JP7307475B2 - Autonomic nerve function evaluation device and autonomic nerve function evaluation program - Google Patents

Description

The present invention relates to an autonomic nerve function evaluation device and an autonomic nerve function evaluation program.

Cardiac function is antagonistically controlled by sympathetic nerves and parasympathetic nerves, and the heartbeat interval (RRI, RR Interval) constantly fluctuates. When frequency analysis is performed on the heartbeat interval variation information composed of time-series heartbeat interval data, two main frequency components can be confirmed with a boundary of 0.15 Hz. One is a frequency component of approximately 0.05 Hz or more and 0.15 Hz or less called a low frequency (LF) component, which mainly reflects the activity state of the sympathetic and parasympathetic nerves. The other is a frequency component of approximately 0.15 Hz or more and 0.45 Hz or less called a high frequency (HF) component, which mainly reflects the activity state of the parasympathetic nerves. Since each of these frequency components reflects the state of the autonomic nerves, the power of the spectrum is used as an index indicating the functional status of the autonomic nerves for evaluation of autonomic nerve function.

The autonomic nerve function evaluation is performed by roughly classifying the balance between the states of the sympathetic nerves and the parasympathetic nerves into, for example, four categories. In other words, evaluation is made under various conditions of DC components, such as the level of power of the sympathetic nerve and the parasympathetic nerve, and the four classifications are (1) to (4) below.
(1) Both the power of the sympathetic nerve and the parasympathetic nerve are high.
(2) The power of the sympathetic nerve is high and the power of the parasympathetic nerve is extremely low.
(3) The power of the sympathetic nerve is low and the power of the parasympathetic nerve is extremely high.
(4) Both the power of the sympathetic and parasympathetic nerves are low

There is a technique using a fast Fourier transform (FFT) for frequency analysis of heartbeat interval variation information. However, the Fourier transform, as is known, suffers from the drawback of not being able to increase the accuracy of both time and frequency factors. As for frequency analysis, the measurement time must be shortened in order to obtain the energy of high frequency components. Conversely, in order to obtain the energy of the low-frequency component, there is a problem that one cycle cannot be obtained unless the measurement time is lengthened. In this way, the Fourier transform can be said to be a method that sacrifices the accuracy related to the time factor, that is, the time domain. In addition, when a pulse skip occurs, the data may be irregular, so that the frequency conversion cannot be performed correctly, and a calculation error occurs about 20 seconds before and after the pulse skip occurs. Furthermore, since the Fourier transform cannot obtain an autonomic nerve index for each measurement time, it is necessary to measure for about 30 seconds or longer. In the Fourier transform, it is not possible to analyze the instantaneous frequency of the measurement after the measurement for a certain period of time is completed. Can not. In the case of the method using the Fourier transform, the individual's autonomic nerves are evaluated based on the number of changes in the autonomic nerves over time. Therefore, it is not possible to evaluate autonomic nerves according to individual characteristics such as occupation, lifestyle, and environment.

Therefore, for example, Patent Document 1 proposes a method of using wavelet transform for frequency analysis of heartbeat interval fluctuation information. This is because the wavelet transform is suitable for analysis when target time-series data is in a non-stationary state. In the technique of Patent Literature 1, biosignal time-series data is divided into complex wavelet coefficients of one or more frequency bands by complex wavelet transform. Then, the power is calculated by squaring the absolute value of the complex number wavelet coefficient obtained by the division, and the temporal fluctuation of the power of the frequency component contained in the biological signal is obtained.

Further, Patent Literature 2 describes a method of obtaining the aforementioned LF component and HF component by performing wavelet transform processing on heartbeat interval variation information, and calculating a degree of relaxation based thereon. In the method of Patent Document 2, wavelet-transformed pulse wave analysis data is subjected to frequency correction to obtain pulse wave corrected data, which is compared in each frequency-time domain to obtain pulse wave data indicating a pulse wave.

Further, Patent Document 3 proposes a method of obtaining the power of the frequency component included in the heartbeat interval variation information with high time resolution. In the technique of Patent Literature 3, data of evenly spaced intervals are generated by interpolating intervals between heartbeats, which are unevenly spaced intervals, and wavelet transform processing is applied to the equally spaced data to divide it into wavelet bands of a plurality of frequency bands. ing.

JP-A-2000-296118 JP-A-10-248819 JP-A-11-128185

However, in the methods described in Patent Documents 1 to 3, in order to evaluate autonomic nerve function from data frequency-analyzed by wavelet transform processing, specialized knowledge for analyzing analysis data is required, and autonomic nerve function status cannot be easily grasped.

