Signal synthesis device, signal synthesis method, and program

Abstract

The signal synthesis device comprises a waveform synthesis part. That obtains a function obtained by subtracting a function obtained by multiplying a function determined by a parameter value of a parameter for determining a second unimodal distribution by a parameter value of a second weight parameter from a function obtained by multiplying a function determined by a parameter value of a parameter for determining a first unimodal distribution by a parameter value of a first weight parameter, and adding a parameter value of a first leveling parameter the synthesized waveform of the R wave is formed by the obtained function; obtained by subtracting, from a function obtained by multiplying a function determined by a parameter value of a parameter for determining a third unimodal distribution by a parameter value of a third weighting parameter, a function obtained by multiplying a function determined by a parameter value of a parameter for determining a fourth unimodal distribution by a parameter value of a fourth weighting parameter, and adding a parameter value of a second leveling parameter the time axis of the waveform formed by the function is reversed to form the synthetic waveform of the T wave, and the synthetic waveform of the R wave and the synthetic waveform of the T wave are connected.

Description

Technical Field

[0001] The present invention relates to a signal synthesis device, a signal synthesis method and a program. Background Art

[0002] The electrocardiogram is useful information for understanding the state of the heart. For example, using the electrocardiogram, it is possible to determine whether the subject is in a state with a high possibility of developing heart failure (Non-Patent Document 1).

[0003] Prior art literature Non-patent literature Non-patent literature 1: Hiroshi Tanaka, "Methodology of the Electrocardiographic Inverse Problem", Medical Electronics and Bioengineering, 1985, Vol. 23, No. 3, p. 147-158. Summary of the invention

[0004] Problems to be solved by the invention However, the waveform of the electrocardiogram obtained from a living body may not always be sufficient to understand the state of the heart. For example, even if the waveform of the electrocardiogram is similar, the onset of heart-related diseases is sometimes different. In this way, it is sometimes difficult to understand the state of the heart by only observing the waveform of the electrocardiogram obtained from a living body, depending on the disease. For example, in the case of heart failure, its suppression can be suppressed by observing the state of the heart based on the waveform of the electrocardiogram in daily life. In order to further improve the accuracy of suppressing the onset, it is possible to consider using other technologies such as blood collection to obtain other information, but it is unrealistic to do this in daily life. Therefore, depending on the disease, it is sometimes necessary to essentially grasp the state of the heart based only on the waveform of the electrocardiogram.

[0005] In addition, this situation is not limited to the situation where the state of the heart is understood based on the waveform of the electrocardiogram. This situation is also common in the case where the state of the heart is understood based on biological information of a time series of a channel related to the heart beat obtained by a sensor in contact with the body surface, a sensor close to the body surface, a sensor inserted into the body, a sensor buried in the body, etc. In addition, biological information of a time series related to the heart beat is, for example, a waveform showing changes in cardiac potential, a waveform showing changes in cardiac pressure, a waveform showing changes in blood flow, and a waveform showing changes in heart sounds. In addition, the waveform of the electrocardiogram is also an example of biological information of a time series related to the heart beat.

[0006] In view of the above circumstances, an object of the present invention is to provide a technology for obtaining information useful for understanding the state of the heart from one channel of time-series biological information related to cardiac pulsation.

[0007] Solutions to Solve Problems One embodiment of the present invention is a signal synthesis device, comprising a waveform synthesis unit, wherein the waveform synthesis unit performs the following operations: a waveform of a time interval of an R wave included in a waveform showing one cycle of a cardiac cycle of a heart is used as a first object time waveform, a waveform of a time interval of a T wave included in the waveform is used as a second object time waveform, a waveform obtained by inverting the time axis of the second object time waveform is used as a second object inverse time waveform, and a group including the following contents as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, a value of a parameter of a first unimodal distribution is determined or a value of a parameter of a first cumulative distribution function is determined, and a value of a parameter of a second unimodal distribution function is determined. The value of the parameter of the peak distribution or the value of the parameter of the second cumulative distribution function, the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, and when the second object inverse time waveform is approximated by the second approximate inverse time waveform, the value of the parameter of the third unimodal distribution or the value of the parameter of the third cumulative distribution function, the value of the parameter of the fourth unimodal distribution or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter, wherein the first approximate time waveform is a function obtained by multiplying the cumulative distribution function of the first unimodal distribution by the value of the first weight parameter minus the value of the first cumulative distribution function. The second approximate inverse time waveform is a waveform formed by a function obtained by multiplying the second cumulative distribution function by the value of the second weight parameter and adding the value of the first level parameter, the second approximate inverse time waveform is a waveform formed by subtracting the fourth unimodal distribution cumulative distribution function, i.e., a function obtained by multiplying the fourth cumulative distribution function by the value of the fourth weight parameter, from the third unimodal distribution cumulative distribution function, i.e., a function obtained by multiplying the third cumulative distribution function by the value of the third weight parameter and adding the value of the second level parameter, and taking the values ​​of each element included in the myocardial activity parameter group as the myocardial activity parameter value, using as Each myocardial activity parameter value included in the myocardial activity parameter group of the object's heart (referred to as the "object heart") will obtain a synthetic waveform of the time interval of the R wave by adding the myocardial activity parameter value of the first level parameter to the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function multiplied by the myocardial activity parameter value of the first weight parameter, and subtracting the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function multiplied by the myocardial activity parameter value of the second weight parameter from the function obtained by multiplying the myocardial activity parameter value of the first level parameter.A waveform obtained by reversing the time axis of a waveform formed by subtracting a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weight parameter from a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weight parameter and adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the fourth ... of the second level parameter is obtained, and the synthetic waveform of the time interval of the R wave is connected with the synthetic waveform of the time interval of the T wave, thereby obtaining a synthetic signal of a waveform showing the cardiac cycle of the heart of the object.

[0008] One embodiment of the present invention is a signal synthesis device, which includes a waveform synthesis unit, which performs the following operations: a waveform of a time interval of an R wave included in a waveform showing one cycle of a cardiac cycle of a heart is used as a first object time waveform, a waveform of a time interval of a T wave included in the waveform is used as a second object time waveform, and a group including the following contents is used as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, a value of a parameter of a first unimodal distribution is determined or a value of a parameter of a first cumulative distribution function is determined, a value of a parameter of a second unimodal distribution is determined or a value of a parameter of a second cumulative distribution function is determined, and a first weighted the value of the third unimodal distribution parameter, the value of the second weight parameter, the value of the first level parameter, and when the second object time waveform is approximated by the second approximate time waveform, the value of the parameter of the third unimodal distribution is determined or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter, the first approximate time waveform is a function obtained by multiplying the cumulative distribution function of the first unimodal distribution, that is, the first cumulative distribution function, by the value of the first weight parameter, minus the cumulative distribution function of the second unimodal distribution, that is, the second cumulative distribution function, by the value of the second cumulative distribution function. The second approximate time waveform is a time waveform formed by a function obtained by adding a function obtained by multiplying the value of the first level parameter and subtracting a function obtained by multiplying the value of the third weight parameter from the cumulative distribution function of the third unimodal distribution from 1, that is, the third cumulative distribution function multiplied by the value of the third weight parameter, and adding a function obtained by multiplying the value of the fourth weight parameter from the cumulative distribution function of the fourth unimodal distribution from 1, that is, the fourth cumulative distribution function multiplied by the value of the fourth weight parameter, and adding the value of the second level parameter, and using the values ​​of each element included in the myocardial activity parameter group as the myocardial activity parameter value, using the heart as the object Each myocardial activity parameter value included in the myocardial activity parameter group of the heart (referred to as the "target heart") is obtained by multiplying the myocardial activity parameter value of the first weight parameter by the myocardial activity parameter value, and adding the myocardial activity parameter value of the first level parameter to the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function to obtain a composite waveform of the time interval of the R wave.A time waveform formed by subtracting a function obtained by multiplying the myocardial activity parameter value of the third weight parameter from a function obtained by subtracting a function obtained by multiplying the myocardial activity parameter value of the third unimodal distribution or the third cumulative distribution function from 1 and adding the myocardial activity parameter value of the second level parameter is obtained by obtaining a synthetic waveform of the time interval of the T wave, and connecting the synthetic waveform of the time interval of the R wave with the synthetic waveform of the time interval of the T wave, thereby obtaining a synthetic signal of the waveform showing the cardiac cycle of the object heart.

[0009] One embodiment of the present invention is a signal synthesis method performed by a signal synthesis device, wherein the method includes a waveform synthesis step, wherein the waveform synthesis step performs the following operations: a waveform of a time interval of an R wave included in a waveform representing one cycle of a cardiac cycle of a heart is used as a first object time waveform, a waveform of a time interval of a T wave included in the waveform is used as a second object time waveform, a waveform obtained by inverting the time axis of the second object time waveform is used as a second object inverse time waveform, and a group including the following contents is used as a myocardial activity parameter group: a value of a parameter determining a first unimodal distribution or a parameter determining a first cumulative distribution function when the first object time waveform is approximated by a first approximate time waveform the value of the parameter for determining the second unimodal distribution or the value of the parameter for determining the second cumulative distribution function, the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, and when the second object inverse time waveform is approximated by the second approximate inverse time waveform, the value of the parameter for determining the third unimodal distribution or the value of the parameter for determining the third cumulative distribution function, the value of the parameter for determining the fourth unimodal distribution or the value of the parameter for determining the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter, wherein the first approximate time waveform is a function obtained by multiplying the cumulative distribution function of the first unimodal distribution, that is, the first cumulative distribution function by the value of the first weight parameter A time waveform formed by subtracting the cumulative distribution function of the second unimodal distribution, i.e., a function obtained by multiplying the second cumulative distribution function by the value of the second weight parameter, and adding the value of the first level parameter; the second approximate inverse time waveform is a waveform formed by subtracting the cumulative distribution function of the fourth unimodal distribution, i.e., a function obtained by multiplying the third cumulative distribution function by the value of the third weight parameter, and adding the value of the second level parameter; the values ​​of each element included in the myocardial activity parameter group are used as myocardial activity parameter values, and using Each myocardial activity parameter value included in the myocardial activity parameter group of the heart as the object (referred to as the "object heart") is obtained by multiplying the myocardial activity parameter value of the first weight parameter by the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function, and subtracting the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function from the cumulative distribution function multiplied by the myocardial activity parameter value of the first weight parameter, and adding the myocardial activity parameter value of the first level parameter to form a time waveform that is a synthetic waveform of the time interval of the R wave,A waveform obtained by reversing the time axis of a waveform formed by subtracting a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weight parameter from a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weight parameter and adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the fourth ... of the second level parameter is obtained, and the synthetic waveform of the time interval of the R wave is connected with the synthetic waveform of the time interval of the T wave, thereby obtaining a synthetic signal of a waveform showing the cardiac cycle of the heart of the object.

[0010] One embodiment of the present invention is a signal synthesis method performed by a signal synthesis device, wherein the method includes a waveform synthesis step, wherein the waveform synthesis step performs the following operations: a waveform of a time interval of an R wave included in a waveform showing an amount of one cycle of a cardiac cycle of a heart is used as a first object time waveform, a waveform of a time interval of a T wave included in the waveform is used as a second object time waveform, and a group including the following contents is used as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, a value of a parameter of a first unimodal distribution is determined or a value of a parameter of a first cumulative distribution function is determined, and a value of a parameter of a second unimodal distribution is determined or a value of a parameter of a second cumulative distribution function is determined. the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, and the value of the parameter of the third unimodal distribution or the value of the parameter of the third cumulative distribution function when the second object time waveform is approximated by the second approximate time waveform, the value of the parameter of the fourth unimodal distribution or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter, the first approximate time waveform being a function obtained by multiplying the cumulative distribution function of the first unimodal distribution, that is, the first cumulative distribution function, by the value of the first weight parameter minus the cumulative distribution function of the second unimodal distribution, that is, the second cumulative distribution function. The second approximate time waveform is a time waveform formed by a function obtained by multiplying the cumulative distribution function of the third unimodal distribution by the value of the second weight parameter and adding the value of the first level parameter, the second approximate time waveform is a time waveform formed by a function obtained by subtracting the cumulative distribution function of the third unimodal distribution, that is, the third cumulative distribution function multiplied by the value of the third weight parameter from 1 and adding the value of the second level parameter from a function obtained by subtracting the cumulative distribution function of the fourth unimodal distribution, that is, the fourth cumulative distribution function multiplied by the value of the fourth weight parameter from 1, and adding the value of the second level parameter, and taking the values ​​of each element included in the myocardial activity parameter group as the myocardial activity parameter value, using as the object The myocardial activity parameter values ​​included in the myocardial activity parameter group of the heart (referred to as the "target heart") are obtained by multiplying the myocardial activity parameter value of the first weight parameter by the myocardial activity parameter value, and the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function is subtracted from the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function and multiplied by the myocardial activity parameter value of the second weight parameter, and the myocardial activity parameter value of the first level parameter is added to form a time waveform that is a synthetic waveform of the time interval of the R wave,A time waveform formed by subtracting a function obtained by multiplying the myocardial activity parameter value of the third weight parameter from a function obtained by subtracting a function obtained by multiplying the myocardial activity parameter value of the third unimodal distribution or the third cumulative distribution function from 1 and adding the myocardial activity parameter value of the second level parameter is obtained by obtaining a synthetic waveform of the time interval of the T wave, and connecting the synthetic waveform of the time interval of the R wave with the synthetic waveform of the time interval of the T wave, thereby obtaining a synthetic signal of the waveform showing the cardiac cycle of the object heart.

[0011] One embodiment of the present invention is a program for causing a computer to function as the above-mentioned signal synthesizing device.

[0012] Effects of the Invention According to the present invention, it is possible to provide a technology for obtaining information useful for understanding the state of the heart from one channel of time-series biological information related to cardiac pulsation. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 This is a diagram showing an example of the hardware configuration of the signal analysis device 1 according to the first embodiment. Figure 2 A diagram schematically showing a function of a first cumulative distribution function multiplied by a weight, a function of a second cumulative distribution function multiplied by a weight, and an approximate time waveform which is a weighted difference between the first cumulative distribution function and the second cumulative distribution function, with respect to a first target time waveform. Figure 3 A diagram schematically showing a function of the third inverse cumulative distribution function multiplied by a weight, a function of the fourth inverse cumulative distribution function multiplied by a weight, and an approximate time waveform which is a weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, with respect to the second target time waveform. Figure 4 This is a diagram showing an example of the result of fitting the waveform of the electrocardiogram of the target heart in the first embodiment using four cumulative distribution functions. Figure 5 This is an explanatory diagram for explaining that the difference between two cumulative distribution functions in the first embodiment can be fitted to a waveform that is substantially the same as the waveform of the falling T wave. Figure 6It is a diagram schematically showing, with respect to the time waveform of the first object, a function of multiplying the first cumulative distribution function by a weight and adding a level value, a function of multiplying the second cumulative distribution function by a weight and adding a level value, and an approximate time waveform obtained by adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function and the level value. Figure 7 It is a diagram schematically showing, with respect to the time waveform of the second object, an approximate time waveform obtained by multiplying the third inverse cumulative distribution function by a weight and adding a level value, a function multiplying the fourth inverse cumulative distribution function by a weight and adding a level value, and the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function plus the level value. Figure 8 is a diagram schematically showing a Δ wave included in a target time waveform. Fig. 9 1 is a diagram showing an example of the functional configuration of the control unit 11 in the first embodiment. Fig.10 This is a flowchart showing an example of a process flow executed by the signal analysis device 1 in the first embodiment. Fig.11 This is a first diagram showing an example of analysis results of the signal analysis device 1 according to the first embodiment. Fig.12 The second diagram shows an example of the analysis result of the signal analysis device 1 according to the first embodiment. Fig.13 The third diagram shows an example of the analysis result of the signal analysis device 1 in the first embodiment. Fig.14 The fourth diagram shows an example of the analysis result of the signal analysis device 1 in the first embodiment. Fig.15 This is a first explanatory diagram of an example of an electrocardiogram of a ventricular extrasystole analyzed by the signal analysis device 1 according to the first embodiment. Fig.16 This is a second explanatory diagram showing an example of an electrocardiogram of a ventricular extrasystole analyzed by the signal analysis device 1 according to the first embodiment. Fig.17 This is a third explanatory diagram showing an example of an electrocardiogram of a ventricular extrasystole analyzed by the signal analysis device 1 according to the first embodiment. Fig.18 This is a first explanatory diagram showing an electrocardiogram of a target heart in the depolarization phase of Buddag syndrome type 1 analyzed by the signal analysis device 1 according to the first embodiment. Fig.19 This is a second explanatory diagram showing an electrocardiogram of a target heart in the depolarization phase of Buddag syndrome type 1 analyzed by the signal analysis device 1 of the first embodiment. Fig. 20 This is a third explanatory diagram showing an electrocardiogram of a target heart in the depolarization phase of Buddag syndrome type 1 analyzed by the signal analysis device 1 according to the first embodiment. Fig.21 This is a diagram showing a first example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig. 22 This is a diagram showing a second example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.23 This is a diagram showing a third example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.24 This is a diagram showing a fourth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.25 This is a diagram showing a fifth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.26 This is a diagram showing a sixth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig. 27 This is a diagram showing a seventh example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.28 This is a diagram showing an eighth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.29 This is a diagram showing a ninth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.30 This is a diagram showing a tenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.31 This is a diagram showing an eleventh example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.32 This is a diagram showing a twelfth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.33 This is a diagram showing a thirteenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.34 This is a diagram showing a fourteenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.35 This is a diagram showing a fifteenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.36 This is a diagram showing a sixteenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.37 This is a diagram showing a seventeenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.38 This is a diagram showing an eighteenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.39 This is a diagram showing a nineteenth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.40 This is a diagram showing the twentieth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.41 This is a diagram showing a twenty-first example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.42 This is a diagram showing a twenty-second example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.43 This is a diagram showing a twenty-third example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.44 This is a diagram showing a twenty-fourth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.45 This is a diagram showing a twenty-fifth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.46 This is a diagram showing a twenty-sixth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.47 This is a diagram showing a twenty-seventh example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.48 This is a diagram showing a twenty-eighth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.49 This is a diagram showing a twenty-ninth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.50 This is a diagram showing the thirtieth example of the electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.51 This is a diagram showing a thirty-first example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.52 This is a diagram showing a thirty-second example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.53 This is a diagram showing a thirty-third example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.54 This is a diagram showing a thirty-fourth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.55 This is a diagram showing a thirty-fifth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.56 This is a diagram showing a thirty-sixth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.57 This is a diagram showing a thirty-seventh example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.58 This is a diagram showing a thirty-eighth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.59 This is a diagram showing a thirty-ninth example of an electrocardiogram analyzed by the signal analysis device 1 according to the first embodiment. Fig.60 This is a diagram showing an example of the functional configuration of the signal conversion device 2 and the signal synthesis device 3 in the second embodiment and its Modification 1 and Modification 5. Fig.61 This is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in the second embodiment and its Modifications 1 and 5. Fig.62 It is a diagram showing an example of the functional configuration of the signal conversion device 2 and the signal synthesis device 3 in Modification 2, Modification 3, and Modification 5 of the second embodiment. Fig.63 This is a flowchart showing an example of a process flow executed by the signal conversion device 2 and the signal synthesis device 3 in Modification 2, Modification 3, and Modification 5 of the second embodiment. Fig.64 This is a diagram showing an example of the functional configuration of the signal conversion device 2 and the signal synthesis device 3 in the fourth modification of the second embodiment. Fig.65 This is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in the fourth modification of the second embodiment. Fig.66 This is a diagram showing an example of the functional configuration of the signal conversion device 2 and the signal synthesis device 3 in the sixth modification of the second embodiment. Fig.67 This is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in the sixth modification of the second embodiment. DETAILED DESCRIPTION

[0014] <First Embodiment> Figure 1 1 is a diagram showing an example of the hardware structure of the signal analysis device 1 of the first embodiment. In the following, for the sake of simplicity, the signal analysis device 1 is described by taking the case of analyzing the waveform of one channel of the electrocardiogram as an example. However, the signal analysis device 1 is not limited to the waveform of the electrocardiogram, and the same analysis can be performed if it is based on the biological information of the time series related to the heart beat. In addition, the biological information of the time series related to the heart beat is, for example, a waveform showing the change of the cardiac potential, a waveform showing the change of the pressure of the heart, a waveform showing the change of the blood flow, and a waveform showing the change of the heart sound. Therefore, the signal analysis device 1 is not limited to the waveform of the electrical signal obtained from the body surface, and a sensor in contact with the body surface, a sensor close to the body surface, a sensor inserted into the body, a sensor buried in the body, etc., whether it is on the body surface or in the body, etc., as long as it shows the waveform of the cardiac cycle obtained from a certain point, any can be used.

[0015] That is, instead of the waveform of the electrocardiogram, the signal analysis device 1 may use a waveform showing changes in cardiac pressure as biological information of a time series related to cardiac pulsation. In addition, instead of the waveform of the electrocardiogram, the signal analysis device 1 may use a waveform showing changes in blood flow as biological information of a time series related to cardiac pulsation. In addition, instead of the waveform of the electrocardiogram, the signal analysis device 1 may use a waveform showing changes in heart sounds as biological information of a time series related to cardiac pulsation. In addition, the waveform of the electrocardiogram is also an example of biological information of a time series related to cardiac pulsation. In addition, biological information of a time series related to cardiac pulsation may also be biological information of a time series related to the periodic pulsation of the heart.

[0016] The signal analysis device 1 obtains the waveform of the electrocardiogram of the heart of the analysis object (hereinafter referred to as the "target heart"). Based on the obtained waveform of the electrocardiogram, the signal analysis device 1 obtains parameters showing the activity of the myocardium of the target heart (hereinafter referred to as "myocardial activity parameters"), which include at least any one of the parameters showing the activity of the outer layer of the myocardium of the target heart (hereinafter referred to as the "outer myocardial layer") and the parameters showing the activity of the inner layer of the myocardium of the target heart (hereinafter referred to as the "inner myocardial layer") (hereinafter referred to as the "inner myocardial layer parameters".).

[0017] Here, the relationship between the activity of the myocardium and the waveform of the electrocardiogram is described. In the medical field, there is a known model called the cardiac electromotive force dipole model (reference document 1) that describes the relationship between the movement of the myocardium and the electrocardiogram. According to the cardiac electromotive force dipole model, the myocardium is modeled as two layers: the outer myocardial layer and the inner myocardial layer.

[0018] Reference 1: Tanaka Yoshifumi "Knowledge of the ECG Waveform Heart Muscle Activity Potential Analysis" by Gakuken Mikaru Hidejunsha (2012).

[0019] In the cardiac electromotive force dipole model, the outer layer of the myocardium and the inner layer of the myocardium are modeled as different sources of electromotive force. According to the cardiac electromotive force dipole model, the composite wave of the epicardial myocardial action potential and the endocardial myocardial action potential is roughly consistent with the time variation of the body surface potential observed on the body surface. The graph showing the time variation of the body surface potential is the waveform of the electrocardiogram. The epicardial myocardial action potential is the result of directly measuring the electromotive force variation generated by the pulsation of the outer layer of the myocardium by inserting a catheter electrode. The endocardial myocardial action potential is the result of directly measuring the electromotive force variation generated by the pulsation of the inner layer of the myocardium by inserting a catheter electrode. This is a brief description of the cardiac electromotive force dipole model.

[0020] However, the outer myocardium in the electromotive force dipole model is a collection of cells. Therefore, the timing of the pulsation of the cells in the outer myocardium in one beat of the outer myocardium may not be the same in all cells, and there may be a distribution in the timing of the pulsation. The same is true for the inner myocardium. That is, the timing of the pulsation of the cells in the inner myocardium in one beat of the inner myocardium may not be the same in all cells, and there may be a distribution in the timing of the pulsation. However, the electromotive force dipole model does not assume the possibility of such a distribution in the timing of the pulsation of the cells.

[0021] In addition, there is also a distribution in the distance between each cell and the electrode located on the body surface, and the structure of the body tissue between each cell and the electrode located on the body surface is also different. Therefore, the conversion efficiency of the excitation of the cells in the outer layer of the myocardium reflected in the waveform of the electrocardiogram may not be the same in all cells, and there may be a distribution in the conversion efficiency of the pulsation reflected in the waveform of the electrocardiogram. Similarly, the conversion efficiency of the excitation of the cells in the inner layer of the myocardium reflected in the waveform of the electrocardiogram may not be the same in all cells, and there may be a distribution in the conversion efficiency of the pulsation reflected in the waveform of the electrocardiogram. However, the cardiac electromotive force dipole model does not assume the possibility of a distribution in the conversion efficiency of the pulsation of the cells reflected in the waveform of the electrocardiogram.

[0022] In the signal analysis device 1, taking into account the possibility of distribution in the timing of cell pulsation and the possibility of distribution in the conversion efficiency reflected by the cell pulsation in the waveform of the electrocardiogram, an analysis is performed assuming that the distribution of the timing of the start of the pulsation of each cell in the outer layer of the myocardium as expressed in the waveform of the electrocardiogram, the distribution of the timing of the start of the pulsation of each cell in the inner layer of the myocardium as expressed in the waveform of the electrocardiogram, the distribution of the timing of the end of the pulsation of each cell in the outer layer of the myocardium as expressed in the waveform of the electrocardiogram, and the distribution of the timing of the end of the pulsation of each cell in the inner layer of the myocardium as expressed in the waveform of the electrocardiogram are each Gaussian distributions. That is, in the signal analysis device 1, an analysis is performed assuming the following contents: the start of the activity of the inner myocardium generated by all cells in the inner myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, the start of the activity of the outer myocardium generated by all cells in the outer myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, the end of the activity of the inner myocardium generated by all cells in the inner myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, and the end of the activity of the outer myocardium generated by all cells in the inner myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution.

