Signal analysis device, signal analysis method, and program

Abstract

The signal analysis device includes an analysis unit that obtains at least a part of the parameters for determining at least any one of the first unimodal distribution, the first cumulative distribution function, the second unimodal distribution, and the second cumulative distribution function when approximating the time waveform of the R wave by using the waveform generated by the difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or the waveform obtained by adding a level value to this waveform, and obtains at least a part of the parameters for determining at least any one of the third unimodal distribution, the third cumulative distribution function, the fourth unimodal distribution, and the fourth cumulative distribution function when approximating the time-axis reversed waveform of the time waveform of the T wave by using the waveform generated by the difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or the waveform obtained by adding a level value to this waveform, and obtains the parameters indicating the activity of the myocardium of the heart through the calculation of the obtained parameters.

Description

Technical Field

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

[0002] This application claims priority based on PCT / JP2021 / 000209 filed on January 6, 2021, and PCT / JP2021 / 033138 filed on September 9, 2021, the contents of which are incorporated herein by reference. Background Art

[0003] An electrocardiogram is useful information for grasping the state of the heart. For example, if an electrocardiogram is used, it is possible to determine whether a subject is in a state where the possibility of developing heart failure is high (Non-Patent Document 1).

[0004] Prior Art Documents

[0005] Non-Patent Documents

[0006] Non-Patent Document 1: Hiroshi Tanaka, "Methodology in Inverse Problems of Electrocardiogram", Medical Electronics and Bioengineering, 1985, Vol. 23, No. 3, pp. 147-158. Summary of the Invention

[0007] Problems to be Solved by the Invention

[0008] However, the waveform of an electrocardiogram is sometimes not sufficient to grasp the state of the heart. For example, even if the waveforms of electrocardiograms are similar waveforms, the onset patterns of heart-related diseases are sometimes different. Thus, it is sometimes difficult to grasp the state of the heart only by observing the waveform of the electrocardiogram itself depending on the disease. For example, in the case of heart failure, during its suppression, it is possible to suppress the onset by observing the state of the heart based on the waveform of the electrocardiogram in daily life. To further improve the accuracy of suppressing the onset, it is conceivable to use other techniques such as collecting blood to obtain other information, but it is not realistic to perform this technique in daily life. Therefore, depending on the disease, it is sometimes necessary to grasp the state of the heart substantially based only on the waveform of the electrocardiogram.

[0009] In addition, such a situation is not limited to the case of grasping the state of the heart based on the waveform of the electrocardiogram. Such a situation is also common in the following cases: grasping the state of the heart based on time-series biological information of one channel related to heart beats 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 implanted in the body, etc. Further, the time-series biological information related to heart beats is, for example, a waveform showing changes in cardiac potential, a waveform showing changes in cardiac pressure, a waveform showing changes in blood flow, a waveform showing changes in heart sound. Further, the waveform of the electrocardiogram is also an example of the time-series biological information related to heart beats.

[0010] In view of the above situation, an object of the present invention is to provide a technique for obtaining useful information for grasping the state of the heart from biological information in a time series of a channel related to heart beats.

[0011] Solution to the problem

[0012] One aspect of the present invention is a signal analysis device, which includes: a biological information acquisition unit that acquires, as a first target time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cycle of a cardiac cycle of a heart to be analyzed, and acquires, as a second target time waveform, a waveform of a time interval of a T wave included in the waveform of the amount of one cycle; and an analysis unit that performs the following operations: acquiring a time waveform, i.e., a first approximate time waveform, generated by a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or a time waveform, i.e., a first approximate time waveform, generated by a result obtained by adding a level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, and approximating at least any one of at least a part of parameters for determining the first unimodal distribution, at least a part of parameters for determining the first cumulative distribution function, at least a part of parameters for determining the second unimodal distribution, and at least a part of parameters for determining the second cumulative distribution function as a parameter showing the activity of the myocardium of the heart in the time interval of the R wave; using, as a second target reverse time waveform, a waveform obtained by reversing the time axis of the second target time waveform; acquiring a waveform, i.e., a second approximate reverse time waveform, generated by a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, or a waveform, i.e., a second approximate reverse time waveform, generated by a result obtained by adding a level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, and approximating at least any one of at least a part of parameters for determining the third unimodal distribution, at least a part of parameters for determining the third cumulative distribution function, at least a part of parameters for determining the fourth unimodal distribution, and at least a part of parameters for determining the fourth cumulative distribution function as a parameter showing the activity of the myocardium of the heart in the time interval of the T wave; and obtaining a parameter showing the activity of the myocardium of the heart through an operation of a parameter showing the activity of the myocardium of the heart in the time interval of the R wave and a parameter showing the activity of the myocardium of the heart in the time interval of the T wave.

