JP6778369B2 - Biological signal analysis system and program - Google Patents

Description

The present invention relates to a biological signal analysis system for analyzing biological signals, and more particularly to a biological signal analysis system suitable for analyzing fluctuations in heartbeat.

In the past, there have been many cases in which a person who seemed to have no problem at first glance suddenly complained of a heart condition and became seriously ill.

On the other hand, since the 1980s, research focusing on the correlation between heartbeat fluctuations and health status has been progressing, and along with this, various studies have been conducted on methods for analyzing heartbeat fluctuations. In this regard, Non-Patent Document 1 discloses a method for analyzing heartbeat fluctuations using DFA (Detrended Fluctuation Analysis).

C.-K.Peng, S. Havlin, H.E. Stanley, and A. L. Goldberger, Quantification of scaling exponents and crossover phenomenain nonstationaryheartbeat time series) ”, Chaos, Volume 5 (Vol.5), 1995, pp.82-87

An object of the present invention is to provide a novel biological signal analysis system capable of providing information useful for detecting signs of cardiac dysfunction.

As a result of diligent studies on the configuration of a novel biological signal analysis system capable of providing information useful for detecting signs of cardiac dysfunction, the present inventor came up with the following configuration and arrived at the present invention.

That is, according to the present invention, a heartbeat component extraction unit that extracts a voltage signal containing a heartbeat component as a main component from a biological signal, a heartbeat generation time acquisition unit that acquires a heartbeat generation time based on the voltage signal, and a heartbeat generation time acquisition unit. The DFA analysis unit for executing DFA based on the time-series data of the heartbeat interval calculated from the heartbeat occurrence time, and the health condition information that presents the scaling index calculated by the DFA analysis unit as an index indicating the health condition of the heart. A biological signal analysis system including a health condition information generation unit to be generated is provided.

As described above, the present invention provides a novel biological signal analysis system capable of providing information useful for detecting signs of cardiac dysfunction.

The outline block diagram of the biological signal analysis system of this embodiment. The functional block diagram of the biological signal analysis system of this embodiment. The figure which shows the waveform of the heartbeat pulse signal extracted by the heartbeat component extraction part of this embodiment. The flowchart which shows the heartbeat occurrence time acquisition processing of this embodiment. The conceptual diagram for demonstrating the heartbeat occurrence time acquisition process of this embodiment. The flowchart which shows the analysis target data generation processing of this embodiment. The conceptual diagram for demonstrating the analysis target data generation processing of this embodiment. The flowchart which shows the graph generation process of this embodiment. The figure which shows the box size data of this embodiment. The conceptual diagram for demonstrating the graph generation process of this embodiment. The figure which shows the log-log graph generated by the graph generation part of this embodiment. The flowchart which shows the scaling index calculation process of this embodiment. The flowchart which shows the health state information generation processing of this embodiment. The figure which shows the health state information of this embodiment. The conceptual diagram for demonstrating the implementation mode of the biological signal analysis system of this invention.

Hereinafter, the present invention will be described with reference to the embodiments shown in the drawings, but the present invention is not limited to the embodiments shown in the drawings. In each of the figures referred to below, the same reference numerals are used for common elements, and the description thereof will be omitted as appropriate.

FIG. 1 shows an outline configuration of the biological signal analysis system 100 according to the embodiment of the present invention. As shown in FIG. 1, the biological signal analysis system 100 of the present embodiment includes a terminal device 10 and a computer 20.

The terminal device 10 is a device for collecting a biological signal of a subject, and is configured as a compact and lightweight device from the viewpoint of portability of the subject. At the time of use, the three electrodes 11a, 11b, 11c connected to the terminal device 10 are attached to appropriate positions on the chest of the subject, and the terminal device 10 creates a potential difference between the three electrodes 11a, 11b, 11c. Acquired as a biological signal (voltage signal). The terminal device 10 generates predetermined data regarding the time when the heartbeat of the subject occurs (hereinafter, referred to as the heartbeat generation time) based on the acquired biological signal, and transmits it to the computer 20. Note that FIG. 1 shows an image in which the terminal device 10 transfers data by short-range wireless communication such as Bluetooth (registered trademark) or Zigbee (registered trademark), but this is merely an example, and the data is shown. It goes without saying that the data may be transferred by another wireless communication method or by wired communication.