Therefore, an object of the present invention is to provide an autonomic-nerve function evaluation device and an autonomic-nerve function evaluation program for easily grasping the state of the autonomic-nerve function from data frequency-analyzed by wavelet transform processing.

An autonomic nerve function evaluation device of the present invention includes a basic value storage unit, a specification unit, and an evaluation unit. The basic value storage unit stores a first basic value of the cycle of the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data. The specifying unit specifies, as the central value of the cycle, the cycle with the highest energy among the energies of each cycle obtained by performing the second wavelet transform processing on the obtained LF/HF time-series waveform data. do. The evaluation unit evaluates the autonomic nerve function based on the degree of difference between the first basic value and the central value of the cycle.

The basic value storage unit stores, as a second basic value, the center value of the amplitude of the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data, and the specifying unit stores the obtained It is preferable that an average amplitude value of the LF/HF time-series waveform data is specified, and the evaluation unit evaluates the autonomic nerve function based on the degree of difference between the second basic value and the average amplitude value.

The basic value storage unit stores the amplitude as a third basic value for the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data, and the specifying unit stores the obtained LF/HF and the evaluation unit evaluates the autonomic nerve function based on the degree of difference between the third basic value and the amplitude.

It is preferable to further include an image generation section that generates an image of the evaluation result obtained by the evaluation section.

An autonomic nerve function evaluation program of the present invention causes a computer to execute a basic value storage step, a specification step, and an evaluation step. The basic value storing step stores a first basic value of the cycle for the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data. The specifying step specifies, as the central value of the cycle, the cycle with the highest energy among the energies of each cycle obtained by performing the second wavelet transform processing on the obtained LF/HF time-series waveform data. do. The evaluation step evaluates the autonomic nerve function based on the degree of difference between the first basic value and the central value of the cycle.

ADVANTAGE OF THE INVENTION According to this invention, the state of an autonomic-nerves function can be easily grasped from the data frequency-analyzed by the wavelet transform process.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram which shows the structure of the autonomic-nerve function-evaluation apparatus which is embodiment. It is a graph which shows an example of the time-series data of LF/HF. FIG. 4 is an explanatory diagram of heartbeats and heartbeat intervals; FIG. 4 is an explanatory diagram of heartbeat intervals; It is each power of LF and HF, and a graph of time-series data of LF/HF. FIG. 4 is an explanatory diagram of ideal waveform data; FIG. 4 is an explanatory diagram of a method of identifying a central value of a period; It is explanatory drawing of the evaluation method of an autonomic-nerves function using a 1st reference value. It is explanatory drawing of the evaluation method of an autonomic nerve function using a 2nd reference value. It is explanatory drawing of the evaluation method of an autonomic-nerves function using a 3rd reference value. FIG. 10 is an explanatory diagram of an image of an evaluation result; FIG. 10 is an explanatory diagram of an image of an analysis result; FIG. 4 is an explanatory diagram of an image of frequency analysis; It is a figure which shows the electrical structure of an autonomic-nerve function-evaluation apparatus.

An autonomic nerve function evaluation device (hereinafter simply referred to as "evaluation device") 10 shown in FIG. 1 is an example of an embodiment of the present invention. The evaluation device 10 is for evaluating autonomic nerve function using LF/HF time-series waveform data (see FIG. 2). LF/HF time-series waveform data is obtained by first wavelet (hereinafter referred to as WL) transform processing based on heartbeat interval data. In FIG. 1, reference numeral 13 denotes the first WL conversion processing unit that performs the first WL conversion processing.

LF/HF time-series waveform data can be obtained by a known method using wavelet transform processing, for example, by the following method. First, heartbeats are detected using a heartbeat detection sensor that is attached to an earlobe, fingertip, or the like to detect heartbeats (pulse waves), and the heartbeat interval (hereinafter referred to as RRI) is obtained based on the heartbeat data (see FIG. 3). RRI data (see FIG. 4) is thus obtained. The heartbeat data includes time information by time stamps, so that the RRI values (RRI1, RRI2, RRI3, . . . ) forming the RRI data are obtained one after another. The heartbeat detection sensor and the device that generates the RRI data may be known or commercially available. The first WL conversion process performs frequency analysis on the obtained RRI data. As a result, the respective powers of the component (LF component) in the low frequency (hereinafter referred to as LF) region and the component (HF component) in the high frequency (hereinafter referred to as HF) region are obtained (see FIG. 5). The dashed line in FIG. 5 is the power graph of the LF component, and the solid line is the power graph of the HF component. The first WL conversion processing unit 13 of this example is configured to obtain a power ratio between the LF component and the HF component (calculated by LF/HF, hereinafter referred to as LF/HF), and the first WL conversion processing LF/HF is obtained by the section 13 . A two-dot chain line in FIG. 5 indicates LF/HF.