[0013] One aspect of the present invention is a signal analysis device, which includes: a biological information acquisition unit that acquires a waveform of a time interval of an R wave included in a waveform of an amount of one cardiac cycle of a heart to be analyzed as a first target time waveform, and acquires a waveform of a time interval of a T wave included in the waveform of the amount of one cardiac cycle as a second target time waveform; and an analysis unit that performs the following operations: acquiring a time waveform, i.e., a first approximate time waveform, generated by a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or acquiring a time waveform, i.e., a first approximate time waveform, generated by a result obtained by adding a level value to a difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, and approximating the first target time waveform, and determining at least any one of at least a part of parameters of the first unimodal distribution, at least a part of parameters of the first cumulative distribution function, at least a part of parameters of the second unimodal distribution, and at least a part of parameters of the second cumulative distribution function as a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave; using a cumulative distribution function of a third unimodal distribution as a third cumulative distribution function, using a cumulative distribution function of a fourth unimodal distribution as a fourth cumulative distribution function, using a function obtained by subtracting the third cumulative distribution function from 1 as a third inverse cumulative distribution function, and using a function obtained by subtracting the fourth cumulative distribution function from 1 as a fourth inverse cumulative distribution function; acquiring a time waveform, i.e., a second approximate time waveform, generated by a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or acquiring a time waveform, i.e., a second approximate time waveform, generated by a result obtained by adding a level value to a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, and approximating the second target time waveform, and determining at least any one of at least a part of parameters of the third unimodal distribution, at least a part of parameters of the third cumulative distribution function, at least a part of parameters of the fourth unimodal distribution, and at least a part of parameters of the fourth cumulative distribution function as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; and obtaining a parameter indicating the activity of the myocardium of the heart through an operation between the parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the myocardium of the heart in the time interval of the T wave.

[0014] One aspect of the present invention is a signal analysis device, which includes: a biological information acquisition unit that acquires, as an object time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cycle of a cardiac cycle of a heart to be analyzed; and an analysis unit that performs the following operations: acquires an approximate time waveform, which is a time waveform generated by a difference or a weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or an approximate time waveform generated by a result obtained by adding a level value to a difference or a weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, to approximate the object time waveform, and determines at least one of at least a part of parameters that determine the first unimodal distribution and at least a part of parameters that determine the first cumulative distribution function as a parameter indicating an activity of an inner layer of the myocardium of the heart in the time interval of the R wave, and acquires at least one of at least a part of parameters that determine the second unimodal distribution and at least a part of parameters that determine the second cumulative distribution function as a parameter indicating an activity of an outer layer of the myocardium of the heart in the time interval of the R wave; and acquires a parameter indicating an activity of the myocardium of the heart by an operation between a parameter indicating an activity of an inner layer of the myocardium of the heart in the time interval of the R wave and a parameter indicating an activity of an outer layer of the myocardium of the heart in the time interval of the R wave.

[0015] One aspect of the present invention is a signal analysis device, which includes: a biological information acquisition unit that acquires a waveform of a time interval of a T wave included in a waveform representing a quantity of one cycle of a cardiac cycle of a heart to be analyzed as an object time waveform; and an analysis unit that performs the following operations: using a waveform obtained by reversing the time axis of the object time waveform as an object reverse time waveform, obtaining an approximate reverse time waveform that is a waveform generated by a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, or an approximate reverse time waveform that is a waveform generated by adding a level value to a difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, approximating the object reverse time waveform, determining at least a part of parameters of the third unimodal distribution and at least a part of parameters of the third cumulative distribution function as parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and obtaining at least a part of parameters of the fourth unimodal distribution and at least a part of parameters of the fourth cumulative distribution function as parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; and obtaining parameters indicating the activity of the myocardium of the heart by performing an operation on the parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and the parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave.