On the other hand, the computer 20 is an information processing device for generating health condition information based on the data transmitted from the terminal device 10, and FIG. 1 shows how the computer 20 displays the health condition information on the display 30. ing. In the present embodiment, the health condition information means information that can be referred to when evaluating the health condition of the heart, and more specifically, information that is useful for detecting signs of cardiac dysfunction. .. Although a notebook PC is exemplified as the computer 20 in FIG. 1, the computer 20 may be a tablet-type or desktop-type PC, or may be a workstation or server specialized for diagnostic purposes. It goes without saying that it may be present.

The outline configuration of the biological signal analysis system 100 of the present embodiment has been described above, but subsequently, the functional configurations of the terminal device 10 and the computer 20 will be described based on the functional block diagram shown in FIG.

First, the functional configuration of the terminal device 10 will be described. The terminal device 10 includes a heartbeat component extraction unit 12 and a heartbeat generation time acquisition unit 14.

The heartbeat component extraction unit 12 amplifies the weak biological signal (analog signal) of the subject detected by the electrodes 11a, 11b, 11c, and removes low-frequency noise from the amplified signal to perform heartbeat. Extract the ingredients. The heartbeat component extraction unit 12 can be realized by an amplifier circuit provided with a high-pass filter.

Here, in the JIS standard, the time constant τ of the high-pass filter of the electrocardiograph is defined as 3.2 seconds (low cutoff frequency 0.05 Hz or less), but in the present embodiment, the heartbeat component extraction unit. It is preferable to set the time constant τ of the high-pass filter of the amplifier circuit constituting 12 to about 0.1 second.

FIG. 3 shows a waveform of a voltage signal (hereinafter referred to as a heartbeat pulse signal) containing a heartbeat component extracted by a heartbeat component extraction unit 12 in which the time constant τ of the high-pass filter is set to 0.1 seconds. .. By setting the time constant τ of the high-pass filter to about 0.1 seconds (low frequency cutoff frequency is about 1.6 Hz or less), low-frequency noise caused by the body movement of the subject is almost completely removed, and the baseline. Will always be stable, eliminating the need to impose excessive restraints on the subject during the examination. If the time constant τ of the high-pass filter is made smaller than the JIS standard value, the waveforms of the P wave and T wave may be distorted, but in this system, the peak time of the R wave or S wave is generated as a heartbeat. This is not a problem as it is acquired as the time.

The heartbeat component extraction unit 12 outputs the heartbeat pulse signal extracted from the biological signal to the heartbeat generation time acquisition unit 14. In response to this, the heartbeat generation time acquisition unit 14 acquires the heartbeat generation time based on the heartbeat pulse signal (analog signal). The heartbeat generation time acquisition unit 14 can be realized by an MCU (microcontroller unit) having a built-in A / D converter.

Here, the process executed by the heartbeat generation time acquisition unit 14 will be described based on the flowchart shown in FIG. 4 and the waveform diagram of the heartbeat pulse signal shown in FIG.

In step 101, voltage monitoring of the heartbeat pulse signal input from the heartbeat component extraction unit 12 is started. The voltage monitoring is continued until the signal voltage of the heartbeat pulse signal exceeds the predetermined threshold voltage _Vth (S101, No), and when the signal voltage of the heartbeat pulse signal exceeds the threshold voltage _Vth (S101, Yes). , The process proceeds to step 102.

In step 102, the voltage value of the heartbeat pulse signal is sampled at a predetermined sampling rate over the sampling period T1, and the sampling is stopped when the sampling period T1 elapses (S103). For example, when the sampling rate = 1 kHz and the sampling period T1 = 100 ms, 100 voltage values are sampled in step 102.

In the following step 104, the time at which the maximum value V _max among the sampled voltage values is sampled is acquired, and this is recorded as the heartbeat generation time t.