[0023] In addition, in the signal analysis device 1, a cumulative Gaussian distribution function can be used, but a sigmoid function, a Gompertz function, a logical function, etc. can also be used instead of the cumulative Gaussian distribution function. That is, in the signal analysis device 1, instead of the cumulative Gaussian distribution function, a cumulative distribution function of a unimodal distribution can also be used, that is, a cumulative distribution function corresponding to a distribution in which the value monotonically increases before the moment when the value becomes the maximum value and the value monotonically decreases after the moment when the value becomes the maximum value. However, the cumulative distribution function used by the signal analysis device 1 needs to be a cumulative distribution function whose shape can be determined by a parameter representing the shape of the cumulative distribution function or a parameter representing the shape of a unimodal distribution as a cumulative source of the cumulative distribution function. Hereinafter, the parameter representing the shape of the cumulative distribution function (i.e., the parameter determining the cumulative distribution function) is referred to as the shape parameter of the cumulative distribution function, and the parameter representing the shape of the unimodal distribution (i.e., the parameter determining the unimodal distribution) is referred to as the shape parameter of the unimodal distribution. Of course, the shape parameter of the cumulative distribution function and the shape parameter of the unimodal distribution are substantially the same. For example, when the cumulative distribution function used by the signal analysis device 1 is a cumulative Gaussian distribution function, the standard deviation (or variance) and mean value of the Gaussian distribution as the cumulative source of the cumulative Gaussian distribution function are shape parameters of the unimodal distribution and are also shape parameters of the cumulative distribution function.

[0024] The signal analysis device 1 takes the waveform of an arbitrary time interval of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram of the target heart obtained as the target time waveform, and obtains the parameters that indicate the characteristics of the target time waveform, i.e., the myocardial activity parameters, by determining the parameters of the first unimodal distribution or the first cumulative distribution function and determining the parameters of the second unimodal distribution or the second cumulative distribution function when approximating the target time waveform by the time waveform obtained by the difference or weighted difference between the first cumulative distribution function as the first unimodal distribution cumulative distribution function and the second cumulative distribution function as the second unimodal distribution cumulative distribution function (hereinafter referred to as the "approximate time waveform"). Hereinafter, approximating the target time waveform by the approximate time waveform, i.e., determining the approximate time waveform, is referred to as "fitting", and the first cumulative distribution function and the second cumulative distribution function included in the approximate time waveform are referred to as "fitting results". In addition, when the signal analysis device 1 uses the weighted difference for approximation, the weight assigned to the first cumulative distribution function and the weight assigned to the second cumulative distribution function can be obtained as parameters representing the characteristics of the object time waveform (i.e., myocardial activity parameters), and the ratio of the weight assigned to the first cumulative distribution function to the weight assigned to the second cumulative distribution function can be obtained as parameters representing the characteristics of the object time waveform (i.e., myocardial activity parameters).

[0025] When the object time waveform is approximated by the approximate time waveform obtained by the difference between the first cumulative distribution function and the second cumulative distribution function, for example, the signal analysis device 1 generates a time waveform obtained by the difference between the first cumulative distribution function and the second cumulative distribution function (hereinafter referred to as "candidate time waveform") using a combination (M×N types) of parameters for determining the cumulative distribution function for each of the plurality (M) candidates of the first cumulative distribution function and a combination (M×N types) of parameters for determining the cumulative distribution function for each of the plurality (N) candidates of the second cumulative distribution function, respectively, determines the candidate time waveform closest to the object time waveform among the generated M×N types of candidate time waveforms as the approximate time waveform, and obtains the parameters for determining the candidate of the first cumulative distribution function and the parameters for determining the candidate of the second cumulative distribution function used in the generation of the determined approximate time waveform as parameters representing the characteristics of the object time waveform. The process of determining the candidate time waveform closest to the object time waveform as the approximate time waveform can be performed, for example, by determining the candidate time waveform with the smallest square error between the candidate time waveform and the object time waveform.

[0026] Alternatively, for example, the signal analysis device 1 determines the candidate time waveform finally obtained as the approximate time waveform by obtaining a time waveform obtained by the difference between a first candidate for the approximate object time waveform and a second candidate for the cumulative distribution function, i.e., the candidate time waveform, and updating at least any one of the parameters for determining each cumulative distribution function in a direction in which the square error between the candidate time waveform and the object time waveform becomes smaller, repeatedly until the square error becomes below a prescribed reference or repeats a prescribed number of times, and obtains the parameters for determining the candidate for the first cumulative distribution function and the parameters for determining the candidate for the second cumulative distribution function used in generating the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0027] In the case of approximating the object time waveform using an approximate time waveform obtained by the weighted difference between the first cumulative distribution function and the second cumulative distribution function, for example, the signal analysis device 1 uses a combination (K×L×M×N types) of cumulative distribution function parameters for each of the multiple (M) candidates for the first cumulative distribution function, parameters for each of the multiple (N) candidates for the second cumulative distribution function, multiple (K) candidates for weights assigned to the first cumulative distribution function, and multiple (K) candidates for weights assigned to the second cumulative distribution function, respectively, to generate a time waveform obtained by the weighted difference between the first cumulative distribution function and the second cumulative distribution function, i.e., a candidate time waveform that is closest to the object time waveform among the generated K×L×M×N candidate time waveforms as the approximate time waveform, and obtains the parameters for determining the candidates for the first cumulative distribution function, the parameters for determining the candidates for the second cumulative distribution function, the weights assigned to the first cumulative distribution function, and the weights assigned to the second cumulative distribution function used in the generation of the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0028] Alternatively, for example, the signal analysis device 1 obtains a time waveform obtained by obtaining a weighted difference between a candidate for a first cumulative distribution function that approximates an object time waveform and a candidate for a second cumulative distribution function, i.e., a candidate time waveform, and updates at least any one of the parameters for determining each cumulative distribution function and the weight assigned to each cumulative distribution function in a direction in which the square error between the candidate time waveform and the object time waveform becomes smaller, repeatedly until the square error becomes below a prescribed benchmark or a prescribed number of times, thereby determining the candidate time waveform finally obtained as an approximate time waveform, and obtaining the parameters for determining the candidate for the first cumulative distribution function, the parameters for determining the candidate for the second cumulative distribution function, the weight assigned to the first cumulative distribution function, and the weight assigned to the second cumulative distribution function used in generating the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0029] Hereinafter, the process of acquiring parameters representing the characteristics of the target time waveform included in the waveform corresponding to one cycle of the acquired electrocardiogram of the target heart is referred to as myocardial activity information parameter acquisition process.

[0030] When the information representing the moment is set to x, when Gaussian distribution is used as the first unimodal distribution and the second unimodal distribution, the first unimodal distribution is expressed by the following formula (1), the first cumulative distribution function f1(x) is expressed by formula (2), the second unimodal distribution is expressed by formula (3), and the second cumulative distribution function f2(x) is expressed by formula (4).

[0031] [Formula 1]

[0032] [Formula 2]

[0033] [Formula 3]

[0034] [Formula 4]

[0035] Formula (1) is a formula with a mean value of μ1 and a standard deviation of σ1 (variance of σ1 2 ) is a Gaussian distribution (normal distribution). Formula (3) is a Gaussian distribution with a mean of μ2 and a standard deviation of σ2 (variance of σ2 2 ) is a Gaussian distribution (normal distribution). Formula (2) is the cumulative distribution function of formula (1). Formula (4) is the cumulative distribution function of formula (3). "erf" is a sigmoid function (error function). The unit of the information x representing the time is arbitrary. For example, the sample number or relative time starting from the waveform of one cycle of the electrocardiogram can be used as the information x representing the time.

[0036] The difference between the first cumulative distribution function and the second cumulative distribution function is expressed by, for example, the following equation (5). The function represented by the following equation (5) is a function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0037] [Formula 5]

[0038] That is, when the object time waveform is approximated by the approximate time waveform which is the difference between the first cumulative distribution function and the second cumulative distribution function, the parameters that determine the first unimodal distribution or the parameters that determine the first cumulative distribution function, i.e., the mean value μ1 and the standard deviation σ1, and the parameters that determine the second unimodal distribution or the second cumulative distribution function, i.e., the mean value μ2 and the standard deviation σ2, are obtained as parameters that represent the characteristics of the object time waveform. In addition, the variance may be obtained as a parameter instead of the standard deviation. This is also the same for the subsequent description where the standard deviation is obtained as a parameter.

[0039] The weighted difference between the first cumulative distribution function and the second cumulative distribution function is expressed, for example, by the following formula (6), where the weight of the first cumulative distribution function is set to k1 and the weight of the second cumulative distribution function is set to k2. The function shown in the following formula (6) is a function obtained by subtracting the function of the second cumulative distribution function multiplied by the weight k2 from the function of the first cumulative distribution function multiplied by the weight k1.

[0040] [Formula 6]

[0041] That is, when the object time waveform is approximated by an approximate time waveform which is a weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least the parameters that determine the first unimodal distribution or the parameters that determine the first cumulative distribution function, i.e., the mean value μ1 and the standard deviation σ1, and the parameters that determine the second unimodal distribution or the parameters that determine the second cumulative distribution function, i.e., the mean value μ2 and the standard deviation σ2 are obtained as parameters that represent the characteristics of the object time waveform. In addition, the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function, or the ratio (k1 / k2 or k2 / k1) of the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function may be obtained as parameters that represent the characteristics of the object time waveform.

[0042] When the function of formula (6) is a function obtained by subtracting a function obtained by multiplying the second cumulative distribution function by the weight k2 from a function obtained by multiplying the first cumulative distribution function by the weight k1, both the weight k1 and the weight k2 are positive values. However, when the subject's heart is in a specific state, the possibility that at least one of the weight k1 and the weight k2 obtained by fitting is not a positive value cannot be denied. Therefore, although the signal analysis device 1 can fit both the weights k1 and k2 to positive values, it is not necessary to fit both the weights k1 and k2 to positive values.

[0043] In addition, the signal analysis device 1 can use the R wave and the T wave included in the waveform of one cycle of the electrocardiogram of the object heart as the first object time waveform, and the T wave as the second object time waveform, and obtain the above-mentioned parameters representing the characteristics of the object time waveform for the first object time waveform and the second object time waveform, respectively.

[0044] For example, when the first target time waveform (i.e., R wave) is approximated by the difference between the first cumulative distribution function and the second cumulative distribution function, the first target time waveform is approximated by the approximate time waveform of equation (9), which is obtained from the first cumulative distribution function f shown in equation (7). a (x) minus the second cumulative distribution function f shown in equation (8) b (x) will determine the parameter of the first cumulative distribution function, i.e., the mean value μ a and standard deviation σ a , and determine the parameter of the second cumulative distribution function, namely the mean value μ b and standard deviation σ b The parameters representing the characteristics of the first target time waveform (ie, the R wave) are acquired.

[0045] [Formula 7]

[0046] [Formula 8]

[0047] [Formula 9]

[0048] For example, when the first target time waveform (i.e., R wave) is approximated by the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the first target time waveform is approximated by the approximation time waveform of equation (10), which is obtained from the first cumulative distribution function f shown in equation (7). a (x) multiplied by weight k a The function minus the second cumulative distribution function f shown in formula (8) b (x) multiplied by weight k b The function obtained by the function of will at least determine the parameters of the first cumulative distribution function, namely the mean value μ a and standard deviation σ a And determine the parameter of the second cumulative distribution function, namely the mean value μ b and standard deviation σ b The parameters representing the characteristics of the first target time waveform (ie, R wave) are obtained. In addition, the weight k of the first cumulative distribution function may be a and the weight k of the second cumulative distribution function b, or the weight k of the first cumulative distribution function a and the weight k of the second cumulative distribution function b The ratio (k a / k b or k b / k a ) also obtains a parameter that represents the characteristics of the parameter representing the characteristics of the first object time waveform (ie, the R wave).

[0049] [Formula 10]

[0050] The function according to formula (10) is the first cumulative distribution function multiplied by the weight k a minus the second cumulative distribution function multiplied by the weight k b The function obtained by the function of weight k a and weight k b However, when the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. a and weight k b Therefore, although the signal analysis device 1 can set the weight k a and weight k b are all fitted as positive values, but there is no need to set the weight k a and weight k b All fit to positive values.

[0051] Since the R wave corresponds to the excitation of all myocardial cells starting in sequence according to the Gaussian distribution, for the R wave, as described above, it is sufficient to approximate the object time waveform in the positive direction of time by the approximate time waveform of the difference or weighted difference of two cumulative Gaussian distributions. On the other hand, according to the T wave, the excitation of all myocardial cells awakens in sequence according to the Gaussian distribution, which can be explained as a phenomenon that the T wave is in the opposite direction of the R wave on the time axis. That is, for the T wave, it is sufficient to approximate the waveform after the time axis of the object time waveform is reversed by the difference or weighted difference of the cumulative Gaussian distribution. It will be referred to as the first method below. In addition, according to the T wave, all myocardial cells awaken from the excited state according to the Gaussian distribution, for the T wave, it can also be said that the object time waveform in the positive direction of time can be approximated by the difference or weighted difference of two functions (the function of the cumulative Gaussian distribution subtracted from 1). It will be referred to as the second method below. The following describes specific examples of the first method and the second method. In order to avoid confusion between the above-mentioned cumulative distribution function for the R wave and the cumulative distribution function for the T wave described below, the above-mentioned first cumulative distribution function will be referred to as the third cumulative distribution function, and the above-mentioned second cumulative distribution function will be referred to as the fourth cumulative distribution function for the T wave.

[0052] When the first method is used to approximate the second target time waveform (i.e., T wave) by the difference between the third cumulative distribution function and the fourth cumulative distribution function, the information indicating the time in the reverse direction is set to x', and the waveform obtained by inverting the time axis of the second target time waveform is called the second target inverse time waveform. The second target inverse time waveform is approximated by the approximate inverse time waveform of formula (13), which is obtained by converting the third cumulative distribution function f shown in formula (11) into the second target inverse time waveform. e (x') minus the fourth cumulative distribution function f shown in formula (12) g The function obtained by (x') will determine the parameter of the third cumulative distribution function, namely the mean value μ e and standard deviation σ e , and determine the parameter of the fourth cumulative distribution function, namely the mean value μ g and standard deviation σ g The parameters representing the characteristics of the second target time waveform (ie, T wave) are acquired.

[0053] [Formula 11]

[0054] [Formula 12]

[0055] [Formula 13]

[0056] For example, when the second target time waveform (i.e., T wave) is approximated by the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function using the first method, the second target inverse time waveform is approximated by the approximate inverse time waveform of equation (14), which is obtained from the third cumulative distribution function f shown in equation (11): e (x') multiplied by the weight k e The function of minus the fourth cumulative distribution function f shown in formula (12) g (x') multiplied by the weight k g The function obtained by the function of will at least determine the parameters of the third cumulative distribution function, namely the mean value μ e and standard deviation σ e , and determine the parameter of the fourth cumulative distribution function, namely the mean value μ g and standard deviation σ g The parameter representing the characteristic of the second target time waveform (ie, T wave) is obtained. In addition, the weight k of the third cumulative distribution function may be e and the weight k of the fourth cumulative distribution function g , or the weight k of the third cumulative distribution function e With the fourth cumulative distribution function weight k g The ratio (ke / k g or k g / k e ) is also obtained as a parameter representing the characteristics of the second object time waveform (ie, T wave).

[0057] [Formula 14]

[0058] According to formula (14), the function is obtained by multiplying the third cumulative distribution function by the weight k e minus the fourth cumulative distribution function multiplied by the weight k g The function obtained by the function of weight k e and weight k g However, when the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. e and weight k g Therefore, although the signal analysis device 1 can set the weight k e and weight k g are all fitted as positive values, but there is no need to set the weight k e and weight k g All fit to positive values.

[0059] For example, when using the second method by subtracting the third cumulative distribution function f shown in equation (15) from 1 c (x) and the function f' c (x) (hereinafter referred to as the "third inverse cumulative distribution function") is the sum of the fourth cumulative distribution function f shown in equation (16) subtracted from 1 d (x) and the function f' d When the second target time waveform (i.e., T wave) is approximated by the difference between the third inverse cumulative distribution function f' and the second target time waveform (i.e., T wave), the second target time waveform is approximated by the approximation time waveform of formula (17), which is obtained from the third inverse cumulative distribution function f' c (x) minus the fourth inverse cumulative distribution function f' d (x) will determine the parameter of the third cumulative distribution function, namely the mean value μ c and standard deviation σ c , and determine the parameter of the fourth cumulative distribution function, namely the mean value μ d and standard deviation σ d The parameters representing the characteristics of the second target time waveform (ie, T wave) are acquired.

[0060] [Formula 15]

[0061] [Formula 16]

[0062] [Formula 17]

[0063] For example, when the second method is used to approximate the second target time waveform (i.e., T wave) by the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, the second target time waveform is approximated by the approximate time waveform of formula (18), which is obtained from the third inverse cumulative distribution function f' c (x) multiplied by weight k c minus the fourth inverse cumulative distribution function f' d (x) multiplied by weight k d The function obtained by the function of will determine the parameter of the third cumulative distribution function, namely the mean value μ c and standard deviation σ c , and determine the parameter of the fourth cumulative distribution function, namely the mean value μ d and standard deviation σ d The parameter representing the characteristic of the second target time waveform (ie, T wave) is obtained. In addition, the weight k of the third inverse cumulative distribution function may be c and the weight k of the fourth inverse cumulative distribution function d , or the weight k of the third inverse cumulative distribution function c with the fourth inverse cumulative distribution function with weight k d The ratio (k c / k d or k d / k c ) is obtained as a parameter representing the characteristics of the second object time waveform (ie, T wave).

[0064] [Formula 18]

[0065] According to formula (18), the function is the third inverse cumulative distribution function multiplied by the weight k c minus the fourth inverse cumulative distribution function multiplied by the weight k d The function obtained by the function of weight k c and weight k d However, when the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. c and weight k d Therefore, although the signal analysis device 1 can set the weight k c and weight k d are all fitted as positive values, but there is no need to set the weight k cand weight k d All fit to positive values.

[0066] In addition, since the approximate time waveform of equation (17) is derived from the fourth cumulative distribution function f d (x) minus the third cumulative distribution function f c (x), is the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d In addition, the approximate time waveform of equation (18) is the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d The shape of the curve obtained by adding a constant term to the weighted difference of (x) is similar to the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d In addition, as mentioned above, it can be explained that the T wave is in the opposite direction of the R wave on the time axis. For the T wave, the third cumulative distribution function f e (x) and the fourth cumulative distribution function f g Therefore, in the following description of the T wave, sometimes only the cumulative distribution function is used instead of describing the cumulative distribution function and the inverse cumulative distribution function at the same time.

[0067] Figure 2 is a schematic diagram showing a first cumulative distribution function f with respect to a first target time waveform (ie, R wave). a (x) multiplied by weight k a The function k a f a (x), the second cumulative distribution function f b (x) multiplied by weight k b The function k b f b (x), and an approximate time waveform k which is a weighted difference between the first cumulative distribution function and the second cumulative distribution function a f a (x)-k b f b (x). The single-point dashed line is the first cumulative distribution function f a (x) multiplied by weight k a The function k a f a (x), the double-dotted line is the second cumulative distribution function f b (x) multiplied by weight k b The function k b f b (x), the dotted line is the approximate time waveform ka f a (x)-k b f b (x). The approximate time waveform k a f a (x)-k b f b (x) is a waveform that approximates the first target time waveform (ie, R wave).

[0068] Figure 3 is a schematic diagram showing a third inverse cumulative distribution function f′ with respect to a second object time waveform (ie, T wave). c (x) = 1-f c (x) multiplied by weight k c The function k c f' c (x), the fourth inverse cumulative distribution function f' d (x) = 1-f d (x) multiplied by weight k d The function k d f' d (x), and an approximate time waveform k which is a weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function c f' c (x)-k d f' d (x) is a schematic diagram. The single-point dashed line is the third inverse cumulative distribution function f' c (x) = 1-f c (x) multiplied by weight k c The function k c f' c (x), the double-dotted line is the fourth inverse cumulative distribution function f' d (x) = 1-f d (x) multiplied by weight k d The function k d f' d (x), the dotted line is the approximate time waveform k c f' c (x)-k d f' d (x). The approximate time waveform k c f' c (x)-k d f' d (x) is a waveform that approximates the second target time waveform (ie, T wave).

[0069] Figure 4This is a diagram schematically showing the result of fitting each of the first object time waveform and the second object time waveform by the difference between the two cumulative distribution functions, using the R wave included in the waveform of one cycle of the electrocardiogram of the object heart in the first embodiment as the first object time waveform and the T wave as the second object time waveform. Figure 4 The horizontal axis represents time, and the vertical axis represents potential. The units of the horizontal and vertical axes are both arbitrary units.

[0070] Specifically, Figure 4 This is an example of fitting the first target time waveform (i.e., R wave) by the difference between the first cumulative distribution function and the second cumulative distribution function, and fitting the second target time waveform (i.e., T wave) by the difference between the third cumulative distribution function and the fourth cumulative distribution function. The domain of the first cumulative distribution function is the same as the domain of the second cumulative distribution function, which is from time T1 to time T3, which is the time interval of the first target time waveform (i.e., R wave). The domain of the third cumulative distribution function is the same as the domain of the fourth cumulative distribution function, which is from time T4 to time T6.

[0071] Figure 4 The “first fitting result” and “second fitting result” are the fitting results for the R wave. Figure 4 The “third fitting result” and “fourth fitting result” in FIG. 1 are the fitting results for the T wave.

[0072] exist Figure 4 In , the “first fitting result” shows the first cumulative distribution function in the fitting result of the first object time waveform (ie, R wave) of the electrocardiogram. Figure 4 In , the "second fitting result" shows the second cumulative distribution function in the fitting result of the first object time waveform (ie, R wave) of the electrocardiogram. Figure 4 In FIG. 1 , “third fitting result” shows the third cumulative distribution function in the fitting result of the second object time waveform (ie, T wave) of the electrocardiogram. Figure 4 In FIG. 1 , “the fourth fitting result” shows the fourth cumulative distribution function in the fitting result of the second object time waveform (ie, T wave) of the electrocardiogram. Figure 4 In the figure, “body surface potential” represents the waveform of the electrocardiogram of the object of fitting.

[0073] In addition, the signal analysis device 1 does not perform fitting for the period from time T3 to time T4, which is neither the time period of the first target time waveform (i.e., R wave) nor the time period of the second target time waveform (i.e., T wave). Figure 4In the example, the first fitting result at time T3 and the third fitting result at time T4 are connected, and the second fitting result at time T3 and the fourth fitting result at time T4 are connected. That is, when the signal analysis device 1 displays the fitting results, as shown in FIG. Figure 4 As shown, the signal analysis device 1 only needs to display a line connecting the first fitting result at time T3 and the third fitting result at time T4, and the second fitting result at time T3 and the fourth fitting result at time T4 using a predetermined function such as a constant function or a linear function.

[0074] In addition, when the signal analysis device 1 displays the fitting results, the weight value may be corrected so that the first fitting result at time T3 and the third fitting result at time T4 can be displayed with the same value. That is, the actual first fitting result at time T3 is k a f a (T3), the actual third fitting result at time T4 is k c f' c (T4), but we can also find the solution that satisfies k a f a (T3) = α1k c f' c (T4) α1, use α1k c Instead of weight k c To display the fitting results, you can also use k a / α1 replaces weight k a Similarly, when the signal analysis device 1 displays the fitting results, the weight value can also be corrected so that the second fitting result at time T3 and the fourth fitting result at time T4 can be displayed with the same value. That is, the second fitting result at time T3 is k b f b (T3), the fourth fitting result at time T4 is k d f' d (T4), but we can also find the solution that satisfies k b f b (T3) = α2k d f' d (T4) α2, use α2k d Instead of weight k d To display the fitting results, you can also use k b / α2 replaces the weight k b to display the fitting results.

[0075] In addition, the fitting of the first object time waveform and the fitting of the second object time waveform may not be performed separately. That is, the fitting of the first object time waveform and the second object time waveform may be performed together as the fitting of the two. For example, when the signal analysis device 1 performs the fitting of the first object time waveform and the second object time waveform together, the fitting may be performed in consideration of the fact that the difference between the first fitting result at time T3 and the third fitting result at time T4 becomes smaller, and the difference between the second fitting result at time T3 and the fourth fitting result at time T4 becomes smaller.

[0076] Figure 5 This is an explanatory diagram for explaining that typical features of the T wave can be visualized by approximating the T wave with a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function and displaying the third and fourth inverse cumulative distribution functions or displaying parameters that determine each cumulative distribution function. Figure 5 Four images, namely, image G1, image G2, image G3, and image G4, are shown. Each of images G1 to G4 is a graph in which the horizontal axis represents time and the vertical axis represents potential. Figure 5 The units of the horizontal axis and the vertical axis of each of the images G1 to G4 are arbitrary units.

[0077] Figure 5 The "first function" of is an example of the third inverse cumulative distribution function. Figure 5 The “second function” of is an example of the fourth inverse cumulative distribution function. Figure 5 The “third function” of represents a function obtained by subtracting the “second function” from the “first function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The shape of the "third function" is roughly the same as the shape of the T wave of a normal heart.

[0078] Figure 5 The "fourth function" of is an example of the third inverse cumulative distribution function. Figure 5 The "fifth function" is an example of the fourth inverse cumulative distribution function. Figure 5 The “sixth function” of represents a function obtained by subtracting the “fifth function” from the “fourth function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The shape of the "sixth function" is roughly the same as the shape of a T wave depression, which is one of the three typical patterns of T wave abnormality. Figure 5The interval between the declining part of the third inverse cumulative distribution function and the declining part of the fourth inverse cumulative distribution function in image G2 is narrower than the interval between the declining part of the third inverse cumulative distribution function and the declining part of the fourth inverse cumulative distribution function in image G1 of a normal heart, thereby visualizing that during the T wave decline, the activity of the outer layer of the myocardium is less delayed from the activity of the inner layer of the myocardium.

[0079] Figure 5 The "seventh function" is an example of the third inverse cumulative distribution function. Figure 5 The "eighth function" of is an example of the fourth inverse cumulative distribution function. Figure 5 The “ninth function” of represents a function obtained by subtracting the “eighth function” from the “seventh function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The shape of the "ninth function" is roughly the same as the shape of an elevation of the T wave, which is one of the three typical patterns of T wave abnormality. Figure 5 The interval between the declining part of the third inverse cumulative distribution function and the declining part of the fourth inverse cumulative distribution function in image G3 of the normal heart is wider than the interval between the declining part of the third inverse cumulative distribution function and the declining part of the fourth inverse cumulative distribution function in image G1 of the normal heart, thereby visualizing that in the increase of the T wave, the activity of the outer layer of the myocardium is delayed significantly from the activity of the inner layer of the myocardium.