[0016] One aspect of the present invention is a signal analysis device, which includes: a biological information acquisition unit that acquires a waveform of a time interval of a T wave included in a waveform of a quantity of one cycle of a cardiac cycle of a heart to be analyzed as an object time waveform; and an analysis unit that performs the following operations: using the cumulative distribution function of a third unimodal distribution as a third cumulative distribution function, using the cumulative distribution function of a fourth unimodal distribution as a fourth cumulative distribution function, using a function obtained by subtracting the third cumulative distribution function from 1 as a third inverse cumulative distribution function, using a function obtained by subtracting the fourth cumulative distribution function from 1 as a fourth inverse cumulative distribution function, acquiring a time waveform generated by a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., an approximate time waveform, or a time waveform generated by a result obtained by adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., an approximate time waveform, to approximate the object time waveform, and taking at least any one of at least a part of the parameters that determine the third unimodal distribution and at least a part of the parameters that determine the third cumulative distribution function as a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and taking at least any one of 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 as a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; and obtaining a parameter indicating the activity of the myocardium of the heart by performing an operation on the parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and the parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave.

[0017] One aspect of the present invention is a signal analysis method, which includes: a biological information acquisition step of acquiring, as a first target time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cardiac cycle of a heart to be analyzed, and acquiring, as a second target time waveform, a waveform of a time interval of a T wave included in the waveform of the amount of one cardiac cycle; and an analysis step in which, when approximating the first target time waveform with a time waveform, i.e., a first approximate time waveform, generated by a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or a time waveform, i.e., a first approximate time waveform, generated by a result obtained by adding a level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least any one of at least a part of parameters for determining the first unimodal distribution, at least a part of parameters for determining the first cumulative distribution function, at least a part of parameters for determining the second unimodal distribution, and at least a part of parameters for determining the second cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave; a waveform obtained by reversing the time axis of the second target time waveform is used as a second target reverse time waveform; when approximating the second target reverse time waveform with a waveform, i.e., a second approximate reverse time waveform, generated by a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, or a waveform, i.e., a second approximate reverse time waveform, generated by a result obtained by adding a level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, at least any one of at least a part of parameters for determining the third unimodal distribution, at least a part of parameters for determining the third cumulative distribution function, at least a part of parameters for determining the fourth unimodal distribution, and at least a part of parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; and a parameter indicating the activity of the myocardium of the heart is obtained by an operation between the parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the myocardium of the heart in the time interval of the T wave.

[0018] One aspect of the present invention is a signal analysis method, which includes: a biological information acquisition step of acquiring, as a first target time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cardiac cycle of a heart to be analyzed, and acquiring, as a second target time waveform, a waveform of a time interval of a T wave included in the waveform of the amount of one cardiac cycle; and an analysis step in which, when approximating the first target time waveform by acquiring a time waveform, i.e., a first approximate time waveform, generated by a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or by a result generated by adding a level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least any one of at least a part of parameters for determining the first unimodal distribution, at least a part of parameters for determining the first cumulative distribution function, at least a part of parameters for determining the second unimodal distribution, and at least a part of parameters for determining the second cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave; taking the cumulative distribution function of a third unimodal distribution as a third cumulative distribution function, taking the cumulative distribution function of a fourth unimodal distribution as a fourth cumulative distribution function, taking the function obtained by subtracting the third cumulative distribution function from 1 as a third inverse cumulative distribution function, and taking the function obtained by subtracting the fourth cumulative distribution function from 1 as a fourth inverse cumulative distribution function; when approximating the second target time waveform by acquiring a time waveform, i.e., a second approximate time waveform, generated by a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or by a result generated by adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, at least any one of at least a part of parameters for determining the third unimodal distribution, at least a part of parameters for determining the third cumulative distribution function, at least a part of parameters for determining the fourth unimodal distribution, and at least a part of parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; and obtaining a parameter indicating the activity of the myocardium of the heart by an operation between the parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the myocardium of the heart in the time interval of the T wave.