Each power of the LF component and the HF component is obtained as time-series data, and when graphed, it becomes time-series waveform data as shown in FIG. Therefore, LF/HF is also obtained as time-series data, and when graphed, it becomes time-series waveform data as shown in FIG. The LF region is a frequency region of 0.04 Hz or more and 0.15 Hz or less, and the HF region is a frequency region of 0.15 Hz or more and 0.4 Hz or less. are not limited to these. For example, in this example, the input unit 17, which will be described later, can reset the lower and upper limits of the LF region and the HF region. analysis and evaluation are possible.

The evaluation device 10 comprises an evaluation unit 16 , an input 17 and an output 18 . The evaluation unit 16 is an evaluation device body that evaluates the autonomic nerve function based on the LF/HF time-series waveform data. The input unit 17 inputs various settings and processing execution instructions to the evaluation unit 16, and the output unit 18 outputs evaluation results obtained by the evaluation unit 16 and guidance on input operations performed by the input unit 17. Output.

The evaluation unit 16 includes a control section 20 , a basic value storage section 21 , an identification section 22 and an evaluation section 23 . The evaluation unit 16 preferably includes an LF/HF data storage section 26, a second WL conversion processing section 27, and an image generation section 28, which is also the case in this example.

The evaluation device 10 is composed of a computer, and by executing a program (autonomic nerve function evaluation program) stored in a storage unit (not shown) such as a hard disk, the control unit 20 of the evaluation unit 16, basic value storage It functions as a unit 21, a specification unit 22, an evaluation unit 23, an LF/HF data storage unit 26, a second WL conversion processing unit 27, an image generation unit 28, an input unit 17, an output unit 18, and the like. The electrical configuration of the evaluation device 10 will be described later using another drawing.

The control section 20 controls each section of the evaluation unit 16 in an integrated manner.

The basic value storage unit 21 stores a basic value (first basic value) of the cycle for the LF/HF time-series waveform data. The first basic value and the second and third basic values described later are hypothetical values assuming that a person who is physically and mentally healthy is the subject to be evaluated. It is a virtual value in series waveform data (hereinafter referred to as ideal waveform data). The ideal waveform data, as shown in FIG. 6, is assumed to be a waveform in which the period T, the center value Ac of the amplitude, and the amplitude A are approximately constant. The first basic value is the period T of the ideal waveform data. Since the frequency is the reciprocal of the period, setting the frequency may be regarded as setting the period T.

The first basic value may be set in advance in the autonomic nerve function evaluation program, or may be set by input through the input unit 17, for example. A default (initial value) may be set in the autonomic nerve function evaluation program, and the setting may be reset by an input through the input unit 17 . This example will be described assuming that the first reference value is set to 100 seconds.

The specifying unit 22 specifies, as the central value of the cycle, the cycle with the highest energy among the energies of each cycle obtained by performing the second WL conversion processing on the obtained LF/HF time-series waveform data. do. Specifically, when LF/HF time-series waveform data (hereinafter referred to as measured waveform data) as shown in FIG. By performing the second WL conversion process on the waveform data, the energy for each period is obtained. When the energy for each period is graphed with the horizontal axis representing the period and the vertical axis representing the energy, for example, a mountain-shaped graph as shown in FIG. 7 is obtained. The specifying unit 22 specifies the cycle showing the largest energy among the energies for each cycle as the central value of the cycle. In the example shown in FIG. 7, the cycle is 110 seconds and the energy is maximized, and this "110 seconds" is the central value of the cycle. Note that a frequency, which is the reciprocal of the period, may be used instead of the period. That is, the energy for each frequency may be obtained, the frequency with the largest energy may be specified as the center value of the frequency, and this specification may be regarded as the specification of the center value of the cycle.