Note that FIG. 5 shows an example in which the peak time of the waveform corresponding to the R wave of the heartbeat pulse signal is set to the beat generation time t, but depending on the electrode mounting position (guidance), the peak time is downward from the upward R wave. The S wave may appear larger. In that case, sampling is performed from the time when the signal voltage of the heartbeat pulse signal falls below a predetermined threshold voltage, and the sampling time (peak time of the waveform corresponding to the S wave) of the minimum value among the sampled voltage values is set as the heartbeat. The time of occurrence may be t. In short, in the present embodiment, the threshold voltage is determined according to the direction of the waveform of interest, sampling is performed from the time when the signal voltage of the heartbeat pulse signal reaches the threshold voltage, and the pole in the sampled voltage value is performed. The sampling time of the value may be set to the heartbeat generation time t.

In the following step 105, it is determined whether or not the number of acquisitions of the heartbeat occurrence time t has reached the target number A. Here, the target number A corresponds to the pulse rate, and in the present embodiment, it is preferable to set the target number A to a natural number in the range of 1500 to 2500. This value is based on the empirical knowledge of the present inventor that the subject can maintain a steady state for about 30 minutes at the most, and the range of 1500 to 2500 is the average for 30 minutes for adults. Corresponds to the typical pulse rate.

As a result of the determination in step 105, if the target number A has not been reached (S105, No), it is determined whether or not the waiting period T2 has elapsed after the sampling is stopped (S106). After that, this determination process is looped until the waiting period T2 elapses (S106, No).

When the waiting period T2 elapses (S106, Yes), the process returns to step 101 again to resume voltage monitoring of the heartbeat pulse signal. After that, in step 105, the above-mentioned series of processes (S101 to S106) are repeatedly executed until it is determined that the number of acquired heartbeat occurrence times t has reached the target number A.

That is, in the present embodiment, by interrupting the voltage monitoring of the heartbeat pulse signal during the standby period T2 continuous with the sampling period T1, it is possible to prevent unexpected noise generated during that period from being counted as the heartbeat.

In the present embodiment, it is preferable to set the waiting period T2 to be a time length corresponding to a difference between the time length of approximately half of the average heartbeat interval and the time length of the sampling period T1. This is based on the empirical fact that no more than one heartbeat occurs in a time length that is approximately half the normal heartbeat interval. For example, when the sampling period T1 is 100 ms for a subject having an average heart rate interval of 1000 ms, the waiting period T2 may be set to 400 ms (1000 / 2-100). As the average heartbeat interval time length, the average value of the heartbeat intervals of the subject obtained in advance can be used.

As a result of repeatedly executing the series of processes (S101 to S106) described above, when the number of acquired heartbeat occurrence times t reaches the target number A (S106, Yes), the heartbeat occurrence time acquisition unit 14 performs the heartbeat occurrence time acquisition process. finish. In response to this, the terminal device 10 transmits the A heartbeat generation times acquired by the heartbeat generation time acquisition unit 14 to the computer 20.

The functional configuration of the terminal device 10 has been described above, but subsequently, the functional configuration of the computer 20 will be described.

As shown in FIG. 2, the computer 20 uses the DFA analysis unit 22 for executing DFA (Detrended Fluctuation Analysis) based on the heartbeat generation time transmitted from the terminal device 10 and the result of the DFA to be in a healthy state. It is configured to include a health condition information generation unit 28 for generating information. The DFA method adopted by the DFA analysis unit 22 of the present embodiment is effective not only when the distribution function of the fluctuation of the signal to be analyzed is a Gaussian distribution but also when it is a Levy distribution. , It becomes possible to precisely detect changes in heartbeat fluctuations. The DFA analysis unit 22 is generated by an analysis target data generation unit 23 for generating time-series data to be DFA, and a graph generation unit 24 that generates a log-log graph based on the generated time-series data. It is configured to include a scaling index calculation unit 25 for deriving a scaling index based on a log-log graph. Hereinafter, the contents of the processing executed by each of the above-mentioned functional units will be described step by step.

First, the analysis target data generation process executed by the analysis target data generation unit 23 will be described based on the flowchart shown in FIG.