[0019] One aspect of the present invention is a signal analysis method, which includes: a biological information acquisition step of acquiring, as an object time waveform, a waveform of a time interval of an R wave included in a waveform of a quantity of one cycle of a cardiac cycle of a heart to be analyzed; and an analysis step of obtaining, in the analysis step, an approximate time waveform, which is a time waveform generated by a difference or a weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or an approximate time waveform generated by a result obtained by adding a level value to a difference or a weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, to approximate the object time waveform, and determining at least one of at least a part of parameters of the first unimodal distribution and at least a part of parameters of the first cumulative distribution function as a parameter indicating an activity of an inner layer of myocardium of the heart in the time interval of the R wave, and determining at least one of at least a part of parameters of the second unimodal distribution and at least a part of parameters of the second cumulative distribution function as a parameter indicating an activity of an outer layer of myocardium of the heart in the time interval of the R wave; and obtaining a parameter indicating an activity of the myocardium of the heart by an operation between the parameter indicating an activity of the inner layer of myocardium of the heart in the time interval of the R wave and the parameter indicating an activity of the outer layer of myocardium of the heart in the time interval of the R wave.

[0020] One aspect of the present invention is a signal analysis method, which includes: a biological information acquisition step of acquiring, as an object time waveform, a waveform of a time interval of a T wave included in a waveform of an amount of one cycle of a cardiac cycle of a heart to be analyzed; and an analysis step in which, as an object reverse time waveform, a waveform obtained by reversing the time axis of the object time waveform is used, and an approximate reverse time waveform generated by a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, or an approximate reverse time waveform generated by a result obtained by adding a level value to a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, is used to approximate the object reverse time waveform, at least a part of parameters for determining the third unimodal distribution and at least a part of parameters for determining the third cumulative distribution function are determined as parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and at least a part of parameters for determining the fourth unimodal distribution and at least a part of parameters for determining the fourth cumulative distribution function are determined as parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; and by performing an operation on the parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and the parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave, parameters indicating the activity of the myocardium of the heart are obtained.

[0021] One aspect of the present invention is a signal analysis method, which includes: a biological information acquisition step of acquiring, as an object time waveform, a waveform of a time interval of a T wave included in a waveform showing a quantity of one cycle of a cardiac cycle of a heart to be analyzed; and an analysis step in which a cumulative distribution function of a third unimodal distribution is used as a third cumulative distribution function, a cumulative distribution function of a fourth unimodal distribution is used as a fourth cumulative distribution function, a function obtained by subtracting the third cumulative distribution function from 1 is used as a third inverse cumulative distribution function, a function obtained by subtracting the fourth cumulative distribution function from 1 is used as a fourth inverse cumulative distribution function, and an approximate time waveform, which is a time waveform generated by a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or an approximate time waveform, which is a time waveform generated by a result obtained by adding a level value to a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, is acquired to approximate the object time waveform, and at least any one of at least a part of parameters for determining the third unimodal distribution and at least a part of parameters for determining the third cumulative distribution function is used as a parameter showing an activity of an inner layer of myocardium of the heart in the time interval of the T wave, and at least any one of at least a part of parameters for determining the fourth unimodal distribution and at least a part of parameters for determining the fourth cumulative distribution function is used as a parameter showing an activity of an outer layer of myocardium of the heart in the time interval of the T wave; and a parameter showing an activity of the myocardium of the heart is acquired by an operation between the parameter showing an activity of the inner layer of myocardium of the heart in the time interval of the T wave and the parameter showing an activity of the outer layer of myocardium of the heart in the time interval of the T wave.

[0022] One aspect of the present invention is a program for causing a computer to function as the above-described signal analysis device.

[0023] Advantages of the Invention

[0024] According to the present invention, a technique for obtaining useful information for grasping the state of the heart from time-series biological information of one channel related to heart beats can be provided. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 is a diagram showing an example of the hardware configuration of the signal analysis device 1 according to the embodiment.

[0026] Figure 2 is a diagram schematically showing a function obtained by multiplying a weight by a first cumulative distribution function, a function obtained by multiplying a weight by a second cumulative distribution function, 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 object time waveform.

[0027] Figure 3 It is a diagram schematically showing a function obtained by multiplying a third inverse cumulative distribution function regarding a second object time waveform by a weight, a function obtained by multiplying a fourth inverse cumulative distribution function 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.