In this example, the measured waveform data supplied to the second WL conversion processing section 27 is stored in the LF/HF data storage section 26 . The LF/HF data storage unit 26 is connected (electrically connected) to the first WL conversion processing unit 13 and stores LF/HF time-series data obtained by the first WL conversion processing unit 13 . That is, based on the RRI data obtained from the heartbeat data, which is actually measured data of the person being evaluated, the first WL conversion processing unit 13 obtains time-series data of the LF component, the HF component, and the LF/HF. is stored in the LF/HF data storage unit 26. Since the LF/HF time-series data becomes waveform data when graphed, if the LF/HF time-series data is stored, the graph is actually generated and the generated graph is stored. Even if it is not stored, it can be regarded as storing the actually measured waveform data.

The LF/HF data storage unit 26 stores time-series data of each power of the LF component and the HF component obtained by the first WL conversion processing unit 13, in addition to the LF/HF time-series data as actually measured waveform data. You may have Note that the first WL conversion processing unit 13 does not have to be connected to the LF/HF data storage unit 26 . In that case, for example, the LF/HF time-series data as the actually measured waveform data obtained by the first WL conversion processing unit 13 is input by the input unit 17, and the LF/HF data storage unit is input via the control unit 20. 26.

The evaluation unit 23 evaluates the autonomic nerve function based on the degree of difference between the first basic value and the central value of the cycle identified by the identification unit 22 . Specifically, the first reference data BD1 (see FIG. 8) for evaluation in which the degree of difference between the first basic value and the central value of the cycle is associated with the evaluation score of the autonomic nerve function is generated and stored. Keep In this example, the second WL conversion processing unit 27 performs the second WL conversion processing in response to the input of the autonomic nerve function evaluation instruction from the input unit 17, the identification processing by the identification unit 22, and the evaluation processing by the evaluation unit 23. It is assumed that However, the present invention is not limited to this example. For example, the evaluation unit 23 responds to an input of an autonomic nerve function evaluation instruction from the input unit 17, and the evaluation unit 23 issues a second WL conversion processing execution request via the control unit 20. It may be sent to the 2WL conversion processing unit 27, the second WL conversion processing unit 27 may perform the second WL conversion processing in response to the execution request, the identification processing by the identification unit 22, the evaluation processing by the evaluation unit 23, and so on.

The first reference data BD1 may be set in advance as a default in the autonomic nerve function evaluation program, or may be reset by the evaluation unit 23 under the control of the control unit 20 by input of the input unit 17, for example. . The first reference data BD1 may be stored in the basic value storage unit 21, or may be stored in a storage unit (not shown) different from the basic value storage unit 21, which is further provided. Here, it is assumed that the basic value storage unit 21 stores the first reference data BD1.

The lowest score (worst evaluation score) and highest score (best evaluation score) in the first reference data BD1 may be set as appropriate. ), and the maximum score is 100 points. The first reference data BD1 associates the highest score with the first basic value, and associates the lower score with the longer period on the side smaller than the first basic value and on the side larger than the first basic value. In the case of this example in which the first reference value is 100 seconds, 100 points are associated with the period T of 100 seconds, 0 points are assigned to the periods of 25 seconds and 400 seconds, and 0 points are assigned to the periods of 25 seconds and 400 seconds from 100 seconds. The score is gradually reduced continuously toward each of the points.

The degree of difference between the first basic value and the central value of the period identified by the identifying unit 22 (hereinafter referred to as the first dissimilarity) DD1 is the absolute value of the difference between the central value of the period and the first basic value, Based on the first reference data BD1, the score of the central value of the period specified by the specifying unit 22 can be obtained. For example, in this example in which the central value of the cycle (marked with symbol MV1 in FIG. 8) is specified as 110 seconds, the autonomic nerve function score is approximately 93 as shown in FIG. In addition, in FIG. 8, the score obtained as the evaluation is given the symbol EV1.

Thus, according to the configuration of this example, the autonomic nerve function is evaluated based on the first dissimilarity DD1. Therefore, it is possible to easily grasp the state of the autonomic nerve function of the person to be evaluated. The autonomic nerve function evaluated based on the first dissimilarity DD1 is hereinafter referred to as the first autonomic nerve function.

For the first reference data BD1, the range R1 from the first basic value to the cycle that associates the lowest point on the side smaller than the first basic value and the lowest point on the side larger than the first basic value are associated. The range R2 up to the period may be different from each other. For example, in this example, the range R2 is specifically set larger than the range ^R1 . 300 seconds up to (=100×2 ² ) seconds. As a result, the evaluation results on the side smaller than the first basic value and on the side larger than the first basic value are obtained as well-balanced results.