Upon receiving the A heartbeat generation times from the terminal device 10 by the computer 20, the analysis target data generation unit 23 starts the following processing.

In step 201, M (A-1) heartbeat intervals x are calculated from the A heartbeat occurrence times t received from the terminal device 10, and time-series data {with the calculated M heartbeat intervals x as elements. Generate x _i }. Specifically, the A heartbeat occurrence times t received from the terminal device 10 are arranged in a time series, and the time difference between the (i + 1) th time and the (i) th time is calculated as the heartbeat interval x. As a result, M (A-1) heartbeat intervals x are obtained, and {x _i } is generated as time series data composed of M elements x. Figure 7 (a) shows a graph of the time-series data {x _i} illustratively generated in step 201. In the graph shown in FIG. 7A, the horizontal axis is the time series order [i] of the data, and the vertical axis is the heartbeat interval [x].

In step 202, when after calculating the average value x _ave of the M heartbeat interval x constituting the series data {x _i}, subtracting the mean value x _ave from the elements constituting the series data {x _i} When By doing so, time series data {(x−x _ave ) _i } is generated. FIG. 7B shows a graph of the time series data {(xx _ave ) _i } generated in step 202.

In the following step 203, the time series data {(x−x _ave ) _i } is integrated to generate the time series data {y _i }. Specifically, time-series data {y _i } is generated by adding each element of time-series data {(xx- _ave ) _i } in chronological order. The following formula (1) shows the calculation formula of the time series data {y _i }.

FIG. 7C shows a graph of the time series data {y _i } generated in step 203. In the present embodiment, this time series data {y _i } is the data to be analyzed. Finally, the generated time series data {y _i } is stored in the storage unit 26 (step 204), and the process is terminated.

The process executed by the analysis target data generation unit 23 has been described above. Next, the graph generation process executed by the graph generation unit 24 will be described based on the flowchart shown in FIG.

In response to the analysis target data generation unit 23 generating the analysis target data (time series data {y _i }), the graph generation unit 24 starts the following processing.

In step 301, the time series data {y _i } saved by the analysis target data generation unit 23 is loaded from the storage unit 26.

In the following step 302, the box size data is loaded from the storage unit 26. Here, the box size data is a set of a plurality of box sizes (integers) used in DFA, and the box size means the number of elements of the data. FIG. 9 schematically shows box size data having 136 integers in the range of 10 to 1000 as box sizes. In the example shown in FIG. 9, the 136 integers contained in the box size data are incremented by 1 between 10 and 100, incremented by 10 between 100 and 500, and incremented by 100 between 500 and 1000. Incrementing.

In the following step 303, the first box size [N] is set from the loaded box size data. In the case of the box size data shown in FIG. 9, for example, the minimum box size [10] is set first.

In the following step 304, the time series data {y _i } loaded in step 301 is divided by the box size [N] set at that time. For example, when the box size set at that time is [10], the time series data {y _i } is divided into subsections (hereinafter, referred to as boxes) including 10 elements. That is, when the time series data {y _i } is composed of M elements, in step 304, the time series data {y _i } is divided into M / N boxes. FIG. 10A shows time series data {y _i } divided by the box size [10]. In this case, each box (BOX (1), BOX (2), BOX (3) ...) Contains 10 elements.

In the following step 305, an approximate curve is fitted to each of the N pieces of data existing in the box for each of the divided boxes (BOX (1), BOX (2), BOX (3) ...). The value on the approximate curve is determined as the local trend y _V of each box. Here, the fitting of the approximate curve can be performed by the minimum square method using the linear function to the quaternary function. The approximate curve referred to here is a concept including a straight line. FIG. 10 (b) shows approximate curves y _V (y _V (1), y _V (2), y _V (3) for each box (BOX (1), BOX (2), BOX (3) ...). ) ...) is shown in the fitted state. In FIG. 10B, for convenience of explanation, the approximate curve is shown by a linear function.

In step 306, the box (BOX (1), BOX ( 2), BOX (3) ...) for each of, by subtracting the local trend _{y V} from each of the elements constituting the box determined for the box, the time series It generates data _{{z i}.} Following formula (2), when The equation of series data {z _i}, FIG. 10 (c) shows a graph of the time-series data {z _i} generated in step 205.