[0028] Figure 4 It is a diagram showing an example of a result of fitting a waveform of an electrocardiogram of an object heart in an embodiment by four cumulative distribution functions.

[0029] Figure 5 It is an explanatory diagram showing that a difference between two cumulative distribution functions in an embodiment can be fitted to a waveform of a descending T wave as substantially the same waveform.

[0030] Figure 6 It is a diagram schematically showing a function obtained by multiplying a first cumulative distribution function regarding a first object time waveform by a weight and adding a level value, a function obtained by multiplying a second cumulative distribution function by a weight and adding a level value, and an approximate time waveform obtained by adding a level value to a weighted difference between the first cumulative distribution function and the second cumulative distribution function.

[0031] Figure 7 It is a diagram schematically showing a function obtained by multiplying a third inverse cumulative distribution function regarding a second object time waveform by a weight and adding a level value, a function obtained by multiplying a fourth inverse cumulative distribution function by a weight and adding a level value, and an approximate time waveform obtained by adding a level value to a weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function.

[0032] Figure 8 It is a diagram schematically showing a Δ wave included in an object time waveform.

[0033] Figure 9 It is a diagram showing an example of a functional structure of a control unit 11 in an embodiment.

[0034] Figure 10 It is a flowchart showing an example of a processing flow executed by a signal analysis device 1 in an embodiment.

[0035] Figure 11 It is a first diagram showing an example of an analysis result of a signal analysis device 1 in an embodiment.

[0036] Figure 12 It is a second diagram showing an example of an analysis result of a signal analysis device 1 in an embodiment.

[0037] Figure 13 It is a third diagram showing an example of an analysis result of a signal analysis device 1 in an embodiment.

[0038] Figure 14It is the fourth figure showing an example of the analysis result of the signal analysis device 1 in the embodiment.

[0039] Figure 15 It is the first explanatory figure showing an example of the electrocardiogram of ventricular premature contraction analyzed by the signal analysis device 1 of the embodiment.

[0040] Figure 16 It is the second explanatory figure showing an example of the electrocardiogram of ventricular premature contraction analyzed by the signal analysis device 1 of the embodiment.

[0041] Figure 17 It is the third explanatory figure showing an example of the electrocardiogram of ventricular premature contraction analyzed by the signal analysis device 1 of the embodiment.

[0042] Figure 18 It is the first explanatory figure showing the electrocardiogram of the target heart during the depolarization period of Brugada syndrome type 1 analyzed by the signal analysis device 1 in the embodiment.

[0043] Figure 19 It is the second explanatory figure showing the electrocardiogram of the target heart during the depolarization period of Brugada syndrome type 1 analyzed by the signal analysis device 1 in the embodiment.

[0044] Figure 20 It is the third explanatory figure showing the electrocardiogram of the target heart during the depolarization period of Brugada syndrome type 1 analyzed by the signal analysis device 1 in the embodiment.

[0045] Figure 21 It is a figure showing the first example of the electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0046] Figure 22 It is a figure showing the second example of the electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0047] Figure 23 It is a figure showing the third example of the electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0048] Figure 24 It is a figure showing the fourth example of the electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0049] Figure 25 It is a figure showing the fifth example of the electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0050] Figure 26 It is a figure showing the sixth example of the electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0051] Figure 27It is a diagram showing the seventh example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0052] Figure 28 It is a diagram showing the eighth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0053] Figure 29 It is a diagram showing the ninth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0054] Figure 30 It is a diagram showing the tenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0055] Figure 31 It is a diagram showing the eleventh example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0056] Figure 32 It is a diagram showing the twelfth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0057] Figure 33 It is a diagram showing the thirteenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0058] Figure 34 It is a diagram showing the fourteenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0059] Figure 35 It is a diagram showing the fifteenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0060] Figure 36 It is a diagram showing the sixteenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0061] Figure 37 It is a diagram showing the seventeenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0062] Figure 38 It is a diagram showing the eighteenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0063] Figure 39 It is a diagram showing the nineteenth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0064] Figure 40 It is a diagram showing the twentieth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0065] Figure 41It is a diagram showing the twenty-first example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0066] Figure 42 It is a diagram showing the twenty-second example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0067] Figure 43 It is a diagram showing the twenty-third example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0068] Figure 44 It is a diagram showing the twenty-fourth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0069] Figure 45 It is a diagram showing the twenty-fifth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0070] Figure 46 It is a diagram showing the twenty-sixth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0071] Figure 47 It is a diagram showing the twenty-seventh example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0072] Figure 48 It is a diagram showing the twenty-eighth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0073] Figure 49 It is a diagram showing the twenty-ninth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0074] Figure 50 It is a diagram showing the thirtieth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0075] Figure 51 It is a diagram showing the thirty-first example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0076] Figure 52 It is a diagram showing the thirty-second example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0077] Figure 53 It is a diagram showing the thirty-third example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0078] Figure 54 It is a diagram showing the thirty-fourth example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment.