The basic value storage unit 21 shown in FIG. 1 preferably stores the center value of the amplitude of the ideal waveform data as the second basic value, and this example also does so. The second basic value is the central value Ac (see FIG. 6) of the amplitude A of the ideal waveform data.

The second basic value may be set in advance in the autonomic nerve function evaluation program, or may be set by input through the input unit 17, for example. A default (initial value) may be set in the autonomic nerve function evaluation program, and the setting may be reset by an input through the input unit 17 . This example will be described assuming that the second base value is set to 1 (that is, LF/HF=1).

The identifying unit 22 identifies the average value of the amplitudes of the actually measured waveform data. For example, in this example, the specifying unit 22 reads the measured waveform data from the LF/HF data storage unit 26 under the control of the control unit 20 in response to the input from the input unit 17, and specifies the average amplitude value. . The average value of amplitude is, for example, the average value of all LF/HF values forming the measured waveform data. Here, the average value of all LF/HF values is determined as the average amplitude value, and is specified as 0.7.

The evaluation unit 23 preferably evaluates the autonomic nerve function based on the degree of difference between the second basic value and the average amplitude value specified by the specification unit 22, and this example also does so. Specifically, the second reference data BD2 (see FIG. 9) for evaluation in which the degree of difference between the second basic value and the average value of the amplitude is associated with the evaluation score of the autonomic nerve function is generated and stored. Keep The second reference data BD2 may be set in advance as a default in the autonomic nerve function evaluation program, or may be reset by the evaluation unit 23 under the control of the control unit 20 by input of the input unit 17, for example. . The second reference data BD2 may be stored in the basic value storage unit 21, or may be stored in a storage unit (not shown) different from the basic value storage unit 21 provided. Here, it is assumed that the basic value storage unit 21 stores the second reference data BD2.

The lowest score (worst evaluation score) and highest score (best evaluation score) in the second reference data BD2 may be set as appropriate. ), and the maximum score is 100 points. The second reference data BD2 associates the highest score with the second basic value, and associates the lower score with the average value of the amplitude that is further away from the second basic value on the side smaller and larger than the second basic value. In the case of this example in which the second reference value is 1, 100 points are associated with this 1, 0 and 2 are respectively 0 points, and the points are consecutively assigned from 1 to 0 and 2 respectively. is gradually reduced to

The second difference DD2 between the second basic value and the average amplitude specified by the specifying unit 22 is the absolute value of the difference between the average amplitude and the second basic value, and is based on the second reference data BD2. , the score of the average value of the amplitude specified by the specifying unit 22 can be obtained. For example, in this example in which the average value of amplitude (denoted by symbol MV2 in FIG. 9) is specified as 0.7, as shown in FIG. In addition, in FIG. 9, the score obtained as the evaluation is given the symbol EV2.

Thus, according to the configuration of this example, the autonomic nerve function is evaluated based on the second dissimilarity DD2 in addition to the first dissimilarity DD1. Therefore, the state of the autonomic nerve function of the person to be evaluated can be known simply and in more detail. The autonomic nerve function evaluated based on the second dissimilarity DD2 is hereinafter referred to as the second autonomic nerve function.

The basic value storage unit 21 shown in FIG. 1 preferably stores the amplitude A (see FIG. 6) as the third basic value for the ideal waveform data, and this example also does so. The third basic value is the amplitude A of the ideal waveform data.

The third basic value may be set in advance in the autonomic nerve function evaluation program, or may be set by input through the input unit 17, for example. A default (initial value) may be set in the autonomic nerve function evaluation program, and the setting may be reset by an input through the input unit 17 . This example will be described assuming that the third basic value is set to 1.2.

The identifying unit 22 identifies the amplitude of the measured waveform data. For example, in this example, the specifying unit 22 reads measured waveform data from the LF/HF data storage unit 26 under the control of the control unit 20 in response to an input from the input unit 17 and specifies the amplitude. The amplitude may be a value obtained by specifying the maximum and minimum values among all the LF/HF values that constitute the measured waveform data and subtracting the minimum value from the maximum value. It may be obtained using a threshold such as In this example, it is basically obtained by (maximum value)-(minimum value). However, in order to eliminate obvious measurement errors, the standard deviation of the entire data (may be the average value, etc.) is calculated in advance, and if there is a value larger or smaller than the standard deviation, that value or that value Neighboring values may not be selected. By the way, (maximum value)-(minimum value) is obtained at the point where the obtained data is differentiated and the slope becomes zero.