In step 307, when box-series data _{{z i} (BOX (1} ), BOX (2), BOX (3) ...) for each of, shown enclosed by the first element (dotted circle constituting the box ) And the value of the last element (indicated by the dotted square).

Instead of the processes of steps 306 and 307 described above, the following method may be adopted to reduce the amount of calculation. That is, each box (BOX (1), BOX ( 2), BOX (3) ...) by subtracting the first element of the corresponding local trend _{y V} from the corresponding local trend _{y V} values and end of elements was subtracted from of This is a method for finding the difference between values.

In the following step 308, the root mean square [S] of the difference (difference between the beginning and the end) obtained for all the boxes (BOX (1), BOX (2), BOX (3) ...) is calculated.

In the following step 309, a set (N, S) of numerical values consisting of the root mean square [S] calculated in step 307 and the box size [N] set at that time is recorded.

In the following step 310, it is determined whether or not the set (N, S) is recorded for all the box sizes [N] included in the box size data loaded in step 302. As a result, if the recording of the set (N, S) is not completed for all the box sizes [N] (step 310, No), the process proceeds to step 311.

In step 311, the next box size [N] is newly set from the values included in the box size data. In the case of the box size data shown in FIG. 9, for example, the box size [11] is newly set. After that, the process returns to step 304 again.

After that, in step 310, the series of processes (S304 to S310) described above are repeatedly executed until it is determined that the set (N, S) has been recorded for all the box sizes [N].

That is, in the present embodiment, by executing the series of processes (S304 to S310) described above for all the box sizes included in the box size data loaded in step 302, a set of numerical values (as many as the number of box sizes) ( N, S) will be recorded, and in the case of the box size data shown in FIG. 9, 136 sets (N, S) will be recorded.

Here, the relationship between the box size [N] and the root mean square [S] is defined as the function S (N) shown in the following equation (3). In the following equation (3), "M" indicates the number of elements of the time series data {y _i }, "N" indicates the box size, and "z _{jN + N −} z _{jN + 1} " is the beginning of the jth box. The difference (displacement) between the element (z _{jN + 1} ) and the last element (z _{jN + N} ) is shown.

Returning to FIG. 8 again, the explanation will be continued. If it is determined in step 310 that the set (N, S) has been recorded for all the box sizes [N] (step 310, Yes), the process proceeds to step 312.

In step 312, the recorded pairs (N, S) are plotted on a log-log graph. FIG. 11 illustrates an exemplary log-log graph generated in step 312. In the log-log graph shown in FIG. 11, the vertical axis is log [S] and the horizontal axis is log [N], and the logarithmic scale is engraved. Finally, the log-log graph generated in step 312 is stored in the storage unit 26 (step 313), and the process ends.

The process executed by the graph generation unit 24 has been described above. Next, the scaling index calculation process executed by the scaling index calculation unit 25 will be described based on the flowchart shown in FIG.

The scaling index calculation unit 25 receives a request from the health state information generation unit 28 (described later), and starts the following processing.

In step 401, the log-log graph generated by the graph generation unit 24 is loaded from the storage unit 26.

In the following step 402, the linear function is fitted to the plot included in the box size range (described later) specified by the health condition information generation unit 28 among the loaded log-log graph plots, and the fitted linear function is fitted. Is calculated as the scaling index α. Finally, the log-log graph to which the linear function is fitted and the calculated scaling index α are stored in the storage unit 26 (step 403), and the process is terminated.

The process executed by the scaling index calculation unit 25 has been described above. Next, the health state information generation process executed by the health state information generation unit 28 will be described based on the flowchart shown in FIG.

The health state information generation unit 28 receives a processing request for which a range of the box size is specified from the user via a predetermined UI screen. In this embodiment, the designation of two or more box size ranges may be accepted at one time. The health state information generation unit 28 starts the following processing in response to the processing request from the user.

In step 501, a range of the box size specified by the user is specified, and the scaling index calculation unit 25 is requested to calculate the scaling index. In response to this, the scaling index calculation unit 25 executes the scaling index calculation process (see FIG. 12).