[0080] Figure 5 The "tenth function" of is an example of the third inverse cumulative distribution function. Figure 5 The "eleventh function" is an example of the fourth inverse cumulative distribution function. Figure 5 The “twelfth function” of represents a function obtained by subtracting the “eleventh function” from the “tenth function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The shape of the "twelfth function" is roughly the same as the shape of a negative T wave, one of the three typical patterns of T wave abnormalities. Figure 5 The order of the declining part of the third inverse cumulative distribution function and the declining part of the fourth inverse cumulative distribution function in image G4 is opposite to the order of the declining part of the third inverse cumulative distribution function and the declining part of the fourth inverse cumulative distribution function in image G1 of the normal heart, thereby visualizing that in the negative T wave, the outer layer of the myocardium ends the activity earlier than the inner layer of the myocardium.

[0081] The function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can express waves whose widths in the vertical axis direction are different from those in the horizontal axis direction, such as the "third function", "sixth function" and "ninth function". In addition, the function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can express negative waves, such as the "twelfth function". That is, by approximating the T wave with the function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function, or by reversing the time axis and approximating the T wave with the function obtained by subtracting the fourth cumulative distribution function from the third cumulative distribution function, it is possible to express the respective activities of the inner and outer myocardial layers included in the T wave and the relationship between the activities of the inner and outer myocardial layers. This is also the same when the R wave is approximated by the function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0082] Thus, the signal analysis device 1 fits the waveform of the R wave or T wave of the electrocardiogram of the target heart by the difference or weighted difference of the two cumulative distribution functions. Then, the signal analysis device 1 obtains the parameters that determine the approximate time waveform determined by fitting as parameters showing the activity of the myocardium.

[0083] [Approximation by the difference or weighted difference of two cumulative distribution functions plus the value] The signal analysis device 1 may also take the waveform of the time interval of either the R wave or the T wave included in the waveform of one cycle of the electrocardiogram of the target heart obtained as the target time waveform, and take the time waveform obtained by adding a value (hereinafter referred to as a "level value") to the difference or weighted difference between the first cumulative distribution function as the first unimodal cumulative distribution function and the second cumulative distribution function as the second unimodal cumulative distribution function as the approximate time waveform. In this case, in addition to the parameters that determine the first unimodal distribution or the parameters that determine the first cumulative distribution function and the parameters that determine the second unimodal distribution or the parameters that determine the second cumulative distribution function when the target time waveform is approximated by the approximate time waveform, the level value is obtained as a parameter representing the characteristics of the target time waveform. Of course, in the case of approximation by weighted difference, the weight given to the first cumulative distribution function and the weight given to the second cumulative distribution function may also be obtained as parameters representing the characteristics of the target time waveform, and the ratio of the weight given to the first cumulative distribution function and the weight given to the second cumulative distribution function may also be obtained as a parameter representing the characteristics of the target time waveform.

[0084] In the case of using a weighted difference, for example, the signal analysis device 1 uses a combination (J×K×L×M×N types) of the cumulative distribution function parameters for each of the multiple (M) candidates for the first cumulative distribution function, the cumulative distribution function parameters for each of the multiple (N) candidates for the second cumulative distribution function, the multiple (K) candidates for weights assigned to the first cumulative distribution function, the multiple (L) candidates for weights assigned to the second cumulative distribution function, and the multiple (J) candidates for level values, to generate a time waveform obtained by adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function to the level value, i.e., a candidate time waveform, and determines the candidate time waveform that is closest to the object time waveform among the generated J×K×L×M×N types of candidate time waveforms as the approximate time waveform, and obtains the parameters for determining the candidate for the first cumulative distribution function, the parameters for determining the candidate for the second cumulative distribution function, the weight assigned to the first cumulative distribution function, the weight assigned to the second cumulative distribution function, and the level value used in the generation of the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0085] Alternatively, for example, the signal analysis device 1 obtains a time waveform obtained by adding a weighted difference between a candidate of a first cumulative distribution function that approximates the object time waveform and a candidate of a second cumulative distribution function plus a level value, i.e., a candidate time waveform, and updates the parameters of each cumulative distribution function in a direction in which the square error between the candidate time waveform and the object time waveform becomes smaller, and repeats at least one of the weights and level values ​​assigned to each cumulative distribution function until the square error becomes below a prescribed benchmark or is repeated a prescribed number of times, thereby determining the final candidate time waveform as an approximate time waveform, and obtaining the parameters for determining the candidate of the first cumulative distribution function, the parameters for determining the candidate of the second cumulative distribution function, the weight assigned to the first cumulative distribution function, the weight assigned to the second cumulative distribution function, and the level value used in the generation of the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0086] Alternatively, the level value may be determined before fitting. In this case, the signal analysis device 1 firstly calculates the starting end (equivalent to the R wave) of the target time waveform when the target time waveform is an R wave. Figure 4 The potential at the time T1 in the example is taken as the level value, and when the target time waveform is a T wave, the end end of the target time waveform (equivalent to Figure 4The potential at time T6 in the signal analysis device 1 is obtained as the level value. Furthermore, the signal analysis device 1 uses a combination (K×L×M×N types) of the cumulative distribution function parameters for each of the plurality of (M) candidates for the first cumulative distribution function, the cumulative distribution function parameters for each of the plurality of (N) candidates for the second cumulative distribution function, the plurality of (K) candidates for the weight given to the first cumulative distribution function, and the plurality of (L) candidates for the weight given to the second cumulative distribution function, respectively, to generate a candidate time waveform obtained by adding the level value to the weighted difference between the first cumulative distribution function and the second cumulative distribution function, that is, a candidate time waveform, which is the closest to the target time waveform among the generated K×L×M×N candidate time waveforms, as the approximate time waveform, and obtains the parameters for determining the candidate for the first cumulative distribution function, the parameters for determining the candidate for the second cumulative distribution function, the weight given to the first cumulative distribution function, the weight given to the second cumulative distribution function, and the level value determined in the initial processing as parameters representing the characteristics of the target time waveform.

[0087] Alternatively, for example, when the target time waveform is an R wave, the signal analysis device 1 firstly changes the starting end (equivalent to Figure 4 The potential at the time T1 in the example is taken as the level value, and when the target time waveform is a T wave, the end end of the target time waveform (equivalent to Figure 4 The potential at the time T6 in the signal analysis device 1 is obtained as the level value. Then, the signal analysis device 1 obtains the candidate time waveform obtained by adding the level value to the weighted difference between the candidate of the first cumulative distribution function and the candidate of the second cumulative distribution function that approximates the object time waveform, that is, the candidate time waveform, and updates at least one of the parameters for determining each cumulative distribution function and the weight assigned to each cumulative distribution function in the direction in which the square error between the candidate time waveform and the object time waveform becomes smaller, and repeats until the square error becomes less than a predetermined reference or repeats a predetermined number of times, thereby determining the candidate time waveform finally obtained as the approximate time waveform, and obtaining the parameters for determining the candidate of the first cumulative distribution function, the parameters for determining the candidate of the second cumulative distribution function, the weight assigned to the first cumulative distribution function, the weight assigned to the second cumulative distribution function, and the level value determined in the initial processing as parameters representing the characteristics of the object time waveform.

[0088] When the level value is β, the value obtained by adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function to the level value is expressed, for example, by the following formula (19). The function shown in the following formula (19) is a function obtained by subtracting the function of the second cumulative distribution function multiplied by the weight k2 from the function of the first cumulative distribution function multiplied by the weight k1 and adding the level value β.

[0089] [Formula 19]

[0090] When the object time waveform is approximated by the approximate time waveform of formula (19) obtained by adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function and the level value, at least the parameters that determine the first unimodal distribution or the parameters that determine the first cumulative distribution function, namely the mean value μ1 and the standard deviation σ1, the parameters that determine the second unimodal distribution or the parameters that determine the second cumulative distribution function, namely the mean value μ2 and the standard deviation σ2, and the level value β are obtained as parameters representing the characteristics of the object time waveform. In addition, the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function, or the ratio of the weight k1 of the first cumulative distribution function to the weight k2 of the second cumulative distribution function (k1 / k2 or k2 / k1) can also be obtained as parameters representing the characteristics of the object time waveform.

[0091] The function according to formula (19) is a function obtained by subtracting the function of the second cumulative distribution function multiplied by the weight k2 from the function of the first cumulative distribution function multiplied by the weight k1 and adding the level value β, and both the weight k1 and the weight k2 are positive values. However, when the subject's heart is in a specific state, the possibility that at least one of the weights k1 and k2 obtained by fitting is not a positive value cannot be denied. Therefore, although the signal analysis device 1 can fit both the weights k1 and k2 to positive values, it is not necessary to fit both the weights k1 and k2 to positive values.

[0092] The signal analysis device 1 can use the R wave included in the waveform of one cycle of the electrocardiogram of the target heart as the first target time waveform, and the T wave as the second target time waveform, and obtain the above-mentioned parameters representing the characteristics of the target time waveform for the first target time waveform and the second target time waveform, respectively.

[0093] For example, when the first target time waveform (i.e., R wave) is approximated by a function obtained by adding a level value to the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the potential at the starting end of the first target time waveform is taken as the level value β R , the first object time waveform is approximated by the approximate time waveform of equation (20), which is obtained from the first cumulative distribution function f shown in equation (7) a (x) multiplied by weight k a The function minus the second cumulative distribution function f shown in formula (8) b (x) multiplied by weight k b function and add the level value β R The obtained function will at least determine the parameter of the first cumulative distribution function, namely the mean value μ a and standard deviation σ a, determine the parameter of the second cumulative distribution function, i.e., the mean value μ b and standard deviation σ b , and the level value β R The parameters representing the characteristics of the first target time waveform (ie, R wave) are obtained. In addition, the weight k of the first cumulative distribution function may be a and the weight k of the second cumulative distribution function b , or the weight k of the first cumulative distribution function a and the weight k of the second cumulative distribution function b The ratio (k a / k b or k b / k a ) also obtains a parameter representing the characteristics of a parameter representing the characteristics of the first object time waveform (ie, the R wave).

[0094] [Formula 20]

[0095] According to formula (20), the function is the first cumulative distribution function multiplied by the weight k a minus the second cumulative distribution function multiplied by the weight k b function and add the level value β R The obtained function, weight k a and weight k b However, when the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. a and weight k b Therefore, although the signal analysis device 1 can set the weight k a and weight k b are all fitted as positive values, but there is no need to set the weight k a and weight k b All fit to positive values.

[0096] For example, when the second target inverse time waveform, which is a waveform obtained by inverting the time axis of the second target time waveform (i.e., T wave), is approximated by a function obtained by adding a level value to the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, the potential at the end of the second target time waveform is taken as the level value β T , the second object inverse time waveform is approximated by the approximate inverse time waveform of equation (21), which is obtained from the third cumulative distribution function f shown in equation (11) e (x') multiplied by the weight k e The function minus the fourth inverse cumulative distribution function f shown in formula (12) g (x') multiplied by the weight k gfunction and add the level value β T The obtained function will at least determine the parameter of the third cumulative distribution function, namely the mean value μ e and standard deviation σ e , determine the parameter of the fourth cumulative distribution function, that is, the mean value μ g and standard deviation σ g , and the level value β T The parameter representing the characteristic of the second target time waveform (ie, T wave) is obtained. In addition, the weight k of the third cumulative distribution function may be e and the weight k of the fourth cumulative distribution function g , or the weight k of the third cumulative distribution function e With the fourth cumulative distribution function weight k g The ratio (k e / k g or k g / k e ) also obtains a parameter representing the characteristics of a parameter representing the characteristics of a second object time waveform (ie, T wave).

[0097] [Formula 21]

[0098] According to formula (21), the function is obtained by multiplying the third cumulative distribution function by the weight k e minus the fourth cumulative distribution function multiplied by the weight k g function and add the level value β T The obtained function, weight k e and weight k g However, when the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. e and weight k g Therefore, although the signal analysis device 1 can set the weight k e and weight k g are all fitted as positive values, but there is no need to set the weight k e and weight k g All fit to positive values.

[0099] For example, when the second target time waveform (i.e., T wave) is approximated by a function obtained by adding a level value to a weighted difference between a function obtained by subtracting the third cumulative distribution function from 1 (i.e., the third inverse cumulative distribution function) and a function obtained by subtracting the fourth cumulative distribution function from 1 (i.e., the fourth inverse cumulative distribution function), the potential at the end of the second target time waveform is taken as the level value β. T, the second object time waveform is approximated by the approximate time waveform of formula (22), which is obtained from the third inverse cumulative distribution function f' c (x) multiplied by weight k c minus the fourth inverse cumulative distribution function f' d (x) multiplied by weight k c function and add the level value β T The obtained function will determine the parameter of the third cumulative distribution function, namely the mean value μ c and standard deviation σ c , determine the parameter of the fourth cumulative distribution function, that is, the mean value μ d and standard deviation σ d , and the level value β T The parameter representing the characteristic of the second target time waveform (ie, T wave) is obtained. In addition, the weight k of the third inverse cumulative distribution function may be c and the weight k of the fourth inverse cumulative distribution function d , or the weight k of the third inverse cumulative distribution function c with the fourth inverse cumulative distribution function with weight k d The ratio (k c / k d or k d / k c ) is also obtained as a parameter representing the characteristics of the second object time waveform (ie, T wave).

[0100] [Formula 22]

[0101] According to formula (22), the function is obtained by multiplying the third inverse cumulative distribution function by the weight k c minus the fourth inverse cumulative distribution function multiplied by the weight k d function and add the level value β T The obtained function, weight k c and weight k d However, when the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. c and weight k d Therefore, although the signal analysis device 1 can set the weight k c and weight k d are all fitted as positive values, but there is no need to set the weight k c and weight k d All fit to positive values.

[0102] Figure 6 is a schematic diagram showing a first cumulative distribution function f with respect to a first target time waveform (ie, R wave). a(x) multiplied by weight k a And add the level value β R The obtained function k a f a (x)+β R , the second cumulative distribution function f b (x) multiplied by weight k b And add the level value β R The obtained function k b f b (x)+β R , and the weighted difference between the first cumulative distribution function and the second cumulative distribution function plus the level value β R The approximate time waveform k a f a (x)-k b f b (x)+β R The single-point dash line is the first cumulative distribution function f a (x) multiplied by weight k a And add the level value β R The obtained function k a f a (x)+β R , the double-dotted line is the second cumulative distribution function f b (x) multiplied by weight k b And add the level value β R The obtained function k b f b (x)+β R , the dotted line is the approximate time waveform k a f a (x)-k b f b (x)+β R The approximate time waveform k a f a (x)-k b f b (x)+β R This is a waveform that approximates the first target time waveform (ie, the R wave).

[0103] Figure 7 is a schematic diagram showing a third inverse cumulative distribution function f′ with respect to a second object time waveform (ie, T wave). c (x) = 1-f c (x) multiplied by weight k c And add the level value β T The obtained function k c f' c (x)+β T , the fourth inverse cumulative distribution function f'd (x) = 1-f d (x) multiplied by weight k d And add the level value β T The obtained function k d f' d (x)+β T , and the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function plus the level value β T The approximate time waveform k c f' c (x)-k d f' d (x)+β T The single-point dashed line is the third inverse cumulative distribution function f' c (x) = 1-f c (x) multiplied by weight k c And add the level value β T The obtained function k c f' c (x)+β T , the double-dotted line is the fourth inverse cumulative distribution function f' d (x) = 1-f d (x) multiplied by weight k d And add the level value β T The obtained function k d f' d (x)+β T , the dotted line is the approximate time waveform k c f' c (x)-k d f' d (x)+β T The approximate time waveform k c f' c (x)-k d f' d (x)+β T This is a waveform that approximates the second target time waveform (ie, T wave).

[0104] In addition, the potential at the beginning of the R wave (more precisely, the QRS wave) is the level value β R It is a value indicating the magnitude of the DC component at the beginning of the R wave. When there is an abnormality in the coronary artery, the level value β R Sometimes it drops to the negative side. In addition, the potential at the end of the T wave, i.e., the level value β T It is a value indicating the magnitude of the DC component at the end of the T wave. When there is an abnormality in myocardial repolarization, the level value β T Sometimes it rises to the positive side.

[0105] [Approximate the difference between the target time waveform and the approximate time waveform] When the R wave or T wave in a special state is used as the target time waveform, a portion that cannot approximate the target time waveform (hereinafter referred to as a "residual portion") may remain in the above-mentioned approximate time waveform. For example, when the target heart has early repolarization or conduction disorder (auxiliary conduction pathway, etc.), the target time waveform (R wave) may include such Figure 8 The Δ wave is shown by the dotted line in the middle. The Δ wave portion cannot approximate the target time waveform in the above-mentioned approximate time waveform, and remains as a residual portion. Since the residual portion is also a time waveform caused by a certain activity of the heart, in the signal analysis device 1, the residual portion can be analyzed by assuming a cumulative Gaussian distribution, a function of a cumulative Gaussian distribution multiplied by a weight, a difference of cumulative Gaussian distributions, or a weighted difference of cumulative Gaussian distributions.

[0106] That is, the signal analysis device 1 can also use the cumulative distribution function (conveniently called the "fifth cumulative distribution function") of a certain unimodal distribution (conveniently called the "fifth unimodal distribution") or the fifth cumulative distribution function multiplied by a weight function to approximate the residual time waveform, that is, the difference between the object time waveform and the approximate time waveform, that is, the residual time waveform, to approximate the residual time waveform, and the parameters that determine the fifth unimodal distribution or the parameters that determine the fifth cumulative distribution function are also obtained as parameters representing the characteristics of the object time waveform.

[0107] Alternatively, the signal analysis device 1 may also approximate the residual time waveform by using the difference or weighted difference between the cumulative distribution function (conveniently referred to as the "fifth cumulative distribution function") of a certain unimodal distribution (conveniently referred to as the "fifth cumulative distribution function") and the cumulative distribution function (conveniently referred to as the "sixth cumulative distribution function") of a unimodal distribution different from the fifth unimodal distribution (conveniently referred to as the "sixth cumulative distribution function") for the difference between the object time waveform and the approximate time waveform, i.e., the residual time waveform, and the parameters of the fifth unimodal distribution or the parameters of the fifth cumulative distribution function, as well as the parameters of the sixth unimodal distribution or the parameters of the sixth cumulative distribution function, as parameters representing the characteristics of the object time waveform.

[0108] More specifically, when the fifth cumulative distribution function, which is the cumulative distribution function of the fifth unimodal distribution as shown in formula (23), is used to approximate the residual time waveform, the signal analysis device 1 approximates the residual time waveform with the approximate time waveform f5(x) of the fifth cumulative distribution function as shown in formula (24) for the difference between the object time waveform and the approximate time waveform, i.e., the residual time waveform. In addition to the above-mentioned parameters representing the characteristics of the object time waveform, the parameters of the fifth cumulative distribution function, i.e., the mean value μ5 and the standard deviation σ5, are also obtained as parameters representing the characteristics of the object time waveform.

[0109] [Formula 23]

[0110] [Formula 24]

[0111] When the residual time waveform is approximated by a function obtained by multiplying the fifth cumulative distribution function by a weight, the signal analysis device 1 approximates the residual time waveform by an approximate time waveform k5f5(x) of a function obtained by multiplying the fifth cumulative distribution function f5(x) shown in formula (24) by a weight k5, with respect to the difference between the object time waveform and the approximate time waveform. In addition to the above-mentioned parameters representing the characteristics of the object time waveform, the parameters that determine the fifth cumulative distribution function, namely, the mean value μ5 and the standard deviation σ5, are also obtained as parameters representing the characteristics of the object time waveform. The signal analysis device 1 can also obtain the weight k5 as a parameter representing the characteristics of the object time waveform.

[0112] In the case of approximating the residual time waveform by the difference between the fifth cumulative distribution function and the sixth cumulative distribution function which is the cumulative distribution function of the sixth unimodal distribution shown in formula (25), for example, the signal analysis device 1 approximates the residual time waveform which is the difference between the object time waveform and the approximate time waveform by the waveform obtained by subtracting the sixth cumulative distribution function shown in formula (26) from the fifth cumulative distribution function shown in formula (24), i.e., the approximate time waveform f5(x)-f6(x). In addition to the above-mentioned parameters representing the characteristics of the object time waveform, the parameters that determine the fifth cumulative distribution function, i.e., the mean value μ5 and the standard deviation σ5, and the parameters that determine the sixth cumulative distribution function, i.e., the mean value μ6 and the standard deviation σ6, are also obtained as parameters representing the characteristics of the object time waveform.

[0113] [Formula 25]

[0114] [Formula 26]

[0115] When the residual time waveform is approximated by the weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function, the signal analysis device 1 approximates the residual time waveform by using the waveform obtained by subtracting the function k6f6(x) of the sixth cumulative distribution function f6(x) multiplied by the weight k6 from the function k5f5(x) of the fifth cumulative distribution function f5(x) multiplied by the weight k5, i.e., the approximate time waveform k5f5(x)-k6f6(x), for the difference between the target time waveform and the approximate time waveform, and in addition to the above-mentioned parameters representing the characteristics of the target time waveform, the parameters that determine the fifth cumulative distribution function, i.e., the mean value μ5 and the standard deviation σ5, and the parameters that determine the sixth cumulative distribution function, i.e., the mean value μ6 and the standard deviation σ6, are also obtained as parameters representing the characteristics of the target time waveform. The signal analysis device 1 may also obtain the weight k5 and the weight k6, or the ratio of the weight k5 to the weight k6 (k5 / k6 or k6 / k5), as parameters representing the characteristics of the target time waveform.

[0116] The residual time waveform is approximated by the approximate time waveform obtained by multiplying the sixth cumulative distribution function, which is a function of the fifth cumulative distribution function multiplied by the weight k5, by the weight k6, and both the weight k5 and the weight k6 are positive values. However, when the subject's heart is in a specific state, the possibility that at least one of the weight k5 and the weight k6 obtained by fitting is not a positive value cannot be denied. Therefore, although the signal analysis device 1 can fit both the weight k5 and the weight k6 to positive values, it is not necessary to fit both the weight k5 and the weight k6 to positive values.

[0117] return Figure 1 The signal analysis device 1 includes a control unit 11 and executes a program. The control unit 11 includes a processor 91 such as a CPU and a memory 92 connected via a bus. The signal analysis device 1 functions as a device including the control unit 11, the input unit 12, the communication unit 13, the storage unit 14, and the output unit 15 by executing the program.

[0118] More specifically, the processor 91 reads out a program stored in the storage unit 14 and stores the read program in the memory 92. As the processor 91 executes the program stored in the memory 92, the signal analysis device 1 functions as a device including the control unit 11, the input unit 12, the communication unit 13, the storage unit 14, and the output unit 15.

[0119] The control unit 11 controls the operation of various functional units included in the signal analysis device 1. For example, the control unit 11 performs a myocardial activity information parameter acquisition process. For example, the control unit 11 controls the operation of the output unit 15 so that the output unit 15 outputs the acquisition result of the myocardial activity information parameter acquisition process. The control unit 11 records various information generated by, for example, performing the myocardial activity information parameter acquisition process in the storage unit 14.

[0120] The input unit 12 includes input devices such as a mouse, a keyboard, and a touch panel. The input unit 12 may be configured as an interface for connecting these input devices to the signal analysis device 1. The input unit 12 receives input of various information to the signal analysis device 1.

[0121] For example, for a plurality of candidates for each cumulative distribution function used for fitting, information indicating the shape of the distribution represented by each cumulative distribution function (hereinafter referred to as “distribution shape designation information”) is input to the input unit 12 .

[0122] In addition, the distribution shape specifying information may be stored in advance in the storage unit 14. In this case, the distribution shape specifying information stored in the storage unit 14 does not need to be input from the input unit 12. In the following, for simplicity of description, the signal analysis device 1 is described by taking the case where the storage unit 14 stores the distribution shape specifying information in advance as an example.

[0123] The communication unit 13 is configured to include a communication interface for connecting the signal analysis device 1 to an external device. The communication unit 13 communicates with the external device via wired or wireless communication. The external device is, for example, a device that is a source of the waveform of the electrocardiogram of the subject's heart. The device that is a source of the waveform of the electrocardiogram of the subject's heart is, for example, an electrocardiogram measuring device. When the external device is, for example, an electrocardiogram measuring device, the communication unit 13 obtains the waveform of the electrocardiogram from the electrocardiogram measuring device through communication. In addition, the waveform of the electrocardiogram may also be input to the input unit 12.

[0124] The storage unit 14 is configured using a non-transitory computer-readable storage medium device such as a magnetic hard disk device or a semiconductor storage device. The storage unit 14 stores various information related to the signal analysis device 1. The storage unit 14 stores, for example, information input via the input unit 12 or the communication unit 13. The storage unit 14 stores, for example, an electrocardiogram input via the input unit 12 or the communication unit 13. The storage unit 14 stores, for example, various information generated by executing a myocardial activity information parameter acquisition process.

[0125] The output unit 15 outputs various information. The output unit 15 is configured to include display devices such as a CRT (Cathode Ray Tube) display, a liquid crystal display, and an organic EL (Electro-Luminescence) display. The output unit 15 can also be configured as an interface for connecting these display devices to the signal analysis device 1. The output unit 15, for example, outputs information input to the input unit 12. The output unit 15 can also display an electrocardiogram input to the input unit 12 or the communication unit 13. The output unit 15 can also display the execution result of the myocardial activity information parameter acquisition process.

[0126] Fig. 91 is a diagram showing an example of the functional configuration of the control unit 11 in the first embodiment. The control unit 11 includes an electrocardiogram acquisition unit 110 , a fitting information acquisition unit 120 , an analysis unit 130 , and a recording unit 140 .

[0127] The electrocardiogram acquisition unit 110 acquires a waveform of one cycle from the waveform of the electrocardiogram of the target heart input to the input unit 12 or the communication unit 13, and outputs it to the analysis unit 130. The waveform of the electrocardiogram of the target heart is a waveform in which waveforms of multiple beats (multiple cycles) are arranged in a time series. Even in the case of an abnormality in the target heart, the waveforms of all beats included in the waveform of the electrocardiogram will not be special waveforms, and there are many cases where only the waveforms of any few beats included in the waveform of the electrocardiogram become characteristic waveforms. In the myocardial activity information parameter acquisition process, it is preferred to take this characteristic waveform as an object. Therefore, the electrocardiogram acquisition unit 110 acquires a waveform of one cycle as a characteristic waveform from the waveform of the electrocardiogram of the target heart. For example, the electrocardiogram acquisition unit 110 can use a known technique for determining similarity or specificity to acquire a waveform of one cycle as a characteristic waveform from the waveform of the electrocardiogram of the target heart. In addition, for example, the electrocardiogram acquisition unit 110 can also cause the output unit 15 to display the waveform of the electrocardiogram, cause the input unit 12 to accept the designation of the waveform of one cycle by a user such as a doctor, and obtain the waveform of one cycle corresponding to the designation accepted by the input unit 12 from the electrocardiogram waveform.