The evaluation unit 23 preferably evaluates the autonomic nerve function based on the degree of difference between the third basic value and the amplitude specified by the specification unit 22, and this example also does so. Specifically, the third reference data BD3 (see FIG. 10) for evaluation in which the degree of difference between the third basic value and the amplitude is associated with the evaluation score of the autonomic nerve function is generated and stored. . The third reference data BD3 may be set in advance as a default in the autonomic nerve function evaluation program, or may be reset by the evaluation unit 23 under the control of the control unit 20, for example, by inputting the input unit 17. . The third reference data BD3 may be stored in the basic value storage unit 21, or may be stored in a storage unit (not shown) different from the basic value storage unit 21, which is further provided. Here, it is assumed that the basic value storage unit 21 stores the third reference data BD3.

The lowest score (worst evaluation score) and highest score (best evaluation score) in the third reference data BD3 may be set as appropriate. ), and the maximum score is 100 points. The third reference data BD3 associates the highest score with the third basic value, and associates lower scores with amplitudes that are further away from the third basic value, respectively. In the case of this example in which the third reference value is 1.2, 100 points are associated with this 1.2, 0 and 2 are respectively 0 points, and 1.2 to 0 and 2 are respectively The score is gradually decreased as it goes.

The third degree of difference DD3 between the third basic value and the amplitude specified by the specifying unit 22 is the absolute value of the difference between the amplitude and the third basic value, and is specified by the specifying unit 22 based on the third reference data BD3. It is possible to obtain a score for the amplitude obtained by The amplitude is denoted by MV3 in FIG. 10, and the autonomic nerve function scores obtained in this example are denoted by EV3.

Thus, according to the configuration of this example, the autonomic nerve function is evaluated based on the third dissimilarity DD3 in addition to the first dissimilarity DD1. Therefore, the state of the autonomic nerve function of the person to be evaluated can be known in a simple and detailed manner. The autonomic nerve function evaluated based on the third dissimilarity DD3 is hereinafter referred to as the third autonomic nerve function.

The image generation unit 28 shown in FIG. 1 generates an image of the evaluation result obtained by the evaluation unit 23 and outputs the generated image to the output unit 18, that is, displays it on a display serving as the output unit 18. FIG. The image to be generated includes the evaluation result obtained based on the first dissimilarity DD1 (FIG. 8), the evaluation result obtained based on the second dissimilarity DD2 (see FIG. 9), and the third dissimilarity DD3 ( 10), or an image showing one evaluation result based on these evaluation results. In this example, the latter of these is used, and a circular graph is generated as an image G11, as shown in FIG. 11, for example. In the graph of image G11, the horizontal axis represents the switching cycle of the sympathetic nerves, and the vertical axis represents the strength of the sympathetic nerves. The horizontal axis indicates a shorter switching cycle (switching is faster) toward the right side of the drawing, and a longer switching cycle (switching is slower) toward the left side. Also, the vertical axis means that the sympathetic nerve function is stronger toward the upper side of the paper surface, and that the sympathetic nerve function is weaker toward the lower side.

In this graph, the point P as the evaluation result is displayed at a position closer to the origin 0 (zero) as the autonomic nerve function is closer to the ideal. When the same measured waveform data as the ideal waveform data is obtained, that is, when the evaluation result of the first and second autonomic nerve functions is a full score (100 points in this example), the evaluation is made at the origin 0 in this graph. The resulting point P is displayed. On the other hand, if either one of the first and second autonomic nerve function evaluations is 0, a point P is displayed on the outermost circle as shown in FIG. Of the first and second autonomic nerve function evaluations, if only the evaluation result of the first autonomic nerve function is less than the full score (100), the score of the first rhythm function is low, and from the set reference value The point P is displayed to the left if it is lower than the set reference value, and to the right if it is higher than the set reference value, away from the origin. Further, among the first and second autonomic nerve function evaluation, if only the evaluation result of the second autonomic nerve function is less than the full score (100), the score of the second autonomic nerve function is low, and the set criteria If the value is lower than the value, the point P is displayed downward, and if it is higher than the set reference value, the point P is displayed upward away from the origin. In addition, the evaluation result of the third autonomic nerve function can be shown, for example, by making the circular graph shown in FIG. 11 three-dimensional. For example, an axis (not shown) passing through the origin 0 of the graph shown in FIG. 11 and extending toward the front side of the paper is generated as an axis indicating the evaluation result of the third autonomic nerve function. When the evaluation result of the third autonomic nerve function is a full score (100), the point P is displayed on the paper surface of FIG. 11 set by the vertical axis and the horizontal axis. , a point P is displayed away from the plane of the paper toward the viewer.