In the following step 502, the log-log graph used for calculating the scaling index and the calculated scaling index α are loaded from the storage unit 26.

In the following step 503, the loaded log-log graph and the scaling index α are used to generate health information.

FIG. 14 schematically shows health condition information generated as visualization data. In the example shown in FIG. 14A, the scaling index α is presented as the health index α on a log-log graph in which the linear function is fitted in the box size range [30 to 270]. On the other hand, in the example shown in FIG. 14 (b), the linear functions are fitted to the three box size ranges [30 to 60], [70 to 140], and [130 to 270], and the linear functions are fitted to each linear graph. The scaling index α corresponding to the function is presented as the health index α. The present invention does not limit the format of the health status information, and any format may be used as long as the scaling index α can be presented as an index representing the health status of the heart.

Finally, the health state information generation unit 28 saves the generated diagnosis support information health state information in the storage unit 26 (step 504), and ends the process.

In this embodiment, the default value of the box size range is set, and when the user does not explicitly specify the box size range, the scaling index α is set for the box size range indicated by the default value. May be calculated and the result presented as an indicator of heart health. In this case, the default value in the box size range is preferably 10 to 300, more preferably 30 to 270. This value is based on the empirical knowledge of the present inventor, and the present inventor calculated the scaling index for the range of the box size from the experience of observing the health condition of hundreds of subjects over time. They found that there was a close correlation between α and stress, which is a risk factor for acute myocardial infarction.

The biological signal analysis system of the present invention has been described above based on the embodiment, but it is said that the biological signal analysis system of the present invention can be implemented in various ways in addition to the embodiment shown in FIG. Not to mention. For example, the biological signal analysis system of the present invention can be implemented as a portable device as shown in FIG. 15A by integrating each of the functional means shown in FIG. 2 into a small housing. Further, the biological signal analysis system of the present invention can be implemented as a Web service by mounting the functional means provided in the computer 20 shown in FIG. 2 on the Web server 50 as a Web application. In this case, the dedicated application installed on the mobile terminal 40 (for example, a smartphone or tablet PC) transmits an HTTP request including the heartbeat occurrence time received from the terminal device 10 to the Web server 50, and the Web server 50 sends a healthy HTTP response. Receive status information. In short, each functional means constituting the biological signal analysis system of the present invention can be freely combined in any unit as long as health condition information can be presented to the user, and they are dispersed in any manner. Can be placed.

As described above, according to the present embodiment, it is possible to present the user with useful health condition information useful for detecting signs of cardiac dysfunction without forcing the subject to be excessively restrained. .. Users with potential heart risk can take measures such as reviewing their lifestyle based on the health information presented, so it does not matter.

Although the present invention has been described above with embodiments, the present invention is not limited to the above-described embodiments, and as long as the present invention exerts its actions and effects within the range of embodiments that can be inferred by those skilled in the art. , Is included in the scope of the present invention.

Further, each function of the terminal device 10 and the computer 20 described above can be realized by a device executable program described in C, C ++, C #, Java (registered trademark), etc., and the program of the present embodiment is , Hard disk devices, CD-ROMs, MOs, DVDs, flexible disks, EEPROMs, EPROMs, etc. Can be stored and distributed in readable recording media, and transmitted via a network in a format that other devices can. Can be done.

10 ... Terminal device, 11 ... Electrode, 12 ... Heartbeat component extraction unit, 14 ... Heartbeat generation time acquisition unit, 20 ... Computer, 22 ... DFA analysis unit, 23 ... Analysis target data generation unit, 24 ... Graph generation unit, 25 ... Scaling index calculation unit, 26 ... Storage unit, 28 ... Health status information generation unit, 30 ... Display, 40 ... Mobile terminal, 50 ... Web server, 100 ... Biological signal analysis system

Claims (10)