[0128] The electrocardiogram acquisition unit 110 outputs the waveform of one cycle as digital time series data sampled at a prescribed sampling frequency. The prescribed sampling frequency refers to the sampling frequency of the signal used in the processing of the fitting information acquisition unit 120, for example, 250 Hz. In the case where the waveform of the input electrocardiogram is sampled at a prescribed sampling frequency, the electrocardiogram acquisition unit 110 only needs to cut out the digital time series data of the waveform of one cycle from the digital time series data of the waveform of the input electrocardiogram and output it. In the case where the waveform of the input electrocardiogram is sampled at a sampling frequency different from the prescribed sampling frequency, the electrocardiogram acquisition unit 110 only needs to cut out the digital time series data of the waveform of one cycle from the digital time series data of the waveform of the input electrocardiogram and convert it to the prescribed sampling frequency before outputting it.

[0129] The electrocardiogram acquisition unit 110 also determines the time interval of the R wave and the time interval of the T wave included in the waveform of one cycle of the electrocardiogram, and also obtains information for determining the time interval of the R wave and information for determining the time interval of the T wave and outputs them to the analysis unit 130. For example, the electrocardiogram acquisition unit 110 only needs to determine the start end of the R wave, the end end of the R wave, the start end of the T wave, and the end end of the T wave by a known technique, and obtain the sample number corresponding to the determined start end of the R wave, the end end of the R wave, the start end of the T wave, and the end end of the T wave, respectively, and the relative time from the start end of the waveform, as information for determining the time interval of the R wave and information for determining the time interval of the T wave. In addition, the R wave in this specification refers to the QRS wave to be precise. Although there are various interpretations about which point in the waveform the start end of the R wave (i.e., the start end of the QRS wave) is, it is true that the electrocardiogram acquisition unit 110 only needs to use the point determined by any known technique as the start end of the R wave.

[0130] In addition, in the waveform of the electrocardiogram of the target heart, a characteristic waveform that changes with the passage of time sometimes appears. Therefore, the electrocardiogram acquisition unit 110 may also acquire waveforms of multiple cycles from the waveform of the electrocardiogram of the target heart as waveforms of the target for myocardial activity information parameter acquisition processing. That is, the electrocardiogram acquisition unit 110 may also acquire a predetermined long-term time series waveform (trend graph) from the waveform of the electrocardiogram of the target heart, and output information about the waveform of each cycle included in the acquired waveform, the waveform and the time interval of the R wave included in the waveform, and the time interval of the T wave included in the waveform to the analysis unit 130.

[0131] The fitting information acquisition unit 120 acquires the distribution shape specifying information. If the distribution shape specifying information is stored in the storage unit 14 , the fitting information acquisition unit 120 reads the distribution shape specifying information from the storage unit 14 .

[0132] The analyzing unit 130 includes a fitting unit 131 and a myocardial activity information parameter acquiring unit 132 .

[0133] The fitting unit 131 fits the target time waveform, which is the waveform of the time section of at least one of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram acquired by the electrocardiogram acquisition unit 110 , using the candidate of the cumulative distribution function indicated by the distribution shape designation information.

[0134] The myocardial activity information parameter acquisition unit 132 acquires parameters representing the characteristics of the target time waveform based on the fitting result performed by the fitting unit 131. For example, the myocardial activity information parameter acquisition unit 132 acquires, as parameters representing the characteristics of the target time waveform, parameters that determine the first unimodal distribution or parameters that determine the first cumulative distribution function, and parameters that determine the second unimodal distribution or parameters that determine the second cumulative distribution function, when the target time waveform is approximated by the difference or weighted difference between the first cumulative distribution function that is the first unimodal distribution cumulative distribution function and the second cumulative distribution function that is the second unimodal distribution cumulative distribution function. The parameters representing the characteristics of the target time waveform acquired by the myocardial activity information parameter acquisition unit 132 are an example of myocardial activity parameters.

[0135] In this way, the analyzing unit 130 acquires the myocardial activity parameter based on the target time waveform, which is the waveform of the time section of at least one of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram of the target heart, and the distribution candidate information.

[0136] The storage unit 14 records various information generated by the processing executed by the control unit 11 in the storage unit 14 .

[0137] Fig.10 1 is a flowchart showing an example of a processing flow performed by the signal analysis device 1 in the first embodiment. The electrocardiogram acquisition unit 110 acquires the waveform of one cycle of the electrocardiogram of the target heart, information for determining the time interval of the R wave, and information for determining the time interval of the T wave via the input unit 12 or the communication unit 13 (step S101). Next, the fitting information acquisition unit 120 acquires the distribution shape designation information (step S102). Next, the fitting unit 131 uses the candidate of the cumulative distribution function indicated by the distribution shape designation information to fit the waveform of the time interval of at least one of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram acquired in step S101, that is, the target time waveform (step S103). Next, the myocardial activity information parameter acquisition unit 132 acquires the myocardial activity parameter based on the fitting result (step S104). The acquired myocardial activity parameter is output to the output unit 15 (step S105).

[0138] In step S105, a graph of the fitting result for each target time waveform may be displayed. In addition, the process of step S102 may be performed before the process of step S103, or before the execution of step S101. The processes of step S103 and step S104 are examples of the processes executed by the analysis unit 130.

[0139] Fig.11 1 is a first diagram showing an example of the analysis result of the signal analysis device 1 in the first embodiment. More specifically, Fig.11This is an example of the analysis result of the waveform of the electrocardiogram of the subject's heart that is operating normally by the signal analysis device 1 . Fig.11 The horizontal axis represents time, and the vertical axis represents potential. The unit of the vertical axis is an arbitrary unit.

[0140] Fig.11 It shows that using k a / α1 replaces weight k a In order to make the first fitting result at time T3 and the third fitting result at time T4 the same value, k b / α2 replaces the weight k b An example of the results of the first and second fittings on the depolarized R wave of the electrocardiogram of the object heart with normal operation and the results of the fourth and third fittings on the repolarized T wave of the electrocardiogram of the object heart with normal operation is displayed so that the second fitting result at time T3 and the fourth fitting result at time T4 have the same value. Hereinafter, the result of connecting the first fitting result and the third fitting result with changed weights via a straight line is referred to as the myocardial inner layer activity approximation function, and the result of connecting the second fitting result and the fourth fitting result with changed weights via a straight line is referred to as the myocardial outer layer activity approximation function.

[0141] Fig.11 Corresponding to the fact that in the depolarization of a normal heart, the activity of the inner myocardium layer (i.e., the activity of the ion channels) starts earlier than that of the outer myocardium layer and progresses rapidly, the activity of the outer myocardium layer starts slightly later than the timing of the start of the activity of the ion channels of the inner myocardium layer, and the difference between the timing of the start of the activity of the ion channels of the inner myocardium layer and the timing of the start of the activity of the outer myocardium layer becomes a positive and sharp R wave. That is, the mean value and standard deviation of the inner myocardium layer activity approximate function of the first fitting result of the inner myocardium layer activity approximate function and the mean value and standard deviation of the second fitting result of the outer myocardium layer activity approximate function are parameters indicating the timing of the activity of the ion channels and the progress of the activity in the depolarization of a normal heart.

[0142] in addition, Fig.11Corresponding to the fact that in the repolarization phase of a normal heart, the outer myocardium starts to inactivate the activity of ion channels earlier than the inner myocardium, the inactivation of the inner myocardium starts later than the inactivation of the outer myocardium, the inactivation of the outer myocardium and the inactivation of the inner myocardium both progress slowly, and the difference between the inactivation of the inner myocardium and the inactivation of the outer myocardium becomes a positive and slow T wave. That is, the average value and standard deviation of the fourth fitting result of the outer myocardial layer activity approximate function and the average value and standard deviation of the third fitting result of the inner myocardial layer activity approximate function are parameters indicating the timing of the inactivation of the activity of ion channels in the repolarization phase of a normal heart and the progress of the inactivation. As described above, the inner myocardial layer activity approximate function and the outer myocardial layer activity approximate function obtained by the first to fourth fittings correspond to the timing and progress of the collective activation of ion channels in the depolarization phase and the collective inactivation of ion channels in the repolarization phase, and the average value and standard deviation of each fitting result of each function are parameters indicating myocardial activity.

[0143] Fig.11 The shapes of the myocardial inner layer activity approximation function and the myocardial outer layer activity approximation function are roughly consistent with the measurement results of the electromotive force of the myocardium directly measured by inserting a catheter electrode into the myocardium of a subject heart with normal operation. This shows that according to the signal analysis device 1, it is possible to obtain information representing myocardial activity only from the electrocardiogram without inserting a catheter electrode.

[0144] Fig.12 2 is a second diagram showing an example of the analysis result of the signal analysis device 1 in the first embodiment. More specifically, Fig.12 This is an example of the analysis result of the waveform of the electrocardiogram of the subject's heart that is operating normally by the signal analysis device 1 .

[0145] Fig.12 The three results of graph G5, graph G6 and result G7 are shown. Fig.12 In the figure, the "inner layer side cumulative distribution function" shows the fitting result of the function representing the collective channel activity timing distribution of ion channels present in the inner layer of the myocardium. Fig.12 In the figure, the "outer layer side cumulative distribution function" shows the fitting result of the function representing the collective channel activity timing distribution of ion channels present in the outer layer of the myocardium. Fig.12 In the figure, the "electrical potential of the body surface" is a function representing the time change of the electric potential of the body surface, which is the waveform of the electrocardiogram. Fig.12 The horizontal axis represents time, and the vertical axis represents potential. The units of the horizontal and vertical axes are both arbitrary units. Figure 12 to Figure 14 The time length shown by the interval of 1 scale mark on each horizontal axis is the same. Figure 12 to Figure 14 The vertical axis is 1, which represents the maximum value of the cumulative Gaussian distribution.

[0146] Graph G5 shows the entire waveform of the electrocardiogram generated in one beat. Graph G6 shows an enlarged view of the T wave region as a part of graph G5. The T wave region is Fig.12 The result G7 shows the statistics of two Gaussian distributions, namely, the Gaussian distribution as the cumulative source of the inner-side cumulative distribution function and the Gaussian distribution as the cumulative source of the outer-side cumulative distribution function. Each value of the result G7 represents the statistics of the two Gaussian distributions. The statistics of the two Gaussian distributions are specifically the mean value and standard deviation of the Gaussian distributions as the cumulative source of the inner-side cumulative distribution function and the outer-side cumulative distribution function.

[0147] Fig.13 3 is a third diagram showing an example of the analysis result of the signal analysis device 1 in the first embodiment. More specifically, Fig.13 This is an example of the analysis result of the waveform of the electrocardiogram of the target heart whose behavior is T prolongation type 3 by the signal analysis device 1 .

[0148] Fig.13 The three results of graph G8, graph G9 and result G10 are shown. Fig.13 In the figure, the "inner layer side cumulative distribution function" shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the inner layer of the myocardium. Fig.13 In the figure, the “outer layer cumulative distribution function” shows the fitting result of the function of the collective channel activity timing distribution of the channels existing in the outer layer of the myocardium. Fig.13 In the figure, the "electrical potential of the body surface" is a function representing the time change of the electric potential of the body surface, which is the waveform of the electrocardiogram. Fig.13 The horizontal axis represents time, and the vertical axis represents potential. The units of the horizontal axis and the vertical axis are both arbitrary units.

[0149] Graph G8 shows the entire waveform of the electrocardiogram generated in one beat. Graph G9 shows an enlarged view of the T wave region as a part of graph G8. The T wave region is Fig.13 The area shown in FIG. 1 is area A2. Result G10 shows the statistics of two Gaussian distributions, namely, the Gaussian distribution as the cumulative source of the inner-side cumulative distribution function and the Gaussian distribution as the cumulative source of the outer-side cumulative distribution function. Each value of result G10 represents the statistics of the two Gaussian distributions, namely, the mean value and standard deviation of the Gaussian distributions as the cumulative source of the inner-side cumulative distribution function and the outer-side cumulative distribution function.

[0150] Fig.13The shapes of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function are substantially consistent with the result of directly measuring the change of the electromotive force generated by the pulsation of the outer layer of the myocardium of the target heart whose motion is T prolongation type 3 by inserting the catheter electrode. This shows that according to the signal analysis device 1, it is possible to obtain information indicating the myocardial activity only from the electrocardiogram without inserting the catheter electrode.

[0151] Fig.14 4 is a fourth diagram showing an example of the analysis result of the signal analysis device 1 in the first embodiment. More specifically, Fig.14 This is an example of the analysis result of the waveform of the electrocardiogram of the target heart whose behavior is QT prolongation type 1 by the signal analysis device 1 .

[0152] Fig.14 The three results of graph G11, graph G12 and result G13 are shown. Fig.14 In the figure, the "inner layer side cumulative distribution function" shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the inner layer of the myocardium. Fig.14 In the figure, the “outer layer side cumulative distribution function” shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the outer layer of the myocardium. Fig.14 In the figure, the "electrical potential of the body surface" is a function representing the time change of the electric potential of the body surface, which is the waveform of the electrocardiogram. Fig.14 The horizontal axis represents time, and the vertical axis represents potential. The units of the horizontal and vertical axes are both arbitrary units.

[0153] Graph G11 shows the entire waveform of the electrocardiogram generated in one beat. Graph G12 shows an enlarged view of the T wave region as a part of graph G11. The T wave region is Fig.13 The result G13 shows the statistics of two Gaussian distributions, namely, the Gaussian distribution as the cumulative source of the inner-side cumulative distribution function and the Gaussian distribution as the cumulative source of the outer-side cumulative distribution function. Each value of the result G13 represents the statistics of the two Gaussian distributions. The statistics of the two Gaussian distributions are specifically the mean value and the standard deviation of the Gaussian distributions as the cumulative source of the inner-side cumulative distribution function and the outer-side cumulative distribution function.

[0154] Fig.14 The shapes of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function are roughly consistent with the result of directly measuring the change of the electromotive force generated by the pulsation of the outer layer of the myocardium of the target heart whose action is QT prolongation type 3 by inserting the catheter electrode. This shows that according to the signal analysis device 1, it is possible to obtain information representing the myocardial activity only from the electrocardiogram without inserting the catheter electrode.

[0155] in addition, Fig.14This is also an example of the estimation result of the channel current characteristic related to sudden death by the signal analysis device 1 .

[0156] use Figure 15 to Figure 17 , it is explained that even for an electrocardiogram of ventricular extrasystole, the signal analysis device 1 can obtain information indicating myocardial activity only from the electrocardiogram. Figure 15 to Figure 17 The horizontal axis represents time (seconds), and the vertical axis represents potential (mV).

[0157] Fig.15 This is a first explanatory diagram for explaining an example of an electrocardiogram of a ventricular extrasystole analyzed by the signal analysis device 1 according to the first embodiment. Fig.16 This is a second explanatory diagram showing an example of an electrocardiogram of a ventricular extrasystole analyzed by the signal analysis device 1 according to the first embodiment. Fig.17 This is a third explanatory diagram showing an example of an electrocardiogram of a ventricular extrasystole analyzed by the signal analysis device 1 according to the first embodiment.

[0158] More specifically, Fig.15 Shows the cardiac potential on the body surface. That is, Fig.15 A normal heartbeat and two consecutive ventricular extrasystoles are shown in the electrocardiogram. More specifically, Fig.16 The myocardial inner layer activity approximation function including the inner layer cumulative distribution function of the depolarization of the ventricular extrasystole and the inner layer cumulative distribution function of the repolarization phase analyzed by the signal analyzing device 1 and the myocardial outer layer activity approximation function including the outer layer cumulative distribution function of the depolarization of the ventricular extrasystole and the outer layer cumulative distribution function of the repolarization phase analyzed by the signal analyzing device 1 are shown. More specifically, Fig.17 An example of a waveform of a ventricular extrasystole in an electrocardiogram actually measured is shown.

[0159] Fig.16 The inner cumulative distribution function of depolarization precedes the outer cumulative distribution function, and the standard deviation of both is larger than that of a normal heartbeat, indicating that the spread of excitement is slow. This analysis result is consistent with the waveform characteristics of the R wave with a wide downward swing.

[0160] Fig.16 The inner layer cumulative distribution function starts to deactivate earlier than the outer layer cumulative distribution function in the repolarization phase, and the order of deactivation of the inner layer and the outer layer is shown as the magnitude relationship of the average values ​​of the two cumulative distribution functions in the repolarization phase. Fig.16 The function obtained by subtracting the outer cumulative distribution function from the inner cumulative distribution function is consistent with the characteristics of the large negative T wave in the repolarization phase, such as Fig.17 As shown, the waveform obtained by subtracting the outer layer side cumulative distribution function from the inner layer side cumulative distribution function is substantially consistent with the waveform of the ventricular extrasystole in the actually measured electrocardiogram.

[0161] in addition, Figure 15 to Figure 17 The results show that the analysis by the signal analysis device 1 corresponds to an example of a giant wave or negative potential generated by altered conduction, early repolarization or delayed repolarization of the myocardial excitation. Figure 15 to Figure 17 In the depolarized phase, the mean μ of the cumulative distribution function of the inner layer is -1, and the standard deviation σ is 0.32. Figure 15 to Figure 17 In the figure, the mean μ of the cumulative distribution function of the outer layer of depolarization is -0.8 and the standard deviation σ is 0.21. Figure 15 to Figure 17 In the case of the repolarization phase, the mean μ of the cumulative distribution function of the inner layer is 1, and the standard deviation σ is 1. Figure 15 to Figure 17 The average value μ of the cumulative distribution function of the outer layer side of the repolarization phase is 2.99, and the standard deviation σ is 0.7.

[0162] use Figures 18 to 20 , it is explained that even with respect to the electrocardiogram of the subject's heart in the depolarization phase of Buddah syndrome type 1, information indicating myocardial activity can be obtained only from the electrocardiogram by the signal analysis device 1. Figures 18 to 20 The vertical axis represents the potential in millivolts.

[0163] Fig.18 This is a first explanatory diagram for explaining an example of an electrocardiogram of a target heart in a depolarization phase of Buddag syndrome type 1 analyzed by the signal analysis device 1 according to the first embodiment. Fig.19 This is a second explanatory diagram showing an example of an electrocardiogram of a target heart in the depolarization phase of Buddag syndrome type 1 analyzed by the signal analysis device 1 according to the first embodiment. Fig. 20 This is a third explanatory diagram showing an example of an electrocardiogram of a target heart in the depolarization phase of Buddag syndrome type 1 analyzed by the signal analysis device 1 according to the first embodiment.

[0164] More specifically, Fig.18 An electrocardiogram showing the second induction of the chest in Buddah syndrome. Fig.18 In the figure, the inner frame W1 shows the depolarization phase, and the inner frame W2 shows the partition of the repolarization phase. Fig.19 The same is true. That is, in Fig.19 In the figure, the inner frame W1 represents the depolarization phase, and the inner frame W2 represents the repolarization phase.

[0165] More specifically, Fig.19 The inner layer side cumulative distribution function and the outer layer side cumulative distribution function in the depolarization and repolarization phases analyzed by the signal analysis device 1 are shown. Fig.19 In the example shown, the repolarization phase of the inner layer side cumulative distribution function starts after the depolarization phase, showing the characteristics of early repolarization. Fig.19In the example of , the potential amplitude of the cumulative distribution function on the outer layer side is different between the depolarization phase and the repolarization phase. Fig.19 The gap and anisotropy between the depolarization phase and the repolarization phase of the outer layer cumulative distribution function are shown. In this way, the signal analysis device 1 can express the anisotropy of early repolarization, depolarization, and repolarization, which are the characteristics of the waveform shown in the electrocardiogram of the subject heart of Buddah syndrome, by the average value and standard deviation of the depolarization phase and the repolarization phase of the inner layer cumulative distribution function and the outer layer cumulative distribution function, and the ratio of the weight given to the outer layer cumulative distribution function to the weight given to the inner layer cumulative distribution function (inner-outer ratio).

[0166] Fig. 20 is the comparison between the analytical result and the measured value. More specifically, Fig. 20 Show Fig.19 The difference between the inner and outer cumulative distribution functions in the depolarization and repolarization phases. Fig. 20 The actual measured values ​​of the electrocardiogram are also shown. The analysis results are basically consistent with the actual measured values ​​except for the last one. The last one refers to the potential at the latest time.

[0167] In addition, Figures 18 to 20 In the depolarized phase, the mean μ of the cumulative distribution function of the inner layer is 15, and the standard deviation σ is 0.15. Figures 18 to 20 In the figure, the average value μ of the cumulative distribution function of the outer layer of the depolarized phase is 14, and the standard deviation σ is 0.25. Figures 18 to 20 In the depolarized phase, the ratio of the inner and outer layers is 0.45. Figures 18 to 20 In the figure, the mean μ of the cumulative distribution function of the inner layer of the repolarization phase is 25, and the standard deviation σ is 0.25. Figures 18 to 20 The mean μ of the cumulative distribution function of the outer layer side of the repolarization phase is 20, and the standard deviation σ is 0.5.

[0168] Figures 21 to 59 A library showing the use of publicly available ECG data<https: / / physionet.org / about / database / > The result of analysis by the signal analysis device 1. Figures 21 to 59 The inner layer cumulative distribution function, the outer layer cumulative distribution function, and the fitting result obtained as a result of analyzing the cardiac potential by the signal analyzing device 1 are shown.

[0169] Figures 21 to 59 Each of the figures shows an example of an electrocardiogram analyzed by the signal analysis device 1 of the first embodiment. The determined points recorded in each figure represent the Q point, R point, S point, T start point, and T end point of the cardiac potential in order from the left side of the figure. The determined points are determined by the inflection point detection and peak detection algorithms. Figure 21 to Figure 59The graphs show the inner cumulative distribution function and the outer cumulative distribution function in each interval obtained for the interval of the depolarization phase (QRS wave) and the interval of the repolarization phase (T wave). Figure 21 to Figure 59 It is shown that the signal analysis device 1 can show substantially the same shape for various QRS waves and T waves by adjusting the mean values ​​and standard deviations of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function. Figure 21 to Figure 59 The figure below shows the original waveform of the electrocardiogram and the fitting result. In addition, Figure 21 to Figure 59 The results are the result of 300Hz sampling. Therefore, Figure 21 to Figure 59 The origin of the horizontal axis of each graph represents 0 seconds, and a value of 1 represents 3.33 milliseconds.

[0170] The signal analysis device 1 configured in this way takes as the target time waveform the waveform of an arbitrary time interval of the R wave and the T wave included in the waveform representing one cycle of the cardiac cycle of the target heart, and when the target time waveform is approximated by the difference or weighted difference between the first cumulative distribution function as the first unimodal distribution cumulative distribution function and the second cumulative distribution function as the second unimodal distribution cumulative distribution function, the parameters that determine the first unimodal distribution or the parameters that determine the first cumulative distribution function and the parameters that determine the second unimodal distribution or the parameters that determine the second cumulative distribution function are obtained as parameters that represent the characteristics of the target time waveform. The first cumulative distribution function is information representing the activity of the inner myocardial layer of the target heart, and the second cumulative distribution function is information representing the activity of the outer myocardial layer of the target heart. Therefore, the parameters representing the shape of the first cumulative distribution function are parameters representing the activity of the inner myocardial layer of the target heart (inner myocardial layer parameters), and the parameters representing the shape of the second cumulative distribution function are parameters representing the activity of the outer myocardial layer of the target heart (outer myocardial layer parameters). The conventional analysis of the electrocardiogram waveform cannot obtain information such as these parameters that clearly show the activity characteristics of the inner myocardium layer of the target heart and information that clearly show the activity characteristics of the outer myocardium layer of the target heart. Therefore, according to the signal analysis device 1, it is possible to obtain useful information for understanding the state of the heart from the electrocardiogram waveform.

[0171] (Get only some of the parameters) When only the activity characteristics of the inner myocardium layer of the target heart are to be understood, only the inner myocardium layer parameters may be obtained by the signal analysis device 1. When only the activity characteristics of the outer myocardium layer of the target heart are to be understood, only the outer myocardium layer parameters may be obtained by the signal analysis device 1. In addition, only a part of the parameters representing the shape of the cumulative distribution function may be obtained by the signal analysis device 1 as the inner myocardium layer parameters or the outer myocardium layer parameters.

[0172] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 obtains at least any one of at least a portion of the parameters of the first unimodal distribution, at least a portion of the parameters of the first cumulative distribution function, at least a portion of the parameters of the second unimodal distribution, and at least a portion of the parameters of the second cumulative distribution function as parameters showing the activity of the myocardium of the target heart, namely, myocardial activity parameters, when approximating the first target time waveform as the waveform of the time interval of the R wave of the target heart by the time waveform obtained by the difference or weighted difference between the first cumulative distribution function as the cumulative distribution function of the first unimodal distribution and the second cumulative distribution function as the cumulative distribution function of the second unimodal distribution, or the time waveform obtained by adding the difference or weighted difference between the first cumulative distribution function as the cumulative distribution function of the first unimodal distribution and the second cumulative distribution function as the cumulative distribution function of the second unimodal distribution plus a level value.

[0173] For example, when both the first unimodal distribution and the second unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the mean value of the first unimodal distribution, the standard deviation or variance of the first unimodal distribution, the mean value of the second unimodal distribution, and the standard deviation or variance of the second unimodal distribution as the myocardial activity parameter. In addition, the mean value of the Gaussian distribution is the time when the frequency value in the unimodal distribution becomes the maximum value, and the time when the slope of the cumulative distribution function of the unimodal distribution becomes the maximum value. Therefore, for example, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the time corresponding to the maximum value of the first unimodal distribution, the time corresponding to the maximum slope of the first cumulative distribution function, the time corresponding to the maximum value of the second unimodal distribution, and the time corresponding to the maximum slope of the second cumulative distribution function as the myocardial activity parameter, regardless of whether the first unimodal distribution and the second unimodal distribution are Gaussian distributions.

