the regression line of the ensemble mean in the reference time range. ０ ) and individual angular information (θ), which is the slope of the regression line of the individual ensemble average of the two-dimensional delayed coordinate system in an individual time range shorter than the reference time range. ｉ ) difference (θ ｉ－０ The difference (θ) obtained by the time series data analysis unit, which sequentially determines the difference (θ) while moving the individual time ranges, is obtained by the time series data analysis unit. ｉ－０ This is time-series data of ) The circulatory dynamics index calculation device according to claim 1.

6. The frequency transfer function calculation unit is, The global best approximation curve selection unit determines two types of frequency transfer functions, a quartic function and a quintic function, and selects the global best approximation curve to be used for analysis from among the global approximation curves represented by the respective quartic and quintic functions. has The circulatory dynamics index calculation device according to claim 1.

7. The aforementioned global best approximation curve has an inflection point / intersection extraction unit that extracts inflection points and tangent intersection points. The circulatory dynamics index calculation device according to claim 6.

8. The frequency band setting unit is, From among the residual plots belonging to the group of high residual plots or the group of low residual plots, the plots closest to the frequency corresponding to the inflection point or the intersection of the tangent lines are selected as candidates for the residual plot that determines the upper limit frequency of the long-term blood pressure regulation mechanism and the residual plot that determines the lower limit frequency of the short-term regulation mechanism. The circulatory dynamics index calculation device according to claim 7.

9. The frequency band setting unit is, With respect to the first candidate band of the first effective blood pressure regulation band and the second candidate band of the second effective blood pressure regulation band, which are defined using the candidate plots of the residuals, the first local residual function and the second local residual function are determined, respectively. If the modification frequency that indicates the peak amplitude value of the second local residual function obtained with respect to the second candidate band is located within the second candidate band, and the modification frequency that indicates the peak amplitude value of the first local residual function obtained with respect to the first candidate band is located outside the second candidate band, The first candidate band and the second candidate band are determined to be the first effective blood pressure regulation band and the second effective blood pressure regulation band, respectively. The circulatory dynamics index calculation device according to claim 8.

10. Furthermore, the database construction unit obtains the first and second hemodynamic indices with respect to time-series data of instantaneous heart rate for a predetermined number of subject groups, uses these as explanatory variables, and constructs a database of a three-dimensional response surface model with the measured systolic blood pressure data of each subject group as the dependent variable. The first and second hemodynamic indicators are determined with respect to time-series data of the instantaneous heart rate of the subject to blood pressure estimation, and these are used as the first and second hemodynamic indicators for estimation, respectively, in the estimation indicator calculation unit. A systolic blood pressure estimation unit applies the first and second circulatory dynamic indicators for estimation to the three-dimensional response surface model constructed by the database construction unit to obtain an estimated value of the systolic blood pressure of the subject for blood pressure estimation. has The circulatory dynamics index calculation device according to claim 1.

11. If the estimated systolic blood pressure falls within the range of blood pressure abnormalities, including hypertension, the system includes a factor investigation unit that uses time-series data of the time period containing abnormal signs and time-series data of the time period not containing such abnormal signs, and performs analysis on each using the frequency analysis unit, frequency transfer function calculation unit, residual information calculation unit, frequency band setting unit, and frequency gradient calculation unit to calculate and compare the estimated systolic blood pressure values ​​and investigate the factors causing the blood pressure abnormality. The circulatory dynamics index calculation device according to claim 10.

12. A computer program that makes a computer function as a device for calculating circulatory dynamics indicators, The procedure for performing frequency analysis on time-series data related to instantaneous heart rate and obtaining a spectral diagram on a log-log graph with frequency on the x-axis and power spectrum on the y-axis, and A procedure for determining the frequency transfer function of the spectral diagram in the arterial pressure-related frequency band associated with arterial pressure control, and for determining the global approximation curve set by the frequency transfer function as an indicator showing the characteristics of the relay pressure control mechanism that affects both renal-body fluid pressure control and neuronal regulation included in arterial pressure control, A procedure for obtaining residual information including a residual function that shows the relationship between the residuals between the global approximation curve and the power spectrum and the frequency, A procedure for determining three frequency bands using the residual information, namely a first effective blood pressure regulation band in which the long-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect the renal-body fluid pressure control is effectively operated, a second effective blood pressure regulation band in which the short-term blood pressure regulation mechanism among the relay pressure control mechanisms that affect the nervous regulation is effectively operated, and an overlap band in which both the long-term blood pressure regulation mechanism and the short-term blood pressure regulation mechanism are in operation, The procedure involves determining the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the first effective blood pressure regulation band as a first circulatory dynamics index reflecting the degree of excitation of the long-term blood pressure regulation mechanism, and determining the frequency gradient of a local approximation curve showing the distribution state of the power spectrum in the second effective blood pressure regulation band as a second circulatory dynamics index reflecting the degree of excitation of the short-term blood pressure regulation mechanism. A computer program that causes the aforementioned computer to execute.