A heartbeat component extractor that extracts a voltage signal containing a heartbeat component as a main component from a biological signal,
A heartbeat generation time acquisition unit that acquires a heartbeat generation time based on the voltage signal,
A DFA analysis unit for executing DFA based on the time series data of the heartbeat interval calculated from the heartbeat occurrence time, and
A health condition information generation unit that generates health condition information that presents the scaling index calculated by the DFA analysis unit as an index representing the health condition of the heart,
Including
The heartbeat generation time acquisition unit
The voltage value of the voltage signal is sampled from the time when the voltage of the voltage signal reaches a predetermined threshold value over a sampling period, the time when the extreme value of the voltage is sampled is acquired as the heartbeat generation time, and the time is continuous with the sampling period. The monitoring of the voltage signal is interrupted during the standby period, and the standby period is set to the difference between the time length of half the average heartbeat interval and the time length of the sampling period.
Biological signal analysis system. The biological signal analysis system according to claim 1, wherein the heartbeat component extraction unit includes a high-pass filter, and the time constant of the high-pass filter is about 0.1. The DFA analysis unit
An analysis target data generation unit that generates analysis target data by integrating a value obtained by subtracting an average value from each element of the heartbeat interval time series data calculated from the heartbeat occurrence time.
The analysis target data is divided by the box size N (N is an integer of 1 or more. The same applies hereinafter), the trend for each box is determined, and the trend is calculated from the value obtained by subtracting the trend from the first element and the trend from the last element for each box. A series of processes of obtaining the difference between the subtracted values and recording the set (N, S) of the root mean square S of the difference and the box size N is executed for a plurality of different box sizes N, and the recorded plurality of said A graph generator that plots pairs (N, S) on a log-log graph,
A scaling index calculation unit that fits a linear function to a part or all of the plot in the log-log graph and calculates the slope of the linear function as a scaling index.
The biological signal analysis system according to claim 1 or 2. The graph generator
The biological signal analysis system according to claim 3, wherein an approximate curve is fitted to N elements in each of the boxes to determine the trend. The scaling index calculation unit
The biological signal analysis system according to claim 3 or 4, wherein a linear function is fitted to the plot in the specified box size N range. The scaling index calculation unit
The biological signal analysis system according to any one of claims 3 to 5, wherein a linear function is fitted to the plot having a box size N in the range of 10 to 300. The analysis target data generation unit is
The biological signal analysis system according to any one of claims 3 to 5, which generates the analysis target data based on a predetermined number of heartbeat occurrence times. The biological signal analysis system according to claim 7, wherein the predetermined number is a natural number in the range of 1500 to 2500. A method performed by a computer to generate health information, including indicators of heart health .
The step of extracting a voltage signal containing a heartbeat component as a main component from a biological signal,
The step of acquiring the heartbeat generation time based on the voltage signal and
A step of executing DFA based on the time series data of the heartbeat interval calculated from the heartbeat occurrence time, and
A step to generate health condition information that presents the scaling index calculated by the DFA analysis unit as an index showing the health condition of the heart, and
And run
The step of acquiring the heartbeat occurrence time is
The voltage value of the voltage signal is sampled from the time when the voltage of the voltage signal reaches a predetermined threshold value over a sampling period, the time when the extreme value of the voltage is sampled is acquired as the heartbeat generation time, and the time is continuous with the sampling period. A step of interrupting the monitoring of the voltage signal during the standby period.
The waiting period is set to the difference between half the time length of the average heartbeat interval and the time length of the sampling period.
A computer-executable program. The step of executing the DFA is
A step of integrating the values obtained by subtracting the average value from each element of the time series data of the heartbeat interval calculated from the heartbeat occurrence time to generate the analysis target data, and
The analysis target data is divided by the box size N (N is an integer of 1 or more. The same applies hereinafter), the trend for each box is determined, and the trend is calculated from the value obtained by subtracting the trend from the first element and the trend from the last element for each box. A series of processes of obtaining the difference between the subtracted values and recording the set (N, S) of the root mean square S of the difference and the box size N is executed for a plurality of different box sizes N, and the recorded plurality of said Steps to plot pairs (N, S) on a log-log graph,
A step of fitting a linear function to a part or all of the plot in the log-log graph and calculating the slope of the linear function as a scaling index.
9. The computer-executable program of claim 9 .