[0174] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 obtains at least any one of at least a portion of the parameters of the third unimodal distribution, at least a portion of the parameters of the third cumulative distribution function, at least a portion of the parameters of the fourth unimodal distribution, and at least a portion of the parameters of the fourth cumulative distribution function as a waveform obtained by adding a level value to the difference or weighted difference between the third cumulative distribution function as the third unimodal distribution and the fourth cumulative distribution function as the fourth unimodal distribution as a waveform obtained by adding a level value to the difference or weighted difference between the third cumulative distribution function as the third unimodal distribution and the fourth cumulative distribution function as the fourth unimodal distribution as a waveform obtained by inverting the time axis of the second object time waveform of the time interval of the T wave of the object heart, namely, the second object inverse time waveform, as a parameter showing the activity of the myocardium of the object heart, namely, the myocardial activity parameter.

[0175] For example, when both the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the mean value of the third unimodal distribution, the standard deviation or variance of the third unimodal distribution, and the mean value of the fourth unimodal distribution, and the standard deviation or variance of the fourth unimodal distribution as the myocardial activity parameter. In addition, for example, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the time corresponding to the maximum value of the third unimodal distribution, the time corresponding to the maximum slope of the third cumulative distribution function, the time corresponding to the maximum value of the fourth unimodal distribution, and the time corresponding to the maximum slope of the fourth cumulative distribution function as the myocardial activity parameter, regardless of whether the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions. In addition, when the myocardial activity information parameter acquisition unit 132 acquires the time as the myocardial activity parameter, even when the waveform after the time axis is reversed is approximated, the time (the value of x in the above example) is acquired instead of the information indicating the time in the reverse direction (the value of x' in the above example).

[0176] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 uses the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, uses the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, subtracts the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and subtracts the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function. When the second target time waveform that is the waveform of the time interval of the T wave of the target heart is approximated by the waveform obtained by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, that is, the second approximate time waveform, or the waveform obtained by adding the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, that is, the level value, at least any one of at least a part of the parameters that determine the third unimodal distribution, at least a part of the parameters that determine the third cumulative distribution function, at least a part of the parameters that determine the fourth unimodal distribution, and at least a part of the parameters that determine the fourth cumulative distribution function is acquired as a parameter representing the activity of the myocardium of the target heart, that is, the myocardial activity parameter.

[0177] For example, when both the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the mean value of the third unimodal distribution, the standard deviation or variance of the third unimodal distribution, and the mean value of the fourth unimodal distribution, and the standard deviation or variance of the fourth unimodal distribution as the myocardial activity parameter. In addition, for example, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the time corresponding to the maximum value of the third unimodal distribution, the time corresponding to the maximum slope of the third cumulative distribution function, the time corresponding to the maximum value of the fourth unimodal distribution, and the time corresponding to the maximum slope of the fourth cumulative distribution function as the myocardial activity parameter, regardless of whether the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions.

[0178] Similarly, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 also acquires at least any one of at least a part of the parameters that determine the fifth unimodal distribution and at least a part of the parameters that determine the fifth cumulative distribution function as a parameter showing the activity of the myocardium of the target heart, i.e., a myocardial activity parameter, when approximating a residual time waveform that is a time waveform of a difference between a first object time waveform and a first approximate time waveform, or a residual time waveform that is a time waveform of a difference between a second object time waveform and a second approximate time waveform, or a residual time waveform that is a time waveform of a difference between a second object time waveform and a waveform obtained by inverting the time axis of the second approximate inverse time waveform, i.e., a second approximate time waveform.

[0179] For example, when the fifth unimodal distribution is a Gaussian distribution, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the mean value of the fifth unimodal distribution, the standard deviation, or the variance of the fifth unimodal distribution as the myocardial activity parameter. In addition, for example, the myocardial activity information parameter acquisition unit 132 only needs to acquire any one of the time corresponding to the maximum value of the fifth unimodal distribution and the time corresponding to the maximum slope of the fifth cumulative distribution function as the myocardial activity parameter regardless of whether the fifth unimodal distribution is a Gaussian distribution.

[0180] In addition, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 also obtains at least any one of at least a portion of the parameters that determine the fifth unimodal distribution, at least a portion of the parameters that determine the fifth cumulative distribution function, at least a portion of the parameters that determine the sixth unimodal distribution, and at least a portion of the parameters that determine the sixth cumulative distribution function as a parameter showing the activity of the myocardium of the object heart, namely, a myocardial activity parameter, when the residual time waveform is approximated by the time waveform obtained by the difference or weighted difference between the fifth cumulative distribution function as the cumulative distribution function of the fifth unimodal distribution and the sixth cumulative distribution function as the cumulative distribution function of the sixth unimodal distribution.

[0181] For example, when both the fifth unimodal distribution and the sixth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the average value of the fifth unimodal distribution, the standard deviation or variance of the fifth unimodal distribution, and the average value of the sixth unimodal distribution, and the standard deviation or variance of the sixth unimodal distribution as the myocardial activity parameter. In addition, for example, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least one of the time corresponding to the maximum value of the fifth unimodal distribution, the time corresponding to the maximum slope of the fifth cumulative distribution function, the time corresponding to the maximum value of the sixth unimodal distribution, and the time corresponding to the maximum slope of the sixth cumulative distribution function as the myocardial activity parameter, regardless of whether the fifth unimodal distribution and the sixth unimodal distribution are Gaussian distributions.

[0182] [Acquisition of Myocardial Activity Parameters by Calculation of Parameters Obtained by Fitting] The characteristics of the activity of the myocardium of the object heart are not only manifested in the above-mentioned parameters obtained by fitting, but also clearly manifested in the values ​​obtained by the operation of the parameters obtained by fitting. Therefore, the values ​​obtained by the operation of the parameters obtained by fitting can also be obtained as myocardial activity parameters by the signal analysis device 1. The parameters obtained by fitting refer to at least one of the parameters for determining the unimodal distribution or the parameters for determining the cumulative distribution function, the weights, and the level values. The parameters for determining the unimodal distribution or the parameters for determining the cumulative distribution function are at least one of the mean value and the standard deviation (or variance) when the unimodal distribution is a Gaussian distribution. Regardless of whether the unimodal distribution is a Gaussian distribution, the moment corresponding to the maximum value of the unimodal distribution is an example of determining the parameters of the unimodal distribution, and the moment corresponding to the maximum slope of the cumulative distribution function of the unimodal distribution is an example of determining the parameters of the cumulative distribution function.

[0183] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can obtain parameters showing the activity of the myocardium of the object heart (parameters different from the above-mentioned parameters) by calculating the myocardial activity parameters obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform showing one cycle of the cardiac cycle of the object heart (i.e., parameters showing the activity of the myocardium of the object heart in the time interval of the R wave) and the myocardial activity parameters obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform showing one cycle (i.e., parameters showing the activity of the myocardium of the object heart in the time interval of the T wave).

[0184] In addition, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may obtain parameters showing the activity of the myocardium of the object heart (parameters different from the above-mentioned parameters) by calculating the parameters showing the activity of the inner layer of the myocardium of the object heart obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform showing one cycle of the cardiac cycle of the object heart and the parameters showing the activity of the outer layer of the myocardium of the object heart obtained by the fitting.

[0185] In addition, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may obtain parameters showing the activity of the myocardium of the object heart (parameters different from the above-mentioned parameters) by calculating the parameters showing the activity of the inner layer of the myocardium of the object heart obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform showing one cycle of the cardiac cycle of the object heart and the parameters showing the activity of the outer layer of the myocardium of the object heart obtained by the fitting.

[0186] Hereinafter, an example will be described in which, when all the first to fourth unimodal distributions are Gaussian distributions, a value obtained by calculating the average values ​​obtained by fitting is used as a myocardial activity parameter. When the first to fourth unimodal distributions are not Gaussian distributions, the "average value" in the following example may be replaced with "the time when the value is the largest in the unimodal distribution", i.e., "the time corresponding to the maximum value of the unimodal distribution", "the time when the slope is the largest in the cumulative distribution function", i.e., "the time corresponding to the maximum slope of the cumulative distribution function", etc.

[0187] (1) Parameters indicating the time from depolarization to repolarization It is known that when the time from depolarization of the inner or outer layer of the myocardium to repolarization of the inner or outer layer of the myocardium is extremely short or extremely long, sudden death may occur due to arrhythmia. That is, shortening or prolonging the time from depolarization of the myocardium to repolarization sometimes indicates the possibility of a certain pathological condition of the myocardium. Therefore, the signal analysis device 1 can obtain the time from depolarization of the inner or outer layer of the myocardium to repolarization of the inner or outer layer of the myocardium as a myocardial activity parameter. Specifically, at least any one of the following four parameters (1A) to (1D) can be obtained as a myocardial activity parameter.

[0188] (1A) Parameter representing the time from depolarization of the inner myocardial layer to repolarization of the inner myocardial layer The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may also acquire as the myocardial activity parameter the difference between the average value of the first unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the target heart, i.e., the first target time waveform, and the average value of the third unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform representing one cycle, i.e., the second target time waveform. For example, if the average value of the first unimodal distribution is μ , as in the above-mentioned example, a , the mean of the third unimodal distribution is μ c , the myocardial activity information parameter acquisition unit 132 may also set |μ a -μ c | is obtained as myocardial activity parameter. In addition, if μ c In terms of time, μ a If the myocardial activity information parameter acquisition unit 132 is late, c -μ a The myocardial activity parameter is obtained. The myocardial activity parameter is a parameter indicating the time from depolarization of the inner myocardial layer to repolarization of the inner myocardial layer.

[0189] (1B) Parameter representing the time from depolarization of the outer myocardial layer to repolarization of the outer myocardial layer The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may also acquire as the myocardial activity parameter the difference between the average value of the second unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the target heart, i.e., the first target time waveform, and the average value of the fourth unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform representing one cycle, i.e., the second target time waveform. For example, if the average value of the second unimodal distribution is μ , as in the above example, b , the mean of the fourth unimodal distribution is μ d , the myocardial activity information parameter acquisition unit 132 may also set |μ b -μ d | is obtained as myocardial activity parameter. In addition, if μ d In terms of time, μ b If the myocardial activity information parameter acquisition unit 132 is late, d -μ b The myocardial activity parameter is obtained. The myocardial activity parameter is a parameter indicating the time from depolarization of the myocardial outer layer to repolarization of the myocardial outer layer.

[0190] (1C) Parameter representing the time from depolarization of the outer myocardium to repolarization of the inner myocardium The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may also acquire as the myocardial activity parameter the difference between the average value of the second unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform of one cycle of the cardiac cycle of the target heart, that is, the first target time waveform, and the average value of the third unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform of the one cycle, that is, the second target time waveform. For example, if the average value of the second unimodal distribution is μ , as in the above example, b , the mean of the third unimodal distribution is μ c , the myocardial activity information parameter acquisition unit 132 may also set |μ b -μ c | is obtained as myocardial activity parameter. In addition, if μ c In terms of time, μ b If the myocardial activity information parameter acquisition unit 132 is late, c -μ b The myocardial activity parameter is obtained. The myocardial activity parameter is a parameter indicating the time required to switch from depolarization of the myocardial outer layer to repolarization of the myocardial inner layer.

[0191] (1D) Parameter representing the time from the depolarization of the inner myocardium layer to the repolarization of the outer myocardium layer The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may also acquire as the myocardial activity parameter the difference between the average value of the first unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the target heart, i.e., the first target time waveform, and the average value of the fourth unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform representing one cycle, i.e., the second target time waveform. For example, if the average value of the first unimodal distribution is μ , as in the above-mentioned example, a , the mean of the fourth unimodal distribution is μ d , the myocardial activity information parameter acquisition unit 132 may also set |μ a -μ d | is obtained as myocardial activity parameter. In addition, if μ d In terms of time, μ a If the myocardial activity information parameter acquisition unit 132 is late, d -μ a The myocardial activity parameter is obtained. The myocardial activity parameter is a parameter indicating the time required to switch from depolarization of the inner myocardial layer to repolarization of the outer myocardial layer.

[0192] (2) Parameters representing the time difference and order of inner layer activity and outer layer activity in depolarization When the time difference between the inner layer activity and the outer layer activity in depolarization is longer than normal, the conduction of myocardial excitation may be delayed or blocked, or the place where the excitation starts or the order of the excitation transmission may be different from the normal mode, especially indicating the obstruction of the myocardial stimulation conduction system or the ischemic state of the myocardium, and the existence of extrasystoles. Therefore, the signal analysis device 1 can obtain the parameters representing the time difference and order of the inner layer activity and the outer layer activity in depolarization as myocardial activity parameters. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain the difference between the average value of the first unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in the waveform showing one cycle of the cardiac cycle of the object heart, that is, the first object time waveform, and the average value of the second unimodal distribution as the myocardial activity parameter. For example, if, as in the above example, the average value of the first unimodal distribution is μ a , the mean of the second unimodal distribution is μ b , then the myocardial activity information parameter acquisition unit 132 may also set μ a -μ b or μ b -μ a Obtained as myocardial activity parameters.

[0193] (3) Parameters representing the time difference and order of inner layer activity and outer layer activity in repolarization When the time difference between the inner layer activity and the outer layer activity in repolarization is prolonged or shortened, certain abnormalities may occur in the myocardium. Therefore, the signal analysis device 1 can obtain the parameters representing the time difference and order of the inner layer activity and the outer layer activity in repolarization as myocardial activity parameters. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain the difference between the average value of the third unimodal distribution obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform showing one cycle of the cardiac cycle of the object heart, that is, the second object time waveform, and the average value of the fourth unimodal distribution as the myocardial activity parameter. For example, if, as in the above example, the average value of the third unimodal distribution is μ c , the mean of the fourth unimodal distribution is μ d , then the myocardial activity information parameter acquisition unit 132 may also set μ c -μ d or μ d -μ c Obtained as myocardial activity parameters.

[0194] (Correction of myocardial activity parameters) In addition, among the myocardial activity parameters related to the width in the time direction among the above-mentioned myocardial activity parameters acquired by the myocardial activity information parameter acquisition unit 132, there are characteristics that the influence of the variation caused by the heart rate and the influence of individual differences such as age and gender are seen, similarly to the parameters related to the width in the time direction among the previous parameters representing cardiac activity (e.g., QT interval). It is known that the previous parameters related to the width in the time direction are corrected to reduce the influence of the variation caused by the heart rate or the influence of individual differences, and the corrected values ​​are used as parameters for evaluating cardiac activity. Therefore, for the myocardial activity parameters acquired by the myocardial activity information parameter acquisition unit 132, corrections can also be performed to reduce the influence of the variation caused by the heart rate or the influence of individual differences, and the corrected values ​​can be used as parameters for evaluating myocardial activity. For example, for the variation caused by the heart rate, the myocardial activity parameters acquired by the above-mentioned fitting can also be corrected by linear or nonlinear calculation based on the time interval between adjacent R spines and R spines (hereinafter referred to as "RR interval"), and the corrected values ​​can be used as myocardial activity parameters. In addition, in order to account for individual differences, the myocardial activity parameters obtained by the fitting can be corrected by finding the relationship between the RR interval and the myocardial activity parameters based on the electrocardiogram data of each person, and the corrected values ​​can be used as the myocardial activity parameters. This correction can be performed by other devices after the signal analysis device 1 outputs the myocardial activity parameters obtained by the fitting, or it can be performed by the signal analysis device 1. In the case where the signal analysis device 1 corrects the myocardial activity parameters, for example, the myocardial activity information parameter acquisition unit 132 corrects the myocardial activity parameters obtained by the fitting, and outputs the corrected values ​​as the myocardial activity parameters. In addition, in the case where the correction is performed in the signal analysis device 1, the myocardial activity parameters obtained by the fitting become intermediate parameters obtained in the device in the end, but they are parameters representing myocardial activity in the same way as when they are output outside the device, and this point does not change.

[0195] (Variation Example) In addition, the electrocardiogram is preferably an induction electrocardiogram close to the electromotive force vector of the heart. The induction electrocardiogram close to the electromotive force vector is preferably an electrocardiogram that can obtain three-dimensional information of cardiac potentials such as II induction, V4 induction, and V5 induction. The more channels the electrocardiogram has, the more information it contains, so the more channels the electrocardiogram has, the better. That is, the waveforms of each channel of the multi-channel electrocardiogram can also be taken as objects and analyzed in the signal analysis device 1 to obtain myocardial activity parameters as the analysis results for each channel.

[0196] The signal analysis device 1 may be implemented using a plurality of information processing devices that are communicably connected via a network. In this case, each functional unit included in the signal analysis device 1 may be distributed and implemented in the plurality of information processing devices.

[0197] The electrocardiogram acquisition unit 110 is an example of a biological information acquisition unit.

[0198] <Second Embodiment> In the signal analysis device 1 of the first embodiment, it is possible to obtain myocardial activity parameters that are useful information for understanding the state of the heart based on biological information of a time series of one channel related to the heart beat. However, when the myocardial activity parameters obtained by the signal analysis device 1 of the first embodiment are directly presented to a user such as a doctor who is familiar with the electrocardiogram waveform, it may not be easy for the doctor or other user to understand the state of the heart. Therefore, the signal conversion device of the second embodiment can generate a corrected waveform that makes it easy for the doctor or other user to understand the state of the heart.

[0199] Fig.60 2 is a diagram showing an example of the functional structure of the signal conversion device 2 of the second embodiment. The signal conversion device 2 of the second embodiment includes a signal analysis unit 210, a converter acquisition unit 220, a conversion information setting unit 230, a myocardial activity parameter conversion unit 240, and a waveform synthesis unit 250. As with the signal analysis device 1 of the first embodiment, any biological information in a time series related to the pulsation of the target heart may be input to the signal conversion device 2 of the second embodiment. Hereinafter, an example in which the waveform of the electrocardiogram of the target heart is input to the signal conversion device 2 of the second embodiment will be described. The waveform of the electrocardiogram of the target heart input to the signal conversion device 2 is the waveform of the electrocardiogram obtained from the target heart using a known device or the like, and is the waveform to be processed by the signal conversion device 2, that is, the waveform to be corrected by the signal conversion device 2. The waveform of the electrocardiogram of the target heart input to the signal conversion device 2 is generally a continuous waveform of a plurality of beats (a plurality of cycles) (that is, a waveform in which the waveforms of a plurality of cycles are arranged in a time series), but it may be a waveform of at least one beat (a single cycle).

[0200] In the second embodiment and thereafter, the same parts as those of the signal analysis device 1 of the first embodiment are described by using the same sentences and symbols as those of the first embodiment, thereby omitting the same description as that of the first embodiment, avoiding redundant description, and focusing on the differences from the first embodiment. In addition, in the second embodiment and thereafter, since both information on the type of myocardial activity parameter and information on the value of the myocardial activity parameter are processed, in the second embodiment and thereafter, when indicating the value, it is marked as "the value of the 00 parameter", and when indicating the type, it is marked as "the 00 parameter", thereby distinguishing the type and the value.

[0201] The signal conversion device 2 obtains a synthesized signal based on the waveform of the electrocardiogram of the subject's heart inputted and outputs the synthesized signal. The synthesized signal obtained by the signal conversion device 2 is a signal having a waveform obtained by correcting the waveform of the electrocardiogram of the subject's heart inputted to the signal conversion device 2. The signal conversion device 2 of the second embodiment performs Fig.61 Steps S210, S220, S230A, S240A and S250 are shown.

[0202] The synthesized signal output by the signal conversion device 2 may be input to a display device having a screen for displaying waveforms, for example, and the display device may display the waveform of the synthesized signal. In the case where the signal conversion device 2 has a display unit as a display screen, the signal conversion device 2 may also display the waveform of the synthesized signal on the display unit of the device.

[0203] In addition, when a group of values ​​of myocardial activity parameters of the target heart (hereinafter referred to as a "myocardial activity parameter group") is already obtained, it is also possible to Fig.60 The waveform of the electrocardiogram of the object heart is replaced by a group formed by the values ​​of the myocardial activity parameters of the object heart input as shown by the single-dot chain line in . In this case, the group formed by the values ​​of the myocardial activity parameters of the object heart input to the signal conversion device 2 is a waveform showing the cardiac cycle of the object heart and is a group formed by the values ​​of the myocardial activity parameters obtained from the waveform that is the correction object. In this case, the signal conversion device 2 may not include the signal analysis unit 210, and step S210 may not be performed. It can be said that the signal synthesis device 3 obtains and outputs a synthesized signal from the group formed by the values ​​of the myocardial activity parameters. The hardware structure of the signal conversion device 2 or the signal synthesis device 3 is, for example, the same as Figure 1 The hardware structure of the signal analysis devices 1 shown is the same.

[0204] [Signal analysis unit 210] The waveform of the electrocardiogram of the target heart input to the signal conversion device 2 is input to the signal analysis unit 210. The signal analysis unit 210 obtains the value of the myocardial activity parameter of each cycle of the waveform of the electrocardiogram of the target heart from the waveform of each cycle of the waveform of the electrocardiogram of the target heart by performing the same processing as the signal analysis device 1 of the first embodiment (step S210). The value of the myocardial activity parameter obtained by the signal analysis unit 210 is output from the signal analysis unit 210 and input to the converter acquisition unit 220 and the myocardial activity parameter conversion unit 240. Specifically, the signal analysis unit 210 performs the first analysis processing or the second analysis processing described below.

[0205] The first analysis processing performed by the signal analysis unit 210 is the following processing: for the waveform of each cycle of the input target heart electrocardiogram, a time waveform formed by subtracting a function obtained by multiplying the second cumulative distribution function as a second unimodal distribution by the value of the second weight parameter from a function obtained by multiplying the first cumulative distribution function as a first unimodal distribution by the value of the first weight parameter and adding the value of the first level parameter is used to approximate the waveform of the time interval of the R wave of the cycle, that is, the first target time waveform, when determining the value of the parameter of the first unimodal distribution or the value of the parameter of the first cumulative distribution function, determining the value of the parameter of the second unimodal distribution or the value of the parameter of the second cumulative distribution function, the value of the first weight parameter, and the value of the second weight parameter. The value of the parameter, the value of the first level parameter, and the waveform formed by subtracting the fourth cumulative distribution function as the cumulative distribution function of the fourth unimodal distribution multiplied by the value of the fourth weight parameter from the function obtained by multiplying the third cumulative distribution function as the cumulative distribution function of the third unimodal distribution by the value of the third weight parameter and adding the value of the second level parameter, that is, the second approximate inverse time waveform, the waveform after the time axis of the waveform of the time interval of the T wave of the cycle is reversed, that is, the second object inverse time waveform, the value of the parameter of the third unimodal distribution is determined or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined or the value of the parameter of the fourth cumulative distribution function is determined, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter are obtained as the myocardial activity parameter. That is, the first weight parameter is the weight k of the first cumulative distribution function described in the first embodiment. a The second weight parameter is the weight k of the second cumulative distribution function described in the first embodiment. b The third weight parameter is the weight k of the third cumulative distribution function described in the first embodiment. e The fourth weight parameter is the weight k of the fourth cumulative distribution function described in the first embodiment. g The first level parameter is the level value β of the R wave described in the first embodiment. R The second level parameter is the level value β of the T wave described in the first embodiment. T .

[0206] In the case where each unimodal distribution is a Gaussian distribution, the parameters for determining each unimodal distribution or the parameters for determining each cumulative distribution function are the mean value and the standard deviation or variance of each Gaussian distribution. Therefore, in the case where each of the first to fourth unimodal distributions is a Gaussian distribution, an example of the value of the parameter for determining the first unimodal distribution or the value of the parameter for determining the first cumulative distribution function obtained by the first analytical processing is the mean value μ of the first unimodal distribution (i.e., the first Gaussian distribution). a and the value of the standard deviation σ aAn example of the value of the parameter determining the second unimodal distribution or the value of the parameter determining the second cumulative distribution function obtained by the first analytical process is the mean value μ of the second unimodal distribution (ie, the second Gaussian distribution). b and the value of the standard deviation σ b An example of the value of the parameter determining the third unimodal distribution or the value of the parameter determining the third cumulative distribution function obtained by the first analytical processing is the mean value μ of the third unimodal distribution (ie, the third Gaussian distribution) e and the value of the standard deviation σ e An example of the value of the parameter determining the fourth unimodal distribution or the value of the parameter determining the fourth cumulative distribution function obtained by the first analytical processing is the average value μ of the fourth unimodal distribution (ie, the fourth Gaussian distribution) g and the value of the standard deviation σ g .

[0207] The second analysis processing performed by the signal analysis unit 210 is the following processing: for the waveform of each cycle of the input target heart electrocardiogram, a time waveform formed by subtracting a function obtained by multiplying the second cumulative distribution function as a second unimodal distribution by the value of the second weight parameter from a function obtained by multiplying the first cumulative distribution function as a first unimodal distribution by the value of the first weight parameter and adding the value of the first level parameter to approximate the waveform of the time interval of the R wave of the cycle, that is, the first target time waveform, the value of the parameter of the first unimodal distribution is determined or the value of the parameter of the first cumulative distribution function is determined, the value of the parameter of the second unimodal distribution is determined or the value of the parameter of the second cumulative distribution function is determined, the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, and the value of the first inverse cumulative distribution function. When the waveform of the time interval of the T wave of the cycle, i.e., the second object time waveform, is approximated by the time waveform formed by the function obtained by multiplying the value of the third weight parameter by the fourth inverse cumulative distribution function and adding the value of the second level parameter, i.e., the second approximate time waveform, the value of the parameter of the third unimodal distribution is determined, or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined, or the value of the parameter of the fourth cumulative distribution function is determined, the value of the third weight parameter is determined, the value of the fourth weight parameter is determined, and the value of the second level parameter is obtained as the myocardial activity parameter, wherein the third inverse cumulative distribution function is a function obtained by subtracting the third cumulative distribution function as the cumulative distribution function of the third unimodal distribution from 1, and the fourth inverse cumulative distribution function is a function obtained by subtracting the fourth cumulative distribution function as the cumulative distribution function of the fourth unimodal distribution from 1. That is, the first weight parameter is the weight k of the first cumulative distribution function described in the first embodiment. a The second weight parameter is the weight k of the second cumulative distribution function described in the first embodiment.b The third weight parameter is the weight k of the third cumulative distribution function described in the first embodiment. c The fourth weight parameter is the weight k of the fourth cumulative distribution function described in the first embodiment. d The first level parameter is the level value β of the R wave described in the first embodiment. R The second level parameter is the level value β of the T wave described in the first embodiment. T .