In addition to the image G11, the image generation unit 28 in this example generates actually measured waveform data (LF/HF time-series waveform data) and time-series waveform data of each power of the LF and HF components based on the actually measured waveform data. 5) is generated as an image G12 (see FIG. 12) and displayed on the display serving as the output unit 18. FIG. The image G12 may be displayed on the same screen as the image G11, or may be displayed by switching with the image G11. In this example, the image G12 is displayed on the same screen as the image G11.

In addition to the image G11, the image generation unit 28 in this example generates an image G13 (see FIG. 13), which is a graph of the frequency analysis result at a specific time, and displays it on the display serving as the output unit 18. FIG. “Specific time” can be specified by input through the input unit 17 . In this graph, the vertical axis represents power and the horizontal axis represents frequency (unit: Hz), showing integrated values of LF components and integrated values of HF components. In FIG. 13, the diagonally hatched portion is the integrated value of the LF component, and the dotted portion is the integrated value of the HF component. The image G13 may be displayed on the same screen as the image G11, or may be displayed by switching with the image G11. In this example, the image G13 is displayed on the same screen as the images G11 and G12.

The mother wavelet used for the first WL transform process and the second WL transform process is not particularly limited. For example, when a Gaussian function (normal distribution) is used as the mother wavelet, the value approaches 0 as the distance from the peak value increases. preferable. For example, the calculation target range is until the minimum value of the normal distribution becomes 0.01. Here, the values of the Gaussian function are stored in an array, and the range up to the minimum value is calculated. The WL transform expands or contracts the scale of the data to be measured and the mother wavelet, and performs convolution calculations with the translated data. is significantly reduced.

The calculations of the first WL transform process and the second WL transform process can be performed using a known WL transform formula, and in this example, gabor is implemented as a mother wavelet (Ψ) (see Equation 1 below). Although the results of frequency analysis are different for other mother wavelets, similar effects can be obtained.

In the first WL conversion process and the second WL conversion process, each symbol in Expression 1 has the following meaning.
a (unit; second): reciprocal of frequency b (unit; second): shift t (unit; second): time σ (unit; -): damping coefficient

The first WL transform process and the second WL transform process require different frequency components, and the above parameters such as a and t are set according to the required frequency components. For example, in the first WL conversion process, a region from 0.04 Hz to 0.15 Hz is obtained as the LF component. From 0.04 Hz to 0.15 Hz, the value acquisition interval is arbitrary, for example, it may be acquired every 0.01 Hz or every 0.02 Hz. a is set. For example, when a value is acquired every 0.04 Hz, it is obtained as a=1/0.04. On the other hand, in the case of the second WL conversion process, if the period T, which is the first basic value in FIG. 8, is 100 seconds, then a=100. t is the time desired to be acquired as a frequency component in the time-series RRI data. Therefore, when it is desired to analyze the frequency characteristics of data two seconds after the start of measurement, t=2 seconds. σ can be set to an arbitrary value, and the larger the value, the more accurate the frequency characteristics, and the smaller the value, the more accurate the temporal change. For example, σ=1.

An example of the electrical configuration of the evaluation device 10 is, as shown in FIG. 52, an auxiliary storage device 53 including a HDD (Hard Disc Drive), an input device 54 including a mouse and keyboard, an output device 55 including a display and speakers, and a communication device 56 including a network interface card. and have A device such as an MPU (Micro-Processing Unit) or a DSP (Digital Signal Processor) may be used instead of the CPU. The communication device 56 may be a wired connection device using USB (Universal Serial Bus).

The main storage device 52, the auxiliary storage device 53, the input device 54, the output device 55, and the communication device 56 are each connected to the control processing device 51 via bus lines. The control processing device 51 (1) reads the autonomic nerve function evaluation program stored in the auxiliary storage device 53 onto the main storage device 52, and (2) follows the instructions of the program, the input device 54, the auxiliary storage device 53, and the communication device. 56, (3) computing and processing the acquired data according to the procedure specified in the above program, and (4) storing the computed and processed data in the auxiliary storage device 53. It is provided to at least one of the output device 55 and the communication device 56 .

The above-described second WL conversion processing unit 27 , identification unit 22 and evaluation unit 23 are configured including a control processing device 51 . The control unit 20 and the image generation unit 28 are configured including a control processing device 51 and a communication device 56 . The LF/HF data storage section 26 and the basic value storage section 21 are configured including an auxiliary storage device 53 and a main storage device 52 . The input unit 17 includes an input device 54 , and the output unit 18 includes an output device 55 .