13. As the residual information mentioned above, The residual function, which shows the distribution state of the residual plot with respect to the frequency, The mean residual approximation curve, which is an approximation curve of the mean residual, and Based on the aforementioned mean residual approximation curve, two residual probability distribution curves are used to approximate either a group of high residual plots consisting of residuals distributed in a region where the residual values ​​are above a predetermined level, or a group of low residual plots consisting of residuals distributed in a region where the residual values ​​are below a predetermined level. Perform a procedure that includes the process of determining the value of the value. The computer program according to claim 12.

14. Using the residual information, the procedure is performed to determine the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism, identify the overlap band based on the upper frequency limit of the long-term blood pressure regulation mechanism and the lower frequency limit of the short-term blood pressure regulation mechanism, and identify the frequency band below the lower frequency limit as the first effective blood pressure regulation band and the frequency band above the upper frequency limit as the second effective blood pressure regulation band. The computer program according to claim 12.

15. The procedure involves selecting a residual plot that determines the upper limit frequency of the long-term blood pressure regulation mechanism and a residual plot that determines the lower limit frequency of the short-term regulation mechanism from either the group of low residual plots or the group of high residual plots. The computer program according to claim 14.

16. The time-series data relating to the instantaneous heart rate is as follows: A Lorentz plot is applied to the time-series data of instantaneous heart rate to reconstruct a two-dimensional delayed coordinate system, and reference angle information (θ) is the slope of the regression line of the ensemble mean in the reference time range. ０ ) and individual angular information (θ), which is the slope of the regression line of the individual ensemble average of the two-dimensional delayed coordinate system in an individual time range shorter than the reference time range. ｉ ) difference (θ ｉ－０ The difference (θ) obtained by the time series data analysis unit, which sequentially determines the difference (θ) while moving the individual time ranges, is obtained by the time series data analysis unit. ｉ－０ This is time-series data of ) The computer program according to claim 12.

17. The procedure involves determining two types of frequency transfer functions, a quartic function and a quintic function, and then selecting the best global approximation curve to be used for analysis from among the global approximation curves represented by the respective quartic and quintic functions. The computer program according to claim 12.

18. The procedure for extracting inflection points and tangent intersection points of the aforementioned global best approximation curve is then executed. The computer program according to claim 17.

19. The procedure involves selecting, from among the residual plots belonging to the group of high residual plots or the group of low residual plots, the plots closest to the frequency corresponding to the inflection point or the intersection of the tangent lines, as candidates for the residual plot that determines the upper limit frequency of the long-term blood pressure regulation mechanism and the residual plot that determines the lower limit frequency of the short-term regulation mechanism. The computer program according to claim 18.

20. With respect to the first candidate band of the first effective blood pressure regulation band and the second candidate band of the second effective blood pressure regulation band, which are defined using the candidate plots of the residuals, the first local residual function and the second local residual function are determined, respectively. If the modification frequency that indicates the peak amplitude value of the second local residual function obtained with respect to the second candidate band is located within the second candidate band, and the modification frequency that indicates the peak amplitude value of the first local residual function obtained with respect to the first candidate band is located outside the second candidate band, The procedure is performed to determine the first candidate band and the second candidate band as the first effective blood pressure regulation band and the second effective blood pressure regulation band, respectively. The computer program according to claim 19.

21. Furthermore, the procedure involves determining the first and second hemodynamic indices with respect to time-series data of instantaneous heart rate for a predetermined number of subject groups, using these as explanatory variables, and constructing a database of a three-dimensional response surface model with the measured systolic blood pressure data of each subject group as the dependent variable. The procedure involves determining the first and second hemodynamic indicators with respect to time-series data of the instantaneous heart rate of a subject for blood pressure estimation, and using them as the first and second hemodynamic indicators for estimation, respectively. The procedure involves applying the first and second circulatory dynamic indicators for estimation to the three-dimensional response surface model constructed by the database construction unit, and then obtaining an estimated value of the systolic blood pressure of the subject for blood pressure estimation. The computer program according to claim 12.

22. If the estimated systolic blood pressure falls within the range of blood pressure abnormalities, including hypertension, the procedure involves using the time-series data of the instantaneous heart rate data for the time period containing abnormal signs and the time-series data of the time period without such abnormal signs, and performing analyses on each using the frequency analysis unit, frequency transfer function calculation unit, residual information calculation unit, frequency band setting unit, and frequency gradient calculation unit to calculate and compare the estimated systolic blood pressure values, thereby investigating the cause of the blood pressure abnormality. The computer program according to claim 21.

23. A computer-readable recording medium on which a computer program according to any one of claims 12 to 22 is recorded.

Images