[0208] When each of the first to fourth unimodal distributions is a Gaussian distribution, an example of a value of a parameter that determines the first unimodal distribution or a value of a parameter that determines the first cumulative distribution function obtained by the second analytical processing is an average value μ of the first unimodal distribution (i.e., the first Gaussian distribution). a and the value of the standard deviation σ a An example of the value of the parameter determining the second unimodal distribution or the value of the parameter determining the second cumulative distribution function obtained by the second analytical processing is the mean value μ of the second unimodal distribution (ie, the second Gaussian distribution). b and the value of the standard deviation σ b An example of the value of the parameter determining the third unimodal distribution or the value of the parameter determining the third cumulative distribution function obtained by the second analytical processing is the mean value μ of the third unimodal distribution (ie, the third Gaussian distribution) c and the value of the standard deviation σ c An example of the value of the parameter determining the fourth unimodal distribution or the value of the parameter determining the fourth cumulative distribution function obtained by the second analytical processing is the average value μ of the fourth unimodal distribution (ie, the fourth Gaussian distribution) d and the value of the standard deviation σ d .

[0209] Regardless of whether the first analysis process is performed or the second analysis process is performed, the signal analysis unit 210 obtains a group consisting of the values ​​of the parameters that determine the first unimodal distribution or the values ​​of the parameters that determine the first cumulative distribution function, the values ​​of the parameters that determine the second unimodal distribution or the values ​​of the parameters that determine the second cumulative distribution function, the values ​​of the first weight parameter, the values ​​of the second weight parameter, the values ​​of the first level parameter, the values ​​of the parameters that determine the third unimodal distribution or the values ​​of the parameters that determine the third cumulative distribution function, the values ​​of the parameters that determine the fourth unimodal distribution or the values ​​of the parameters that determine the fourth cumulative distribution function, the values ​​of the third weight parameter, the values ​​of the fourth weight parameter, and the values ​​of the second level parameter for the waveform of each cycle of the electrocardiogram of the input target heart. Hereinafter, the group consisting of the values ​​of the myocardial activity parameters obtained from the waveform of each cycle of the electrocardiogram is referred to as a myocardial activity parameter group. When the first to fourth unimodal distributions are Gaussian distributions, the myocardial activity parameter group is a group of 14 myocardial activity parameter values ​​(element values), including the average value of the first unimodal distribution, the standard deviation or variance value of the first unimodal distribution, the average value of the second unimodal distribution, the standard deviation or variance value of the second unimodal distribution, the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, the average value of the third unimodal distribution, the standard deviation or variance value of the third unimodal distribution, the average value of the fourth unimodal distribution, the standard deviation or variance value of the fourth unimodal distribution, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. For example, when the first to fourth unimodal distributions are Gaussian distributions and the signal analysis unit 210 performs the first analysis processing, the myocardial activity parameter group is (μ a , σ a , μ b , σ b , k a , k b , β R , μ e , σ e , μ g , σ g , k e , k g , β T ), the first to fourth unimodal distributions are Gaussian distributions and the myocardial activity parameter group when the signal analysis unit 210 performs the second analysis processing is (μ a , σ a , μ b , σ b , k a , k b , β R , μ c , σ c , μ d , σ d , k c , kd , β T ).

[0210] In addition, as described in the first embodiment, the unit of the information x indicating the time in each analysis process is arbitrary. However, after the second embodiment, in order to match the information indicating the time in each processing unit, the time at the start end of the cycle and the time at the end end of the cycle are respectively set to a predetermined sample number, or the time at the start end of the cycle and the time at the end end of the cycle are respectively set to a predetermined relative time, and each processing unit of the signal conversion device 2 is operated. For this purpose, for example, the waveform of the electrocardiogram of the target heart can be subjected to a known adjustment process such as compression and stretching in the time direction so that the waveform of the electrocardiogram of the heart of the target is 60 times / minute. The waveform of the electrocardiogram of the target heart input to the signal conversion device 2 can also be subjected to the above-mentioned adjustment process by the signal analysis unit 210 to obtain the value of the myocardial activity parameter. The waveform of the electrocardiogram of the target heart input to the signal conversion device 2 can also be subjected to the above-mentioned adjustment process by the signal analysis unit 210 to obtain the value of the myocardial activity parameter, and then the value of the myocardial activity parameter is processed corresponding to the above-mentioned adjustment process, and the value of the processed myocardial activity parameter is output.

[0211] [Converter Acquisition Unit 220] The myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 is input to the converter acquisition unit 220. When the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart is input to the signal conversion device 2, the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart input to the signal conversion device 2 is input to the converter acquisition unit 220. In a storage unit (not shown) in the converter acquisition unit 220, a myocardial activity parameter group of a standard electrocardiogram (hereinafter referred to as a "standard electrocardiogram") is pre-stored. The standard electrocardiogram is an electrocardiogram of a healthy subject, which, for example, is an excitation propagation of a normal sinus rhythm and the size, shape, position, orientation and physique of the heart are average, and the electrocardiogram is recorded, for example, according to international standard specifications. The myocardial activity parameter group of the standard electrocardiogram is a myocardial activity parameter group obtained from the waveform of one cycle of the standard electrocardiogram through the processing of the above-mentioned signal analysis unit 210. The converter acquisition unit 220 obtains a converter that converts the myocardial activity parameter group of the target heart into a bijective so that the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart is close to the myocardial activity parameter group of the standard electrocardiogram (step S220). More specifically, the converter acquisition unit 220 obtains a converter that converts the group formed by the myocardial activity parameters of the target heart into a bijective so that the group formed by the values ​​of the myocardial activity parameters of each cycle of the electrocardiogram of the target heart is close to the group formed by the values ​​of the myocardial activity parameters of one cycle of the standard electrocardiogram. The converter obtained by the converter acquisition unit 220 is output from the converter acquisition unit 220 and input to the myocardial activity parameter conversion unit 240.

[0212] In the case where the signal analysis unit 210 of the signal conversion device 2 performs the first analysis processing, or in the case where the myocardial activity parameter group input to the signal conversion device 2 is obtained through the first analysis processing, a myocardial activity parameter group is pre-stored in a storage unit (not shown) within the converter acquisition unit 220 for the waveform of one cycle of the standard electrocardiogram, the myocardial activity parameter group including: a time waveform formed by subtracting a function obtained by multiplying a second cumulative distribution function as a cumulative distribution function of a second unimodal distribution by a value of a second weight parameter from a function obtained by multiplying a first cumulative distribution function as a cumulative distribution function of a first unimodal distribution by a value of a first weight parameter and adding the value of a first level parameter to approximate the waveform of the time interval of the R wave in the waveform of the one cycle, i.e., the first object time waveform, the value of the parameter of the first unimodal distribution is determined or the value of the parameter of the first cumulative distribution function is determined when approximating the waveform of the time interval of the R wave in the waveform of the one cycle, i.e., the first object time waveform, the time waveform formed by subtracting a function obtained by multiplying a second cumulative distribution function as a cumulative distribution function of a second unimodal distribution by a value of a second weight parameter from a function obtained by multiplying a first cumulative distribution function as a cumulative distribution function of a first unimodal distribution by a value of a first weight parameter and adding the value of a first level parameter , determining the value of the parameter of the second unimodal distribution or determining the value of the parameter of the second cumulative distribution function, the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, and the waveform formed by subtracting the fourth cumulative distribution function as the cumulative distribution function of the fourth unimodal distribution by the value of the fourth weight parameter from the function obtained by multiplying the third cumulative distribution function as the cumulative distribution function of the third unimodal distribution by the value of the third weight parameter and adding the value of the second level parameter, that is, the second approximate inverse time waveform, the waveform of the time interval of the T wave in the waveform of the one cycle after the time axis is reversed, that is, the second object inverse time waveform, determining the value of the parameter of the third unimodal distribution or determining the value of the parameter of the third cumulative distribution function, determining the value of the parameter of the fourth unimodal distribution or determining the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. If the first to fourth unimodal distributions are Gaussian distributions, the average value of the first unimodal distribution is μ a0 , the standard deviation of the first unimodal distribution is σ a0 , the mean of the second unimodal distribution is μ b0 , the standard deviation of the second unimodal distribution is σ b0 , the mean of the third unimodal distribution is μ e0 , the standard deviation of the third unimodal distribution is σ e0 , the mean of the fourth unimodal distribution is μ g0 , the standard deviation of the fourth unimodal distribution is μ g0 , then the myocardial activity parameter group of the standard electrocardiogram pre-stored in the storage unit (not shown) in the converter acquisition unit 220 is (μ a0 , σ a0 , μ b0 , σ b0 , k a0 , k b0 , βR0 , μ e0 , σ e0 , μ g0 , σ g0 , k e0 , k g0 , β T0 ).

[0213] In the case where the signal analysis unit 210 of the signal conversion device 2 performs the second analysis processing, or in the case where the myocardial activity parameter group input to the signal conversion device 2 is obtained through the second analysis processing, a myocardial activity parameter group is pre-stored in a storage unit (not shown) within the converter acquisition unit 220 for the waveform of one cycle of the standard electrocardiogram, and the myocardial activity parameter group includes: when approximating the waveform of the time interval of the R wave in the waveform of the one cycle, i.e., the first object time waveform, a time waveform formed by a function obtained by multiplying the second cumulative distribution function as a cumulative distribution function of the second unimodal distribution by the value of the second weight parameter from the function obtained by multiplying the first cumulative distribution function as a cumulative distribution function of the first unimodal distribution by the value of the first weight parameter and adding the value of the first level parameter, determining the value of the parameter of the first unimodal distribution or determining the value of the parameter of the first cumulative distribution function, determining the value of the parameter of the second unimodal distribution or determining the value of the parameter of the second cumulative distribution function The value of the parameter, the value of the first weight parameter, the value of the second weight parameter, the value of the first level parameter, and the waveform formed by subtracting the function obtained by multiplying the fourth inverse cumulative distribution function by the value of the fourth weight parameter from the function obtained by multiplying the third inverse cumulative distribution function by the value of the fourth weight parameter and adding the value of the second level parameter from the function obtained by multiplying the third inverse cumulative distribution function by the value of the third weight parameter and adding the value of the second level parameter to approximate the waveform of the time interval of the T wave in the waveform of the one cycle, that is, the second object time waveform, the value of the parameter of the third unimodal distribution is determined, or the value of the parameter of the third cumulative distribution function is determined, or the value of the parameter of the fourth unimodal distribution is determined, or the value of the parameter of the fourth cumulative distribution function is determined, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. If the first to fourth unimodal distributions are Gaussian distributions, the average value of the first unimodal distribution is μ a0 , the standard deviation of the first unimodal distribution is σ a0 , the mean of the second unimodal distribution is μ b0 , the standard deviation of the second unimodal distribution is σ b0 , the mean of the third unimodal distribution is μ c0 , the standard deviation of the third unimodal distribution is σ c0, the mean of the fourth unimodal distribution is μ d0 , the standard deviation of the fourth unimodal distribution is σ d0 , then the myocardial activity parameter group of the standard electrocardiogram pre-stored in the storage unit (not shown) in the converter acquisition unit 220 is (μ a0 , σ a0 , μ b0 , σ b0 , k a0 , k b0 , β R0 , μ c0 , σ c0 , μ d0 , σ d0 , k c0 , k d0 , β T0 ).

[0214] The converter acquisition unit 220 generates a converter that transforms the myocardial activity parameter group of the target heart into a bijection so that the myocardial activity parameter group of the electrocardiogram of the target heart is close to the myocardial activity parameter group of the standard electrocardiogram. For example, the converter acquisition unit 220 generates the converter so that the total of the errors between each of the transformed myocardial activity parameter groups obtained by transforming each of the myocardial activity parameter groups of multiple cycles of the electrocardiogram of the target heart by the converter and the myocardial activity parameter group of the standard electrocardiogram is minimized. The converter acquisition unit 220 may generate one converter based on the myocardial activity parameter groups of all cycles of the electrocardiogram of the input target heart, or may generate one converter for each of the myocardial activity parameter groups of multiple cycles included in the myocardial activity parameter groups of all cycles of the electrocardiogram of the input target heart.

[0215] The converter generated by the converter acquisition unit 220 may be a transformation matrix, a function, a neural network, or other converters. In the case of any of the converters, the converter acquisition unit 220 may generate a converter using, for example, a known technique, which converts the myocardial activity parameter group of the target heart into a bijection so that the total error between each of the transformed myocardial activity parameter groups obtained by transforming each of the myocardial activity parameter groups of multiple cycles of the electrocardiogram of the target heart by the converter and the myocardial activity parameter group of the standard electrocardiogram is minimized.

[0216] [Modification of Converter Acquisition Unit 220] Alternatively, the converter generated based on the myocardial activity parameter group obtained from the waveform of the electrocardiogram obtained from the target heart in the past and the myocardial activity parameter group of the standard electrocardiogram may be pre-stored in a storage unit (not shown) within the converter acquisition unit 220, and the converter acquisition unit 220 may obtain the pre-stored converter. This method is described as a modified example of the converter acquisition unit 220. In the case of this modified example, the converter obtained by the converter acquisition unit 220 is also output from the converter acquisition unit 220 and input to the myocardial activity parameter conversion unit 240.

[0217] In the case of this modification, for example, Fig.60 As shown by the middle dotted line, in addition to the waveform of the electrocardiogram of the target heart, identification information for specifying the target heart (hereinafter referred to as "target heart ID") may be input to the signal conversion device 2, and the target heart ID input to the signal conversion device 2 may be input to the converter acquisition unit 220. The target heart ID is specified by a user such as a doctor. For example, the signal conversion device 2 includes a display unit as a display screen for displaying the target heart ID as a selection item, and an input unit as an input interface such as a keyboard or a mouse for receiving the selection by the user. The target heart ID as a selection item is displayed on the display unit, and the selection of a certain target heart ID by the user is received by the input unit, and the target heart ID received by the input unit is input to the converter acquisition unit 220. In addition, in the case of this modification, the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 may not be input to the converter acquisition unit 220. In addition, in the case of this modification, the converter acquisition unit 220 may not store the myocardial activity parameter group of the standard electrocardiogram.

[0218] The specific processing in this modification example is from the following sub-step S220-A1 to sub-step S220-A3. However, when the signal conversion device 2 is activated, the converter acquisition unit 220 performs the following sub-step S220-A3, and the following sub-steps S220-A1 and S220-A2 are performed in advance before using the signal conversion device 2.

[0219] Sub-step S220-A1: For each of the multiple target hearts, a converter is obtained, which converts the myocardial activity parameter group into a bijection so that each of the myocardial activity parameter groups of multiple cycles of the electrocardiogram obtained in the past is close to the myocardial activity parameter group of the standard electrocardiogram. The converter can be a transformation matrix, a function, a neural network, or other converters, and can be obtained by known techniques as described above. Sub-step S220 - A2 : Each transducer obtained in sub-step S220 - A1 is associated with the target heart ID and stored in advance in a storage unit (not shown) in the transducer acquisition unit 220 . Sub-step S220 - A3 : The transducer acquisition unit 220 acquires the transducer corresponding to the input target heart ID from a storage unit (not shown) within the transducer acquisition unit 220 .

[0220] [Conversion Information Setting Unit 230] The conversion information setting unit 230 receives the designation of one or more myocardial activity parameters by the user, and outputs conversion information as information for specifying the designated myocardial activity parameters (hereinafter referred to as "designated parameters") to the myocardial activity parameter conversion unit 240 (step S230A). The designated parameters are parameters designated by the user such as a doctor for the purpose of discovering or confirming abnormalities in the activity of the myocardium of the target heart.

[0221] For example, the conversion information setting unit 230 includes a display unit as a display screen for displaying identification information of the myocardial activity parameters to be selected, and an input unit as an input interface such as a keyboard or a mouse for receiving selection by the user, the identification information of the myocardial activity parameters to be selected is displayed on the display unit, the selection of one or more of the myocardial activity parameters to be selected by the user is received by the input unit, and conversion information is outputted, the conversion information being information for identifying one or more myocardial activity parameters selected by the input unit. The myocardial activity parameters to be selected are, for example, the 14 myocardial activity parameters described above.

[0222] The transformation information may be any information as long as it is information that can determine which of the options of the myocardial activity parameters the designated parameter is. It may be an identification number or a group of identification numbers indicating the type of the designated parameter, or an identification number or a group of identification numbers indicating the type of myocardial activity parameters that are not designated parameters (hereinafter also referred to as "non-designated parameters"), or an information group indicating whether various myocardial activity parameters that are options are designated parameters or non-designated parameters.

[0223] [Myocardial Activity Parameter Converter 240] The myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210, the converter output from the converter acquisition unit 220, and the conversion information output from the conversion information setting unit 230 are input to the myocardial activity parameter conversion unit 240. When the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart is input to the signal conversion device 2, the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart input to the signal conversion device 2 is input to the myocardial activity parameter conversion unit 240 instead of the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210. The myocardial activity parameter conversion unit 240 directly uses the value of the myocardial activity parameter included in the input myocardial activity parameter group as the value of the transformed myocardial activity parameter for the designated parameter determined by the conversion information, and uses the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter group through the converter as the value of the transformed myocardial activity parameter for the myocardial activity parameter that is not the designated parameter determined by the conversion information (i.e., the non-designated parameter), thereby obtaining the transformed myocardial activity parameter group for each cycle of the electrocardiogram of the target heart (step S240A). For example, the myocardial activity parameter conversion unit 240 obtains the transformed myocardial activity parameter group for each cycle of the electrocardiogram of the target heart by performing the following sub-steps S240A-1 and S240A-2. The transformed myocardial activity parameter group for each cycle of the electrocardiogram of the target heart obtained by the myocardial activity parameter conversion unit 240 is input to the waveform synthesis unit 250.

[0224] [Sub-step S240A-1] The myocardial activity parameter conversion unit 240 first converts the myocardial activity parameter group for each cycle of the electrocardiogram of the target heart through a converter to obtain a converted myocardial activity parameter group (hereinafter referred to as a "temporary converted myocardial activity parameter group"). Hereinafter, each parameter included in the tentative converted myocardial activity parameter group is referred to as a tentative converted myocardial activity parameter.

[0225] [Sub-step S240A-2] Next, the myocardial activity parameter conversion unit 240 uses the values ​​of the myocardial activity parameters included in the input myocardial activity parameter group as the values ​​of the transformed myocardial activity parameters for the designated parameters determined by the conversion information for each cycle of the electrocardiogram of the object's heart, and uses the values ​​of the provisional transformed myocardial activity parameters included in the provisional transformed myocardial activity parameter group as the values ​​of the transformed myocardial activity parameters for the myocardial activity parameters that are not designated parameters determined by the conversion information (i.e., non-designated parameters), thereby obtaining the transformed myocardial activity parameter group.

[0226] The values ​​of a part of the transformed myocardial activity parameters included in the transformed myocardial activity parameter group are the values ​​of the myocardial activity parameters included in the myocardial activity parameter group, and the values ​​of the remaining transformed myocardial activity parameters included in the transformed myocardial activity parameter group are the values ​​of the provisional transformed myocardial activity parameters included in the provisional transformed myocardial activity parameter group. Hereinafter, regardless of whether it is a value of the myocardial activity parameter or a value of the provisional transformed myocardial activity parameter, the following symbols are used to mark the values ​​of each transformed myocardial activity parameter. In addition, the sign of the mean value and the sign of the standard deviation described below are the signs when the first to fourth unimodal distributions are Gaussian distributions.

[0227] For each value of the converted myocardial activity parameter obtained by the myocardial activity parameter conversion unit 240 for each value of the myocardial activity parameter obtained from the waveform of the time interval of the R wave, the average value of the first unimodal distribution is denoted as μ′ a , label the value of the standard deviation of the first unimodal distribution as σ' a , and label the mean of the second unimodal distribution as μ' b , label the value of the standard deviation of the second unimodal distribution as σ' b , label the value of the first weight parameter as k' a , label the value of the second weight parameter as k' b , and label the value of the first level parameter as β' R .

[0228] For each converted myocardial activity parameter value obtained by the myocardial activity parameter conversion unit 240 for each myocardial activity parameter value obtained by the first analysis process from the waveform of the time interval of the T wave, the average value of the third unimodal distribution is denoted as μ′ e , label the value of the standard deviation of the third unimodal distribution as σ' e , and label the mean of the fourth unimodal distribution as μ' g , label the value of the standard deviation of the fourth unimodal distribution as σ' g , label the value of the third weight parameter as k' e , label the value of the fourth weight parameter as k' g , and label the value of the second level parameter as β' T .

[0229] For each converted myocardial activity parameter value obtained by the myocardial activity parameter conversion unit 240 for each myocardial activity parameter value obtained by the second analysis process from the waveform of the time interval of the T wave, the average value of the third unimodal distribution is denoted as μ′ c , label the value of the standard deviation of the third unimodal distribution as σ' c , and label the mean of the fourth unimodal distribution as μ' d , label the value of the standard deviation of the fourth unimodal distribution as σ'd , label the value of the third weight parameter as k' c , label the value of the fourth weight parameter as k' d , and label the value of the second level parameter as β' T .

[0230] [Waveform synthesis section 250] The transformed myocardial activity parameter group of each cycle of the electrocardiogram of the target heart obtained by the myocardial activity parameter conversion unit 240 is input to the waveform synthesis unit 250. The waveform synthesis unit 250 obtains a synthesized signal of each cycle from the transformed myocardial activity parameter group by performing the following first generation processing or second generation processing on each cycle of the electrocardiogram of the target heart (step S250). In addition, the waveform synthesis unit 250 can also connect the synthesized signals of multiple cycles in the order of cycles to obtain a synthesized signal of multiple cycles.

[0231] The first generation processing (step S250A) performed by the waveform synthesis unit 250 on each cycle is the case where the signal analysis unit 210 performs the first analysis processing, or the case where the myocardial activity parameter group input to the signal conversion device 2 is obtained through the first analysis processing, including: obtaining the synthetic waveform of the time interval of the R wave of the cycle (sub-step S250A-1), obtaining the synthetic waveform of the time interval of the T wave of the cycle (sub-step S250A-2), and connecting the synthetic waveform of the time interval of the R wave of the cycle with the synthetic waveform of the time interval of the T wave of the cycle to obtain the synthetic signal of the cycle (sub-step S250A-3).

[0232] The processing of sub-step S250A-1 performed by the waveform synthesis unit 250 is as follows: the function k' is obtained. a F a (x)-k' b F b (x)+β' R The resulting time waveform is used as the synthetic waveform of the time interval of the R wave. The function k' a F a (x)-k' b F b (x)+β' R is a cumulative distribution function F determined from the values ​​of the transformed myocardial activity parameters that determine the parameters of the first unimodal distribution or the parameters that determine the first cumulative distribution function a (x) The value k' of the transformed myocardial activity parameter multiplied by the first weight parameter a The obtained function k' a F aThe cumulative distribution function F is determined by subtracting the value of the transformed myocardial activity parameter of the parameter determining the second unimodal distribution or the parameter determining the second cumulative distribution function from (x) b (x) The value k' of the transformed myocardial activity parameter multiplied by the second weight parameter b The obtained function k' b F b (x) and add the value of the myocardial activity parameter after the transformation of the first level parameter β' R And get.

[0233] When the first unimodal distribution is a Gaussian distribution, the cumulative distribution function F a (x) is obtained by using the average value μ' a and the value of the standard deviation σ' a In addition, when the second unimodal distribution is a Gaussian distribution, the cumulative distribution function F b (x) is obtained by using the average value μ' b and the value of the standard deviation σ' b Therefore, the waveform synthesis unit 250 can obtain the synthesized waveform of the time interval of the R wave by using the formula (29) in the sub-step S250A-1.

[0234] [Formula 27]

[0235] [Formula 28]

[0236] [Formula 29]

[0237] In addition, the function k' a F a (x)-k' b F b (x)+β' R The value of this function is close to the minimum value β' R The time range of the value and the value of this function are close to the maximum value k' a -k' b +β' R In other words, if the purpose is to obtain a corrected waveform that makes it easy for doctors and other users to understand the heart state, then the function k' a F a (x)-k' b F b (x)+β' RThe time range in which the time variation of the value is lower than the time variation of the amplitude caused by noise or fluctuation does not need to be included in the time interval of the R wave. In addition, if each unimodal distribution is a Gaussian distribution, for example, 99.7% of the information is included in the time range up to the value of 6 times the value of the standard deviation from the mean value. Therefore, if the purpose is to obtain a corrected waveform that makes it easy for users such as doctors to understand the heart state, it is not necessary to include other time ranges in the time interval of the R wave. Therefore, in sub-step S250A-1 performed by the waveform synthesis unit 250, the time range based on the function k' is a F a (x)-k' b F b (x)+β' R The waveform of the time interval required for the doctor or other user to understand the heart state in the time waveform of the R wave can be obtained. Specifically, for example, the waveform synthesis unit 250 obtains the waveform of the time interval of the R wave based on the function k' a F a (x)-k' b F b (x)+β' R The synthesized waveform of the time interval of the R wave can be obtained by using the time interval determined by a predetermined method from the value of the standard deviation of the Gaussian distribution included in the function, or the time interval in which the value of the function is within the range determined by a predetermined method from the minimum and maximum values ​​of the function. More specifically, for example, the waveform synthesizer 250 obtains the synthesized waveform of the time interval of the R wave based on the function k' a F a (x)-k' b F b (x)+β' R The waveform of the time interval of the R wave can be obtained by taking the time interval of the Gaussian distribution included in the function as the value of 6 times the standard deviation. a F a (x)-k' b F b (x)+β' R There are two Gaussian distributions included in it, so there are four values ​​whose time difference from the mean value of the Gaussian distribution is 6 times the value of the standard deviation. However, for example, by taking the value closest to the start of the cycle among the values ​​whose time difference from the mean value of the Gaussian distribution is 6 times the value of the standard deviation as the moment of the start of the time interval of the R wave, and taking the value closest to the end of the cycle among the values ​​whose time difference from the mean value of the Gaussian distribution is 6 times the value of the standard deviation as the moment of the end of the time interval of the R wave, a synthetic waveform of the time interval of the R wave can be obtained.

[0238] The processing of sub-step S250A-2 performed by the waveform synthesis unit 250 is as follows: e F e Subtract function k' from (x') g F g (x') and add the value of the myocardial activity parameter after the transformation of the second level parameter β' T The obtained function k' e F e (x')-k' g F g (x')+β' T The time waveform after the time axis of the waveform is inverted is the synthetic waveform of the time interval of the T wave, and the function k' e F e (x') is a cumulative distribution function F determined by transforming the value of the myocardial activity parameter after the parameter determining the third unimodal distribution or the parameter determining the third cumulative distribution function. e (x') is multiplied by the value k' of the transformed myocardial activity parameter of the third weight parameter e And the function k' g F g (x') is a cumulative distribution function F determined by transforming the value of the myocardial activity parameter after the parameter determining the fourth unimodal distribution or the parameter determining the fourth cumulative distribution function. g (x') is multiplied by the value k' of the transformed myocardial activity parameter of the fourth weight parameter g And get.