The autonomic nerve function evaluation program causes a computer to execute a basic value storage step, a specification step, and an evaluation step. The basic value storing step stores a first basic value of the cycle for the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data. The specifying step specifies, as the central value of the cycle, the cycle with the highest energy among the energies of each cycle obtained by performing the second wavelet transform processing on the obtained LF/HF time-series waveform data. do. The evaluation step evaluates the autonomic nerve function based on the degree of difference between the first basic value and the central value of the cycle.

The evaluation device 10 may be connected in combination with an analysis device (not shown) that obtains measured waveform data. The analysis device includes a first WL conversion processing unit 13 (see FIG. 1). Furthermore, the analysis device may be connected in combination with a detection unit (not shown) that detects heartbeats and obtains RRI. The detection unit has a sensor and a heart rate data processor. As the sensor, for example, a photoelectric conversion sensor that is formed in a clip shape, detects a heartbeat (pulse wave) while the earlobe is clamped, and sends heartbeat data (see FIG. 3) to a heartbeat data processing unit is used. be able to. As the heartbeat data processing unit, for example, one that obtains RRI based on heartbeat data from the sensor and sends RRI data RRI1, RRI2, RRI3, . . . shown in FIG.

When the analysis device is connected to the evaluation device 10 in combination, the input section 17 and the output section 18 (see FIG. 1) may be used together as the input section and the output section of the analysis device. That is, the input unit 17 and the output unit 18 may be components of the evaluation device 10 and components of the analysis device.

The analysis device adds a predetermined value as a bias value to the RRI from the detection unit, and obtains actually measured waveform data by the first WL conversion processing section 13 . Note that the RRI data composed of a plurality of RRIs to which the bias value is to be added is subjected to adjustment processing using a parameter set to a predetermined value. By combining and connecting such an analysis device with the evaluation device 10, in this example, the RRI (see FIG. 4) acquired one after another is displayed as an image (not shown), and the image G12 shown in FIG. 12 is displayed. , can be generated almost synchronously and output side by side on the same screen.

10 autonomic nerve function evaluation device 13 first WL conversion processing unit 16 evaluation unit 17 input unit 18 output unit 20 control unit 21 basic value storage unit 22 identification unit 23 evaluation unit 26 LF/HF data storage unit 27 second WL conversion processing unit 28 image Generating section BD1 to 3 1st reference data to 3rd reference data DD1 to DD3 1st dissimilarity to 3rd dissimilarity EV1 to EV3 Points MV1 Cycle center value MV2 Amplitude average value MV3 Amplitude

Claims (5)

a basic value storage unit that stores a first basic value of the cycle of the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data;
a specifying unit that specifies, as a center value of the cycle, the cycle having the largest energy among the energies for each cycle obtained by performing the second wavelet transform processing on the obtained LF/HF time-series waveform data; ,
an evaluation unit that evaluates autonomic nerve function based on the degree of difference between the first basic value and the central value of the cycle;
An autonomic nerve function evaluation device comprising: The basic value storage unit stores, as a second basic value, the center value of the amplitude of the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data,
The specifying unit specifies an average value of amplitudes of the obtained LF/HF time-series waveform data,
The autonomic nerve function evaluation device according to claim 1, wherein the evaluation unit evaluates the autonomic nerve function based on the degree of difference between the second basic value and the average value of the amplitudes. The basic value storage unit stores the amplitude as a third basic value for the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data,
The identifying unit identifies the amplitude of the obtained LF/HF time-series waveform data,
The autonomic nerve function evaluation device according to claim 1, wherein the evaluation unit evaluates the autonomic nerve function based on the degree of difference between the third basic value and the amplitude. The autonomic nerve function evaluation device according to any one of claims 1 to 3, further comprising an image generation unit that generates an image of the evaluation result obtained by the evaluation unit. a basic value storage step of storing a first basic value of the cycle of the LF/HF time-series waveform data obtained by the first wavelet transform processing based on the heartbeat interval data;
a specifying step of specifying, as a central value of the cycle, the cycle having the largest energy among the energies of each cycle obtained by performing the second wavelet transform processing on the obtained LF/HF time-series waveform data; ,
An autonomic nerve function evaluation program that causes a computer to execute an evaluation step of evaluating the autonomic nerve function based on the degree of difference between the first basic value and the central value of the cycle.

Images