[0239] If the third unimodal distribution is Gaussian, then the cumulative distribution function F e (x') is obtained by using the mean value μ' e and the value of the standard deviation σ' e In addition, if the fourth unimodal distribution is a Gaussian distribution, the cumulative distribution function F g (x') is obtained by using the mean value μ' g and the value of the standard deviation σ' g Therefore, the waveform synthesis unit 250 only needs to invert the time axis of the waveform obtained by equation (32) in sub-step S250A-2 to obtain a synthesized waveform of the time interval of the T wave.

[0240] [Formula 30]

[0241] [Formula 31]

[0242] [Formula 32]

[0243] In addition, for the same reason as described in sub-step S250A-2, in sub-step S250A-2 performed by the waveform synthesis unit 250, the waveform synthesis unit 250 performs the sub-step S250A-2 based on the function k'. e F e (x')-k' g F g (x')+β' T The waveform of the time interval required for the doctor or other user to understand the heart state in the time waveform after the time axis of the waveform is inverted can be obtained as a synthetic waveform of the time interval of the T wave. e F e (x')-k' g F g (x')+β' T The synthesized waveform of the time interval of the T wave can be obtained by the time interval determined by a predetermined method from the value of the standard deviation of the Gaussian distribution included in the function, or the time interval in which the value of the function is within the range determined by a predetermined method from the minimum and maximum values ​​of the function. More specifically, for example, the synthetic waveform based on the function k' e F e (x')-k' g F g (x')+β' T The waveform of the time interval of the time waveform after the time axis of the waveform is reversed, and the time difference from the average value of the Gaussian distribution included in the function is 6 times the value of the standard deviation, can be obtained as a synthetic waveform of the time interval of the T wave. e F e (x')-k' g F g (x')+β' T The time waveform after the time axis of the waveform is inverted includes two Gaussian distributions, so there are four values ​​whose time difference from the average value of the Gaussian distribution is 6 times the value of the standard deviation. However, for example, by using the value closest to the beginning of the cycle among the values ​​whose time difference from the average value of the Gaussian distribution is 6 times the value of the standard deviation as the moment of the beginning of the time interval of the T wave, and using the value closest to the end of the cycle among the values ​​whose time difference from the average value of the Gaussian distribution is 6 times the value of the standard deviation as the moment of the end of the time interval of the T wave, a synthetic waveform of the time interval of the T wave can be obtained.

[0244] The processing of sub-step S250A-3 performed by the waveform synthesis unit 250 is as follows: the starting end of the cycle and the starting end of the synthetic waveform of the time interval of the R wave obtained in the processing of sub-step S250A-1 are connected by a predetermined method, the ending end of the synthetic waveform of the time interval of the R wave obtained in the processing of sub-step S250A-1 and the starting end of the synthetic waveform of the time interval of the T wave obtained in the processing of sub-step S250A-2 are connected by a predetermined method, and the ending end of the synthetic waveform of the time interval of the T wave obtained in the processing of sub-step S250A-2 and the ending end of the cycle are connected by a predetermined method, thereby obtaining a synthetic signal of one cycle by connecting the synthetic waveform of the time interval of the R wave and the synthetic waveform of the time interval of the T wave. The predetermined method is, for example, a method of connecting the start end of the cycle and the start end of the composite waveform of the time interval of the R wave, the end end of the composite waveform of the time interval of the R wave and the start end of the composite waveform of the time interval of the T wave, and the end end of the composite waveform of the time interval of the T wave and the end end of the cycle respectively with a straight line or a predetermined function. The predetermined function is, for example, a function that smoothly connects the waveform at each of the start end of the composite waveform of the time interval of the R wave, the end end of the composite waveform of the time interval of the R wave, the start end of the composite waveform of the time interval of the T wave, and the end end of the composite waveform of the time interval of the T wave. In addition, when the waveform synthesis unit 250 connects the start end of a certain cycle among the multiple cycles with the start end of the composite waveform of the time interval of the R wave, it may also use a straight line or a predetermined function that connects the end end of the composite waveform of the time interval of the T wave of the cycle before the certain cycle and the start end of the composite waveform of the time interval of the R wave of the certain cycle. Similarly, when the waveform synthesis unit 250 connects the end end of the synthesized waveform of the time interval of the T wave of a certain cycle among multiple cycles and the end end of the cycle, it can also use a straight line or a predetermined function connecting the end end of the synthesized waveform of the time interval of the T wave of the certain cycle and the beginning end of the synthesized waveform of the time interval of the R wave of the cycle after the certain cycle.

[0245] The second generation process (step S250B) performed by the waveform synthesis unit 250 on each cycle is the case where the signal analysis unit 210 performs the second analysis process, or the myocardial activity parameter group input to the signal conversion device 2 is obtained through the second analysis process, including: obtaining the synthesis waveform of the time interval of the R wave of the cycle (sub-step S250B-1), obtaining the synthesis waveform of the time interval of the T wave of the cycle (sub-step S250B-2), and connecting the synthesis waveform of the time interval of the R wave of the cycle and the synthesis waveform of the time interval of the T wave of the cycle to obtain the synthesis signal of the cycle (sub-step S250B-3). Here, the process of sub-step S250B-1 is the same as the process of sub-step S250A-1, and the process of sub-step S250B-3 is the same as the process of sub-step S250A-3. The difference between step S250B and step S250A is that the following sub-step S250B-2 is included instead of sub-step S250A-2.

[0246] The processing of sub-step S250B-2 performed by the waveform synthesis unit 250 is as follows: c (1-F c (x)) by subtracting the function k' d (1-F d (x)) and add the value of the myocardial activity parameter after the transformation of the second level parameter β' T The obtained function k' c (1-F c (x))-k' d (1-F d (x))+β' T The time waveform is a synthetic waveform of the time interval of the T wave, and the function k' c (1-F c (x)) is a cumulative distribution function F determined by subtracting from 1 the value of the transformed myocardial activity parameter of the parameter determining the third unimodal distribution or the parameter determining the third cumulative distribution function. c (x) The function 1-F is obtained after c (x) The value k' of the transformed myocardial activity parameter multiplied by the third weight parameter c And the function k' d (1-F d (x)) is a cumulative distribution function F determined by subtracting from 1 the value of the transformed myocardial activity parameter of the parameter determining the fourth unimodal distribution or the parameter determining the fourth cumulative distribution function. d (x) The function 1-F is obtained after d (x) multiply the value k' of the transformed myocardial activity parameter by the fourth weight parameter d And get.

[0247] When the third unimodal distribution is Gaussian, the cumulative distribution function F c (x) is obtained by using the average value μ' c and the value of the standard deviation σ' c In addition, when the fourth unimodal distribution is a Gaussian distribution, the cumulative distribution function F d (x) is obtained by using the average value μ' d and the value of the standard deviation σ' d Therefore, the waveform synthesis unit 250 can obtain the synthesized waveform of the time interval of the T wave by using the formula (35) in the sub-step S250B-2.

[0248] [Formula 33]

[0249] [Formula 34]

[0250] [Formula 35]

[0251] In addition, for the same reason as described in sub-step S250A-2, in sub-step S250B-2 performed by the waveform synthesis unit 250, the waveform synthesis unit 250 performs the sub-step S250B-2 based on the function k'. c (1-F c (x))-k' d (1-F d (x))+β' T The waveform of the time interval required for the doctor or other user to understand the heart state in the time waveform of the T wave can be obtained as a synthetic waveform of the time interval of the T wave. c (1-F c (x))-k' d (1-F d (x))+β' T The synthesized waveform of the time interval of the T wave can be obtained by using the time interval determined by a predetermined method from the value of the standard deviation of the Gaussian distribution included in the function, or the time interval in which the value of the function is within the range determined by a predetermined method from the minimum and maximum values ​​of the function. More specifically, for example, the synthetic waveform based on the function k' c (1-F c (x))-k' d (1-F d (x))+β' T The waveform of the time interval of the T wave can be obtained by calculating the time difference from the average value of the Gaussian distribution included in the function to 6 times the value of the standard deviation.c (1-F c (x))-k' d (1-F d (x))+β' T There are two Gaussian distributions included in the time waveform of , so there are four values ​​whose time difference from the mean value of the Gaussian distribution is 6 times the value of the standard deviation. However, for example, by using the value closest to the beginning of the cycle among the values ​​whose time difference from the mean value of the Gaussian distribution is 6 times the value of the standard deviation as the beginning moment of the time interval of the T wave, and using the value closest to the end of the cycle among the values ​​whose time difference from the mean value of the Gaussian distribution is 6 times the value of the standard deviation as the ending moment of the time interval of the T wave, a synthetic waveform of the time interval of the T wave can be obtained.

[0252] [Method of using the signal conversion device 2 according to the second embodiment] For example, if a doctor wants to confirm an abnormality in the myocardial outer layer of the repolarization in the activity of the myocardium of the subject's heart, the doctor can set the parameter for determining the fourth unimodal distribution or the parameter for determining the fourth cumulative distribution function and the fourth weight parameter as the parameters of the myocardial outer layer of the repolarization as the specified parameters, and operate the signal conversion device 2 of the second embodiment. In this way, the signal conversion device 2 of the second embodiment outputs a synthetic signal in which the characteristics other than the characteristics of the activity of the myocardial outer layer of the repolarization in the activity of the myocardium of the subject's heart are greatly reduced, that is, a synthetic signal in which the characteristics of the activity of the myocardial outer layer of the repolarization in the activity of the myocardium of the subject's heart significantly remain. Therefore, by using the signal conversion device 2 of the second embodiment, the doctor can confirm the corrected waveform in which the characteristics of the activity of the myocardial outer layer of the repolarization in the activity of the myocardium of the subject's heart are significantly retained.

[0253] Alternatively, for example, when a doctor wants to confirm an abnormality related to ischemia of the subject's heart, the doctor may set the level parameter or weight parameter related to ischemia as a designated parameter to operate the signal conversion device 2 of the second embodiment. Thus, the signal conversion device 2 of the second embodiment outputs a synthetic signal in which the feature related to ischemia of the subject's heart is significantly retained, and the doctor can confirm the corrected waveform in which the feature related to ischemia of the subject's heart is significantly retained.

[0254] That is, according to the second embodiment, a user such as a doctor can confirm a corrected waveform in which features other than features related to the expected activity or expected abnormality in the activity of the myocardium of the target heart are significantly reduced, that is, a corrected waveform in which features related to the expected activity or expected abnormality in the activity of the myocardium of the target heart are significantly retained. In addition, the features reduced by the signal conversion device 2 of the second embodiment also include individual differences of the target heart.

[0255] <Modification 1 of Second Embodiment> For each myocardial activity parameter, a value between a value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the target heart and a value obtained by converting the value of the myocardial activity parameter by a converter may be used as the value of the converted myocardial activity parameter. This method is described as a first variation of the second embodiment, focusing on the differences from the second embodiment.

[0256] The signal conversion device 2 of the modification example 1 of the second embodiment includes a signal analysis unit 210, a converter acquisition unit 220, a conversion information setting unit 230, a myocardial activity parameter conversion unit 240, and a waveform synthesis unit 250 in the same manner as the signal conversion device 2 of the second embodiment, but the operation of the conversion information setting unit 230 and the operation of the myocardial activity parameter conversion unit 240 are different from those of the signal conversion device 2 of the second embodiment. The signal conversion device 2 of the modification example 1 of the second embodiment performs Fig.61 Steps S210, S220, S230B, S240B and S250 are shown. Step S210 performed by the signal analysis unit 210, step S220 performed by the converter acquisition unit 220 and step S250 performed by the waveform synthesis unit 250 of the signal conversion device 2 of the modification 1 of the second embodiment are the same as those of the signal conversion device 2 of the second embodiment.

[0257] In addition, similar to the second embodiment, when the myocardial activity parameter group of the target heart has been obtained, the myocardial activity parameter group of the target heart may be input instead of the electrocardiogram waveform of the target heart. In this case, the signal conversion device 2 may not include the signal analysis unit 210, and step S210 may not be performed. It can be said that it is a signal synthesis device 3 that obtains and outputs a synthesized signal from the myocardial activity parameter group.

[0258] [Conversion Information Setting Unit 230] The transformation information setting unit 230 receives the designation of the reflection weight value of each myocardial activity parameter by the user, and outputs the information that determines the designated reflection weight value for each myocardial activity parameter, i.e., the transformation information, to the myocardial activity parameter transformation unit 240 (step S230B). The reflection weight value of each myocardial activity parameter is designated by a user such as a doctor for the purpose of discovering or confirming abnormalities in the activity of the myocardium of the target heart. For example, the transformation information setting unit 230 causes the display unit to display a column or a slider for setting the reflection weight value of each myocardial activity parameter, and outputs the transformation information including the input value received by the user for each column or each slider by the input unit as the reflection weight value of each myocardial activity parameter. The myocardial activity parameter to be designated as the reflection weight value is, for example, the 14 types of myocardial activity parameters described above.

[0259] [Myocardial Activity Parameter Converter 240] The myocardial activity parameter conversion unit 240 uses the weighted average value of the value of the myocardial activity parameter included in the input myocardial activity parameter group and the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter group by the converter as the value of the transformed myocardial activity parameter, thereby obtaining the transformed myocardial activity parameter group for each cycle of the electrocardiogram of the target heart (step S240B). For example, the myocardial activity parameter conversion unit 240 uses the reflected weight value as the weight given to the value of the myocardial activity parameter included in the input myocardial activity parameter group for each myocardial activity parameter of each cycle, obtains the above-mentioned weighted average value, and uses the obtained weighted average value as the value of the transformed myocardial activity parameter.

[0260] For example, assuming that the lower limit value of the range reflecting the weight value is 0 and the upper limit value is 1, the myocardial activity parameter conversion unit 240, for each myocardial activity parameter of each cycle, multiplies the value of the myocardial activity parameter included in the input myocardial activity parameter group by the value reflecting the weight value, and multiplies the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter group through the converter by the value obtained by subtracting the value reflecting the weight value from 1, and adds the result obtained as the value of the transformed myocardial activity parameter. That is, the myocardial activity parameter conversion unit 240 assumes that the value of the myocardial activity parameter included in the input myocardial activity parameter group is the value of the first end of the possible range of the value of the transformed myocardial activity parameter, and assumes that the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter group through the converter is the value of the second end of the possible range of the value of the transformed myocardial activity parameter, so that the value of the first end of the possible range of the value of the transformed myocardial activity parameter, which is closer to the value of the first end of the possible range of the value of the transformed myocardial activity parameter, becomes the value of the transformed myocardial activity parameter, and the value of the second end of the possible range of the value of the transformed myocardial activity parameter, which is closer to the value of the second end of the possible range of the value of the transformed myocardial activity parameter, becomes the value of the transformed myocardial activity parameter, which is closer to the value of the second end of the possible range of the value of the transformed myocardial activity parameter, which is closer to the value of the second end of the possible range of the value of the transformed myocardial activity parameter, which is closer to the value of the second end of the possible range of the value of the transformed myocardial activity parameter, becomes the value of the transformed myocardial activity parameter. In addition, the myocardial activity parameter conversion unit 240 may directly use the value of the myocardial activity parameter included in the input myocardial activity parameter group as the value of the transformed myocardial activity parameter, and may use the value of the myocardial activity parameter included in the input myocardial activity parameter group as the value of the transformed myocardial activity parameter, which is 0.

[0261] In addition, for example, the myocardial activity parameter conversion unit 240 may also set the lower limit value of the range reflecting the possible weight value to 0, and the upper limit value to a value greater than 1, and for each myocardial activity parameter of each cycle, a value of the myocardial activity parameter included in the input myocardial activity parameter group multiplied by the value reflecting the weight value, and a value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter group through the converter multiplied by the value obtained by subtracting the value reflecting the weight value from 1, and the resultant value is used as the value of the transformed myocardial activity parameter. That is, the myocardial activity parameter conversion unit 240 may also assume, for each myocardial activity parameter of each cycle, that a specified value relative to the value of the myocardial activity parameter included in the input myocardial activity parameter group and located in the opposite direction to the value obtained by converting the value of the myocardial activity parameter through the converter is the value at the first end of the possible range of the value of the transformed myocardial activity parameter, and assume that the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter group through the converter is the value at the second end of the possible range of the value of the transformed myocardial activity parameter, so that the value of the transformed myocardial activity parameter is obtained in such a manner that the larger the reflection weight value determined by the conversion information, the closer it is to the first end of the possible range of the value of the transformed myocardial activity parameter, and the smaller the reflection weight value determined by the conversion information, the closer it is to the second end of the possible range of the value of the transformed myocardial activity parameter, and the value of the transformed myocardial activity parameter is obtained in such a manner. Furthermore, in this case, in addition to the value existing between the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the object heart and the value obtained by transforming the value of the myocardial activity parameter through the converter, a value that is located in the opposite direction to the value obtained by transforming the value of the myocardial activity parameter through the converter relative to the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the object heart is also allowed to become the value of the transformed myocardial activity parameter.

[0262] [Method of using the signal conversion device 2 according to the first variation of the second embodiment] For example, when a doctor wants to confirm an abnormality of the outer myocardial layer in the repolarization of the myocardial activity of the target heart, the doctor may specify the reflection weight value in such a manner that the reflection weight value of the parameter for determining the fourth unimodal distribution or the parameter for determining the fourth cumulative distribution function and the fourth weight parameter as the outer myocardial layer parameter in the repolarization becomes a value greater than the reflection weight value of the other myocardial activity parameters, and operate the signal conversion device 2 of the modification 1 of the second embodiment. In this way, the signal conversion device 2 of the modification 1 of the second embodiment outputs a synthetic signal in which features other than the features of the outer myocardial layer in the repolarization of the myocardial activity of the target heart are further reduced, that is, a synthetic signal in which the features of the outer myocardial layer in the repolarization of the myocardial activity of the target heart are more reflected. Therefore, by using the signal conversion device 2 of the second embodiment, the doctor can confirm the corrected waveform in which the features of the outer myocardial layer in the repolarization of the myocardial activity of the target heart are more reflected.

[0263] In addition, for example, when a doctor wants to confirm an abnormality related to ischemia of the target heart, the doctor may specify a reflection weight value so that the reflection weight value of the level parameter or weight parameter related to ischemia becomes a value larger than the reflection weight value of the other myocardial activity parameters, and operate the signal conversion device 2 of the modification 1 of the second embodiment. As a result, the signal conversion device 2 of the modification 1 of the second embodiment outputs a synthetic signal in which the feature related to ischemia of the target heart is more reflected, and the doctor can confirm a corrected waveform in which the feature related to ischemia of the target heart is more reflected.

[0264] That is, according to the first variation of the second embodiment, a user such as a doctor can confirm a corrected waveform in which features other than features related to the expected activity or expected abnormality in the activity of the myocardium of the target heart are further reduced, that is, a corrected waveform in which features related to the expected activity or expected abnormality in the activity of the myocardium of the target heart are more reflected. In addition, the features reduced by the signal conversion device 2 in the first variation of the second embodiment also include individual differences of the target heart.

[0265] <Modification 2 of Second Embodiment> Instead of the values ​​obtained by converting the values ​​of the myocardial activity parameters obtained from the waveform of the electrocardiogram of the target heart by the converter in the second embodiment, the values ​​of the myocardial activity parameters obtained from the waveform of the standard electrocardiogram may be used. That is, for each myocardial activity parameter, either the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the target heart or the value of the myocardial activity parameter obtained from the waveform of the standard electrocardiogram may be used as the value of the converted myocardial activity parameter. This method is described as a second variation of the second embodiment, focusing on the differences from the second embodiment.

[0266] Fig.62 1 is a diagram showing an example of the functional structure of the signal conversion device 2 of the second embodiment. The signal conversion device 2 of the second embodiment includes a signal analysis unit 210, a conversion information setting unit 230, a myocardial activity parameter conversion unit 240, and a waveform synthesis unit 250. The signal conversion device 2 of the second embodiment does not include a converter acquisition unit 220, and the operation and storage content of the myocardial activity parameter conversion unit 240 are different from those of the signal conversion device 2 of the second embodiment. The signal conversion device 2 of the second embodiment performs Fig.63 The steps S210, S230A, S240C and S250 shown in FIG. 1 are the same as those of the signal converter 2 of the second embodiment. The step S210 performed by the signal analysis unit 210, the step S230A performed by the conversion information setting unit 230, and the step S250 performed by the waveform synthesis unit 250 of the signal converter 2 of the second embodiment are the same as those of the signal converter 2 of the second embodiment. The method of using the signal converter 2 of the second embodiment is the same as that of the signal converter 2 of the second embodiment.

[0267] As in the second embodiment, when the myocardial activity parameter group of the target heart has been obtained, the myocardial activity parameter group of the target heart may be input instead of the waveform of the electrocardiogram of the target heart. In this case, the signal conversion device 2 may not include the signal analysis unit 210, and step S210 may not be performed. It can be said that the signal synthesis device 3 obtains and outputs the synthesized signal from the myocardial activity parameter group.

[0268] [Myocardial Activity Parameter Converter 240] The myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 and the conversion information output from the conversion information setting unit 230 are input to the myocardial activity parameter conversion unit 240. The myocardial activity parameter group of the standard electrocardiogram is pre-stored in a storage unit (not shown) within the myocardial activity parameter conversion unit 240. The myocardial activity parameter conversion unit 240 uses the value of the myocardial activity parameter included in the input myocardial activity parameter group as the value of the transformed myocardial activity parameter for the designated parameter determined by the conversion information, and uses the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram as the value of the transformed myocardial activity parameter for the myocardial activity parameter that is not the designated parameter determined by the conversion information (i.e., the non-designated parameter), thereby obtaining the transformed myocardial activity parameter group of each cycle of the electrocardiogram of the target heart (step S240C).

[0269] <Variation 3 of Second Embodiment> Instead of the values ​​obtained by converting the values ​​of the myocardial activity parameters obtained from the waveform of the electrocardiogram of the target heart by the converter in the modification example 1 of the second embodiment, the values ​​of the myocardial activity parameters obtained from the waveform of the standard electrocardiogram may be used. That is, for each myocardial activity parameter, the value existing between the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the target heart and the value of the myocardial activity parameter obtained from the waveform of the standard electrocardiogram may be used as the value of the myocardial activity parameter after conversion. This method is described as the modification example 3 of the second embodiment, focusing on the differences from the modification example 1 of the second embodiment.

[0270] An example of the functional structure of the signal conversion device 2 of the modification example 3 of the second embodiment is the same as the functional structure of the signal conversion device 2 of the modification example 2 of the second embodiment. Fig.62 That is, the signal conversion device 2 of the modification example 3 of the second embodiment includes a signal analysis unit 210, a conversion information setting unit 230, a myocardial activity parameter conversion unit 240, and a waveform synthesis unit 250. The signal conversion device 2 of the modification example 3 of the second embodiment does not include a converter acquisition unit 220, and the operation and storage content of the myocardial activity parameter conversion unit 240 are different from those of the signal conversion device 2 of the modification example 1 of the second embodiment. The signal conversion device 2 of the modification example 3 of the second embodiment performs Fig.63 The steps S210, S230B, S240D and S250 shown in FIG. 1 are the same as those of the signal converter 2 of the modification example 1 of the second embodiment. The method of using the signal converter 2 of the modification example 3 of the second embodiment is the same as the method of using the signal converter 2 of the modification example 1 of the second embodiment.

[0271] Similar to the first modification of the second embodiment, when the myocardial activity parameter group of the target heart has been obtained, the myocardial activity parameter group of the target heart may be input instead of the electrocardiogram waveform of the target heart. In this case, the signal conversion device 2 may not include the signal analysis unit 210, and may not perform step S210. It can be said that the signal synthesis device 3 obtains and outputs the synthesized signal from the myocardial activity parameter group.

[0272] [Myocardial Activity Parameter Converter 240] The myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 and the conversion information output from the conversion information setting unit 230 are input to the myocardial activity parameter conversion unit 240. When the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart is input to the signal conversion device 2, the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart input to the signal conversion device 2 is input to the myocardial activity parameter conversion unit 240 instead of the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210. In a storage unit (not shown) in the myocardial activity parameter conversion unit 240, the myocardial activity parameter group of the standard electrocardiogram is pre-stored. The myocardial activity parameter conversion unit 240 uses a weighted average of the values ​​of the myocardial activity parameters included in the input myocardial activity parameter group and the values ​​of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram as the value of the transformed myocardial activity parameter for each cycle, thereby obtaining the transformed myocardial activity parameter group for each cycle of the electrocardiogram of the target heart (step S240D).

[0273] For example, assuming that the lower limit value of the range that the weight value can take is 0 and the upper limit value is 1, the myocardial activity parameter transformation unit 240 multiplies the value of the myocardial activity parameter included in the input myocardial activity parameter group by the value reflecting the weight value, and multiplies the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram by the value obtained by subtracting the value reflecting the weight value from 1, and obtains the value of the transformed myocardial activity parameter. That is, the myocardial activity parameter conversion unit 240 assumes that the value of the myocardial activity parameter included in the input myocardial activity parameter group is the value of the first end of the possible range of the value of the transformed myocardial activity parameter, and assumes that the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram is the value of the second end of the possible range of the value of the transformed myocardial activity parameter, so that the value of the transformed myocardial activity parameter is obtained in such a manner that the value of the first end of the possible range of the value of the transformed myocardial activity parameter is closer to the value of the transformed myocardial activity parameter as the reflection weight value determined by the conversion information is larger, and the value of the second end of the possible range of the value of the transformed myocardial activity parameter is closer to the value of the transformed myocardial activity parameter as the reflection weight value determined by the conversion information is smaller. In addition, the myocardial activity parameter conversion unit 240 may directly use the value of the myocardial activity parameter included in the input myocardial activity parameter group as the value of the transformed myocardial activity parameter for the myocardial activity parameter whose reflection weight value is 1, and may directly use the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram as the value of the transformed myocardial activity parameter for the myocardial activity parameter whose reflection weight value is 0.

[0274] In addition, for example, the myocardial activity parameter transformation unit 240 may also set the lower limit value of the range reflecting the weight value to 0 and the upper limit value to a value greater than 1, and for each myocardial activity parameter of each cycle, the value of the myocardial activity parameter included in the input myocardial activity parameter group multiplied by the value reflecting the weight value and the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram multiplied by the value obtained by subtracting the value reflecting the weight value from 1 to obtain the value of the transformed myocardial activity parameter. That is, the myocardial activity parameter conversion unit 240 may also assume, for each myocardial activity parameter of each cycle, that a specified value that is located in the opposite direction to the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram relative to the value of the myocardial activity parameter included in the myocardial activity parameter group of the input is the value of the first end of the possible range of the value of the transformed myocardial activity parameter, and assume that the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram is the value of the second end of the possible range of the value of the transformed myocardial activity parameter, so that the value of the transformed myocardial activity parameter is obtained in such a manner that the larger the reflection weight value determined by the transformation information, the closer it is to the first end of the possible range of the value of the transformed myocardial activity parameter, and the smaller the reflection weight value determined by the transformation information, the closer it is to the second end of the possible range of the value of the transformed myocardial activity parameter. In addition, in this case, in addition to the value that exists between the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the object heart and the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram, a value that is located in the opposite direction to the value of the myocardial activity parameter included in the myocardial activity parameter group of the standard electrocardiogram relative to the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the object heart is also allowed to become the value of the transformed myocardial activity parameter.

[0275] As described above, the myocardial activity parameter conversion unit 240 of the second embodiment and its variants 1 to 3 transforms one or more myocardial activity parameters so that the values ​​of the myocardial activity parameters included in the myocardial activity parameter group of the above-mentioned object heart are close to or away from the values ​​of the myocardial activity parameters included in the myocardial activity parameter group of the standard electrocardiogram, thereby obtaining the values ​​of the transformed myocardial activity parameters.

[0276] <Variation 4 of Second Embodiment> For each myocardial activity parameter, a representative value of the myocardial activity parameter obtained from waveforms of a plurality of cycles of an electrocardiogram of the target heart may be used as the value of the converted myocardial activity parameter. This aspect is described as a fourth modification of the second embodiment, focusing on the differences from the second embodiment.

[0277] Fig.641 is a diagram showing an example of the functional structure of the signal conversion device 2 of the modification example 4 of the second embodiment. The signal conversion device 2 of the modification example 4 of the second embodiment includes a signal analysis unit 210, a myocardial activity parameter conversion unit 240, and a waveform synthesis unit 250. The signal conversion device 2 of the modification example 4 of the second embodiment does not include the converter acquisition unit 220 and the conversion information setting unit 230, and the operation of the myocardial activity parameter conversion unit 240 is different from that of the signal conversion device 2 of the second embodiment. The signal conversion device 2 of the modification example 4 of the second embodiment performs Fig.64 Steps S210, S240E and S250 are shown. Step S210 performed by the signal analysis unit 210 and step S250 performed by the waveform synthesis unit 250 of the signal conversion device 2 of the modification 4 of the second embodiment are the same as those of the signal conversion device 2 of the second embodiment.

[0278] As in the second embodiment, when the myocardial activity parameter group of the target heart has been obtained, the myocardial activity parameter group of the target heart may be input instead of the waveform of the electrocardiogram of the target heart. In this case, the signal conversion device 2 may not include the signal analysis unit 210, and step S210 may not be performed. It can be said that the signal synthesis device 3 obtains and outputs the synthesized signal from the myocardial activity parameter group.

[0279] [Myocardial Activity Parameter Converter 240] The myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 or the myocardial activity parameter group of each cycle of the electrocardiogram of the target heart input to the signal conversion device 2 is input to the myocardial activity parameter conversion unit 240. The myocardial activity parameter conversion unit 240 uses the representative value of the values ​​of the myocardial activity parameter of a plurality of cycles including the cycle (hereinafter referred to as the "target cycle") as the value of the transformed myocardial activity parameter for each myocardial activity parameter of each cycle, thereby obtaining the transformed myocardial activity parameter group of each cycle of the electrocardiogram of the target heart (step S240E). The representative value is, for example, a median value, an average value, a minimum value, a maximum value, and a moving average value.

[0280] The multiple cycles including the object cycle may be a continuous multiple cycle including the object cycle, or may be a discontinuous multiple cycle including the object cycle. In addition, the multiple cycles including the object cycle may be a multiple cycle formed by a cycle past the object cycle and the object cycle, or may be a multiple cycle formed by a cycle past the object cycle and a cycle future the object cycle, or may be a multiple cycle formed by a cycle future the object cycle and the object cycle. What kind of cycle to use as the multiple cycles including the object cycle may be preset, but may also be specified by a user such as a doctor. In the case of being specified by a user such as a doctor, the signal conversion device 2 includes a display screen that displays options for the multiple cycles including the object cycle, i.e., a display unit, and an input interface such as a keyboard or a mouse that accepts the selection by the user, i.e., an input unit, wherein the options are displayed by the display unit, and the user's selection of one of the options is accepted by the input unit, and the selection result accepted by the input unit is input into the myocardial activity parameter conversion unit 240.

[0281] [Method of using the signal conversion device 2 according to the fourth variation of the second embodiment] For example, if the doctor wants to confirm the myocardial activity other than the respiratory fluctuation of the subject's heart, the number of cardiac cycles corresponding to the time of one or more cycles of breathing consisting of inhalation and exhalation is used, and the continuous cycles of the number of cardiac cycles or more are regarded as "a plurality of cycles including the subject cycle", and the signal conversion device 2 of the fourth modification of the second embodiment is operated. In this way, the signal conversion device 2 uses the representative value of the value of the myocardial activity parameter of the cardiac cycle corresponding to the time of one or more cycles of breathing including the cycle as the value of the converted myocardial activity parameter for each myocardial activity parameter of each cycle, and obtains a synthetic signal. As a result, the signal conversion device 2 of the fourth modification of the second embodiment outputs the synthetic signal in which the influence of the respiratory fluctuation is reduced, and the doctor can confirm the corrected waveform in which the influence of the respiratory fluctuation of the electrocardiogram of the subject's heart is reduced.

[0282] In addition, for example, when a doctor wants to confirm the activity of the myocardium other than the expected physiological change of the target heart, the signal conversion device 2 of the fourth modification of the second embodiment may be operated by including both the cycle of the electrocardiogram of the target heart obtained by applying a predetermined load that causes the expected physiological change and the cycle of the electrocardiogram of the target heart obtained without applying the load in the "plurality of cycles including the target cycle". For example, the electrocardiogram of the target heart is continuously obtained in the state where the predetermined load that causes the expected physiological change is applied and the state where the predetermined load is not applied, and the signal conversion device 2 of the fourth modification of the second embodiment may obtain a synthetic signal by using the representative values ​​of all the cycles of the obtained electrocardiogram as the value of the myocardial activity parameter after conversion.

[0283] The loads that cause expected physiological changes refer to loads related to breathing (for example, ventilation caused by deep breathing or artificial respirator), loads related to posture (for example, body movements such as lying, standing, sitting, side-lying, their changes, head up, head down, lower limbs raised, or standing up, etc.), loads related to acceleration or pressure (acceleration applied to the body, pressure or decompression of the body such as the lower limbs or abdomen), loads caused by exercise, loads caused by drug administration, loads caused by changes in circulatory dynamics (for example, infusion, diuresis, dialysis), etc.

[0284] In the case where the doctor wants to confirm the activity of the myocardium other than the expected physiological change of the target heart, the signal conversion device 2 of the fourth variation of the second embodiment may be operated by including both the cycle of the electrocardiogram of the target heart obtained by applying a predetermined load that causes the expected physiological change to a large extent and the cycle of the electrocardiogram of the target heart obtained by applying the predetermined load to a small extent in the “multiple cycles including the target cycle”. For example, the electrocardiogram of the target heart may be continuously obtained in the state where the predetermined load is applied to a large extent and the state where the predetermined load is applied to a small extent, and the signal conversion device 2 of the fourth variation of the second embodiment may obtain a synthetic signal by using the representative values ​​of all the cycles of the obtained electrocardiogram as the values ​​of the myocardial activity parameters after conversion.

[0285] In the case where the doctor wants to confirm the activity of the myocardium other than the expected physiological change of the target heart, the signal conversion device 2 of the fourth variation of the second embodiment may be operated by including both the cycle of the electrocardiogram of the target heart obtained by applying the prescribed first load that causes the expected physiological change and the cycle of the electrocardiogram of the target heart obtained by applying the prescribed second load that causes the physiological change in the "plurality of cycles including the target cycle". For example, the electrocardiogram of the target heart may be continuously obtained in the state where the prescribed first load is applied and the state where the prescribed second load is applied, and the signal conversion device 2 of the fourth variation of the second embodiment may use the representative values ​​of all the cycles of the obtained electrocardiogram as the value of the myocardial activity parameter after conversion to obtain a synthetic signal.

[0286] As described above, the signal conversion device 2 uses the representative value of the value of the myocardial activity parameter of the cardiac cycle when the load is different as the value of the myocardial activity parameter after conversion, and obtains a synthetic signal. Thus, the signal conversion device 2 of the modification 4 of the second embodiment outputs a synthetic signal in which the influence of the desired physiological change is reduced, and the doctor can confirm the corrected waveform in which the influence of the desired physiological change of the electrocardiogram of the subject's heart is reduced.

[0287] <Variation 5 of Second Embodiment> In the myocardial activity parameter conversion unit 240 of the second embodiment and its variants 1 to 3, a representative value of the myocardial activity parameter value obtained from the waveform of the electrocardiogram of the target heart may be used instead of the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the target heart. This method is referred to as variant 5 of the second embodiment, and the difference from the second embodiment and its variants 1 to 3 is described. The difference between variant 5 of the second embodiment and the second embodiment and its variants 1 to 3 lies in the operation of the myocardial activity parameter conversion unit 240.

[0288] [Myocardial Activity Parameter Converter 240] like Fig.61 and Fig.63 As shown by the double-dashed line in the figure, the myocardial activity parameter conversion unit 240 first obtains a representative value of the values ​​of the myocardial activity parameters of multiple cycles of the cycle for each myocardial activity parameter of each cycle (step S240α). The method by which the myocardial activity parameter conversion unit 240 obtains the representative value is the same as that of the second embodiment and its variant 4. In addition, the myocardial activity parameter conversion unit 240 uses the representative value obtained in step S240α to replace the value of the myocardial activity parameter included in the input myocardial activity parameter group for each myocardial activity parameter of each cycle, and performs step S240A of the second embodiment, step S240B of variant 1 of the second embodiment, step S240C of variant 2 of the second embodiment, or step S240D of variant 3 of the second embodiment.

[0289] By using the signal conversion device 2 of the fifth modification of the second embodiment, it is possible to obtain a synthetic signal in which the characteristics related to the desired activity or abnormality are more retained in a state where the influence of the desired physiological change is reduced. That is, by using the signal conversion device 2 of the fifth modification of the second embodiment, a user such as a doctor can confirm a corrected waveform in which the influence of the desired physiological change on the activity of the myocardium of the target heart is reduced and the characteristics related to the desired activity or abnormality in the activity of the myocardium of the target heart are more retained.

[0290] <Variation 6 of the Second Embodiment> For any of the myocardial activity parameters, the conversion performed in the myocardial activity parameter conversion unit 240 of the second embodiment and its modifications 1 to 5 may be omitted, and the synthesized signal may be directly obtained by using the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the target heart. This method is described as modification 6 of the second embodiment, focusing on the differences from the second embodiment.

[0291] Fig.661 is a diagram showing an example of the functional structure of the signal conversion device 2 of the modification example 6 of the second embodiment. The signal conversion device 2 of the modification example 6 of the second embodiment includes a signal analysis unit 210 and a waveform synthesis unit 250. The signal conversion device 2 of the modification example 6 of the second embodiment does not include a converter acquisition unit 220, a conversion information setting unit 230, and a myocardial activity parameter conversion unit 240, which is different from the signal conversion device 2. The signal conversion device 2 of the modification example 6 of the second embodiment performs Fig.67 Steps S210 and S250 are shown. Step S210 performed by the signal analysis unit 210 of the signal conversion device 2 of the modification 6 of the second embodiment is the same as that of the signal conversion device 2 of the second embodiment.

[0292] In addition, similar to the second embodiment, when the myocardial activity parameter group of the target heart has been obtained, the myocardial activity parameter group of the target heart may be input instead of the waveform of the electrocardiogram of the target heart. In this case, the signal conversion device 2 may not include the signal analysis unit 210, and step S210 may not be performed. It can be said that it is a signal synthesis device 3 that obtains and outputs a synthesized signal from the myocardial activity parameter group.

[0293] [Waveform synthesis section 250] The myocardial activity parameter group of each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 is input to the waveform synthesis unit 250. The waveform synthesis unit 250 performs the first generation process or the second generation process described in the second embodiment for each cycle of the electrocardiogram of the target heart, using the values ​​of the myocardial activity parameters included in the input myocardial activity parameter group instead of the values ​​of the transformed myocardial activity parameters described in the second embodiment and its modified examples 1 to 5, thereby obtaining a synthesized signal of each cycle (step S250).

[0294] The synthesized signal output by the signal conversion device 2 of the modification 6 of the second embodiment is neither a waveform in which features other than features related to the desired activity or the desired abnormality are greatly reduced, nor a waveform in which the influence of the desired physiological change is reduced, but a waveform generated by the myocardial activity parameter representing the main features of the activity of the myocardium of the target heart. That is, the synthesized signal output by the signal conversion device 2 of the modification 6 of the second embodiment is a waveform in which noise is reduced, and the noise is not the main feature of the myocardial activity included in the waveform of the electrocardiogram of the target heart. That is, according to the signal conversion device 2 of the modification 6 of the second embodiment, the user such as a doctor can confirm the corrected waveform in which the noise included in the waveform of the electrocardiogram of the target heart is reduced.

[0295] In addition, all or part of the functions of the signal analysis device 1 and / or the signal conversion device 2 and / or the signal synthesis device 3 can also be implemented using hardware such as ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), FPGA (Field Programmable Gate Array). The program can be recorded in a computer-readable recording medium. The computer-readable recording medium is a storage device such as a removable medium such as a floppy disk, a magneto-optical disk, a ROM, a CD-ROM, a hard disk built into a computer system, etc. The program can also be sent via a telecommunications line.

[0296] As mentioned above, although the embodiment of the present invention is described in detail with reference to the drawings, the specific structure is not limited to this embodiment, and also includes the design etc. within the scope which does not deviate from the su...

Claims

1. A signal synthesis device, wherein: The invention comprises a waveform synthesis unit, wherein the waveform synthesis unit performs the following operations: The waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the heart is taken as the first target time waveform. The waveform of the time interval of the T wave included in the waveform is used as the second target time waveform, The second object time waveform is obtained by inverting the time axis as a second object inverse time waveform. A group including the following contents is used as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, the value of the parameter of the first unimodal distribution is determined or the value of the parameter of the first cumulative distribution function is determined, the value of the parameter of the second unimodal distribution is determined or the value of the parameter of the second cumulative distribution function is determined, the value of the first weight parameter, the value of the second weight parameter, and the value of the first level parameter; when the second object inverse time waveform is approximated by a second approximate inverse time waveform, the value of the parameter of the third unimodal distribution is determined or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. The first approximate The time waveform is a time waveform formed by subtracting the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, multiplied by the value of the second weight parameter from the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, multiplied by the value of the first weight parameter, and adding the value of the first level parameter; the second approximate inverse time waveform is a waveform formed by subtracting the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, multiplied by the value of the fourth weight parameter from the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, multiplied by the value of the third weight parameter, and adding the value of the second level parameter, The values ​​of each element included in the myocardial activity parameter group are used as myocardial activity parameter values. Using each myocardial activity parameter value included in the myocardial activity parameter group of the target heart (hereinafter referred to as the "target heart"), A composite waveform of the time interval of the R wave is obtained by subtracting a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the first weight parameter from a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter and adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first level parameter, and then adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter to obtain a time waveform formed by the function obtained by A waveform obtained by reversing the time axis of a waveform formed by subtracting a function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the third weight parameter from a function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weight parameter and adding the function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the fourth weight parameter and adding the function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the fourth weight parameter is obtained, and the synthetic waveform of the time interval of the T wave is connected with the synthetic waveform of the time interval of the T wave, thereby obtaining a synthetic signal of the waveform showing the cardiac cycle of the object heart.

2. A signal synthesis device, wherein: The invention comprises a waveform synthesis unit, wherein the waveform synthesis unit performs the following operations: The waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the heart is taken as the first target time waveform. The waveform of the time interval of the T wave included in the waveform is used as the second target time waveform, A group including the following contents is used as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, the value of the parameter of the first unimodal distribution is determined or the value of the parameter of the first cumulative distribution function is determined, the value of the parameter of the second unimodal distribution is determined or the value of the parameter of the second cumulative distribution function is determined, the value of the first weight parameter, the value of the second weight parameter, and the value of the first level parameter; when the second object time waveform is approximated by a second approximate time waveform, the value of the parameter of the third unimodal distribution is determined or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. The first approximate time waveform is obtained from the first approximate time waveform. a time waveform formed by subtracting a function obtained by multiplying the second unimodal distribution cumulative distribution function, i.e., the second cumulative distribution function, by the value of the second weight parameter from a function obtained by multiplying the first cumulative distribution function by the value of the first weight parameter, and adding the value of the first level parameter; the second approximate time waveform is a time waveform formed by subtracting a function obtained by multiplying the third unimodal distribution cumulative distribution function, i.e., the third cumulative distribution function, by the value of the third weight parameter from 1 from a function obtained by multiplying the third unimodal distribution cumulative distribution function, i.e., the third cumulative distribution function, by the value of the fourth weight parameter, and adding the value of the second level parameter from a function obtained by multiplying the fourth unimodal distribution cumulative distribution function, i.e., the fourth cumulative distribution function, by the value of the fourth weight parameter, from 1, The values ​​of each element included in the myocardial activity parameter group are used as myocardial activity parameter values. Using each myocardial activity parameter value included in the myocardial activity parameter group of the target heart (hereinafter referred to as the "target heart"), A composite waveform of the time interval of the R wave is obtained by subtracting a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the first weight parameter from a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter and adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first level parameter, and then adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter to obtain a time waveform formed by the function obtained by A time waveform formed by subtracting a function obtained by multiplying the myocardial activity parameter value of the third weight parameter from a cumulative distribution function determined by the myocardial activity parameter value that determines the third unimodal distribution or the third cumulative distribution function from 1 and multiplying the myocardial activity parameter value of the third weight parameter and adding the myocardial activity parameter value of the second level parameter to form a synthetic waveform of the time interval of the T wave, The synthesized waveform of the time interval of the R wave and the synthesized waveform of the time interval of the T wave are connected, thereby obtaining a synthesized signal of a waveform indicating the cardiac cycle of the subject's heart.

3. The signal synthesis device according to claim 1 or 2, wherein: It also includes a myocardial activity parameter conversion unit, which transforms one or more myocardial activity parameters so that the myocardial activity parameter values ​​included in the myocardial activity parameter group of the object heart are close to or away from the myocardial activity parameter values ​​included in the myocardial activity parameter group of the standard electrocardiogram, thereby obtaining transformed myocardial activity parameter values. The waveform synthesis unit uses the transformed myocardial activity parameter values ​​to replace the myocardial activity parameter values ​​included in the myocardial activity parameter group of the object heart, thereby obtaining a synthesized signal.

4. The signal synthesis device according to claim 3, wherein: Also includes: a converter acquisition unit that obtains a converter that transforms the myocardial activity parameter group of the target heart into a bijection so that the myocardial activity parameter group of the target heart approaches the myocardial activity parameter group of the standard electrocardiogram; and A conversion information setting unit is configured to obtain conversion information, wherein the conversion information determines one or more myocardial activity parameters, and the myocardial activity parameter conversion unit performs the following operations: For the myocardial activity parameter determined by the transformation information, the myocardial activity parameter value included in the myocardial activity parameter group of the subject heart is directly obtained as the transformed myocardial activity parameter value, For a myocardial activity parameter that is not a myocardial activity parameter determined by the conversion information, the myocardial activity parameter value included in the myocardial activity parameter group of the subject heart is converted by the converter to obtain the converted myocardial activity parameter value.

5. The signal synthesis device according to claim 3, wherein: Also includes: a converter acquisition unit, which obtains a converter, wherein the converter transforms the myocardial activity parameter group of the target heart into a bijection so that the myocardial activity parameter group of the target heart is close to the myocardial activity parameter group of the standard electrocardiogram; as well as a conversion information setting unit, which obtains conversion information, wherein the conversion information determines a reflection weight value of each myocardial activity parameter, The myocardial activity parameter conversion unit obtains, for each myocardial activity parameter, a weighted average of the myocardial activity parameter value included in the myocardial activity parameter group of the object heart and the value obtained by converting the myocardial activity parameter value included in the myocardial activity parameter group of the object heart by the converter, as the transformed myocardial activity parameter value, and the weighted average uses the reflected weight value as the weight assigned to the myocardial activity parameter value included in the myocardial activity parameter group of the object heart.

6. The signal synthesis device according to claim 1 or 2, wherein: The invention also includes a myocardial activity parameter conversion unit, which converts, for one or more myocardial activity parameters, a representative value of the myocardial activity parameter value included in the myocardial activity parameter group of a plurality of cycles of the subject heart into a converted myocardial activity parameter value, The waveform synthesis unit obtains a synthesis signal by replacing the myocardial activity parameter value included in the myocardial activity parameter group of the target heart with the converted myocardial activity parameter value.

7. A signal synthesis method performed by a signal synthesis device, wherein: The method comprises a waveform synthesis step, wherein the waveform synthesis step performs the following operations: The waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the heart is taken as the first target time waveform. The waveform of the time interval of the T wave included in the waveform is used as the second target time waveform, and the waveform obtained by inverting the time axis of the second target time waveform is used as the second target inverse time waveform. A group including the following contents is used as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, the value of the parameter of the first unimodal distribution is determined or the value of the parameter of the first cumulative distribution function is determined, the value of the parameter of the second unimodal distribution is determined or the value of the parameter of the second cumulative distribution function is determined, the value of the first weight parameter, the value of the second weight parameter, and the value of the first level parameter; when the second object inverse time waveform is approximated by a second approximate inverse time waveform, the value of the parameter of the third unimodal distribution is determined or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. The first approximate The time waveform is a time waveform formed by subtracting the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, multiplied by the value of the second weight parameter from the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, multiplied by the value of the first weight parameter, and adding the value of the first level parameter; the second approximate inverse time waveform is a waveform formed by subtracting the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, multiplied by the value of the fourth weight parameter from the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, multiplied by the value of the third weight parameter, and adding the value of the second level parameter, The values ​​of each element included in the myocardial activity parameter group are used as myocardial activity parameter values. Using each myocardial activity parameter value included in the myocardial activity parameter group of the target heart (hereinafter referred to as the "target heart"), A composite waveform of the time interval of the R wave is obtained by subtracting a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the first weight parameter from a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter and adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first level parameter, and then adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter to obtain a time waveform formed by the function obtained by A waveform obtained by reversing the time axis of a waveform formed by subtracting a function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the third weight parameter from a function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weight parameter and adding the function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the fourth weight parameter and adding the function obtained by multiplying the cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the fourth weight parameter is obtained, and the synthetic waveform of the time interval of the T wave is connected with the synthetic waveform of the time interval of the T wave, thereby obtaining a synthetic signal of the waveform showing the cardiac cycle of the object heart.

8. A signal synthesis method performed by a signal synthesis device, wherein: The method comprises a waveform synthesis step, wherein the waveform synthesis step performs the following operations: The waveform of the time interval of the R wave included in the waveform representing one cycle of the cardiac cycle of the heart is taken as the first target time waveform. The waveform of the time interval of the T wave included in the waveform is used as the second target time waveform, A group including the following contents is used as a myocardial activity parameter group: when the first object time waveform is approximated by a first approximate time waveform, the value of the parameter of the first unimodal distribution is determined or the value of the parameter of the first cumulative distribution function is determined, the value of the parameter of the second unimodal distribution is determined or the value of the parameter of the second cumulative distribution function is determined, the value of the first weight parameter, the value of the second weight parameter, and the value of the first level parameter; when the second object time waveform is approximated by a second approximate time waveform, the value of the parameter of the third unimodal distribution is determined or the value of the parameter of the third cumulative distribution function is determined, the value of the parameter of the fourth unimodal distribution is determined or the value of the parameter of the fourth cumulative distribution function, the value of the third weight parameter, the value of the fourth weight parameter, and the value of the second level parameter. The first approximate time waveform is obtained from the first approximate time waveform. a time waveform formed by subtracting a function obtained by multiplying the second unimodal distribution cumulative distribution function, i.e., the second cumulative distribution function, by the value of the second weight parameter from a function obtained by multiplying the first cumulative distribution function by the value of the first weight parameter, and adding the value of the first level parameter; the second approximate time waveform is a time waveform formed by subtracting a function obtained by multiplying the third unimodal distribution cumulative distribution function, i.e., the third cumulative distribution function, by the value of the third weight parameter from 1 from a function obtained by multiplying the third unimodal distribution cumulative distribution function, i.e., the third cumulative distribution function, by the value of the fourth weight parameter, and adding the value of the second level parameter from a function obtained by multiplying the fourth unimodal distribution cumulative distribution function, i.e., the fourth cumulative distribution function, by the value of the fourth weight parameter, from 1, The values ​​of each element included in the myocardial activity parameter group are used as myocardial activity parameter values. Using each myocardial activity parameter value included in the myocardial activity parameter group of the target heart (hereinafter referred to as the "target heart"), A composite waveform of the time interval of the R wave is obtained by subtracting a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the first weight parameter from a function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter and adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first level parameter, and then adding the function obtained by multiplying a cumulative distribution function determined by the myocardial activity parameter value that determines the parameters of the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weight parameter to obtain a time waveform formed by the function obtained by A time waveform formed by subtracting a function obtained by multiplying the myocardial activity parameter value of the third weight parameter from a cumulative distribution function determined by the myocardial activity parameter value that determines the third unimodal distribution or the third cumulative distribution function from 1 and multiplying the myocardial activity parameter value of the third weight parameter and adding the myocardial activity parameter value of the second level parameter to form a synthetic waveform of the time interval of the T wave, The synthesized waveform of the time interval of the R wave and the synthesized waveform of the time interval of the T wave are connected, thereby obtaining a synthesized signal of a waveform indicating the cardiac cycle of the subject's heart.

9. A program for causing a computer to function as the signal synthesizing device according to claim 1 or 2.

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