[0079] Figure 55 This is a diagram showing the thirty-fifth example in which an electrocardiogram is analyzed by the signal analysis device 1 in the embodiment.

[0080] Figure 56 This is a diagram showing the thirty-sixth example in which an electrocardiogram is analyzed by the signal analysis device 1 in the embodiment.

[0081] Figure 57 This is a diagram showing the thirty-seventh example in which an electrocardiogram is analyzed by the signal analysis device 1 in the embodiment.

[0082] Figure 58 This is a diagram showing the thirty-eighth example in which an electrocardiogram is analyzed by the signal analysis device 1 in the embodiment.

[0083] Figure 59 This is a diagram showing the thirty-ninth example in which an electrocardiogram is analyzed by the signal analysis device 1 in the embodiment. Detailed Embodiment

[0084] Figure 1 This is a diagram showing an example of the hardware structure of the signal analysis device 1 in the embodiment. Hereinafter, for simplicity of explanation, the case where the signal analysis device 1 analyzes the waveform of one channel of the electrocardiogram will be taken as an example. However, the signal analysis device 1 is not limited to the waveform of the electrocardiogram and can perform the same analysis based on the time-series biological information related to the heartbeat. Further, the time-series biological information related to the heartbeat is, for example, a waveform showing the change in the cardiac potential, a waveform showing the change in the pressure of the heart, a waveform showing the change in the blood flow, and a waveform showing the change in the heart sound. Therefore, the signal analysis device 1 can use any waveform as long as it is a waveform showing the cardiac cycle obtained from a certain point (whether on the body surface or inside the body, etc.) using sensors in contact with the body surface, sensors close to the body surface, sensors inserted into the body, sensors implanted in the body, etc.

[0085] That is, the signal analysis device 1 can use the waveform showing the change in the pressure of the heart instead of the waveform of the electrocardiogram as the time-series biological information related to the heartbeat. In addition, the signal analysis device 1 can use the waveform showing the change in the blood flow instead of the waveform of the electrocardiogram as the time-series biological information related to the heartbeat. In addition, the signal analysis device 1 can also use the waveform showing the change in the heart sound instead of the waveform of the electrocardiogram as the time-series biological information related to the heartbeat. Further, the waveform of the electrocardiogram is also an example of the time-series biological information related to the heartbeat. Further, the time-series biological information related to the heartbeat can also be the time-series biological information related to the periodic pulsation of the heart.

[0086] The signal analysis device 1 acquires the waveform of an electrocardiogram of the heart to be analyzed (hereinafter referred to as the "target heart"). Based on the acquired waveform of the electrocardiogram, the signal analysis device 1 acquires a parameter indicating the activity of the myocardium of the target heart (hereinafter referred to as the "myocardial activity parameter"), which includes at least any one of a parameter indicating the activity of the layer outside the myocardium of the target heart (hereinafter referred to as the "outer myocardial layer parameter") and a parameter indicating the activity of the layer inside the myocardium of the target heart (hereinafter referred to as the "inner myocardial layer parameter").

[0087] Here, the relationship between the activity of the myocardium and the waveform of the electrocardiogram will be described. In the medical field, a model called the cardiac electromotive force dipole model (Reference 1) that explains the relationship between myocardial movement and the electrocardiogram is known. According to the cardiac electromotive force dipole model, the myocardium is modeled by two layers, the outer myocardial layer and the inner myocardial layer.

[0088] Reference 1: Yoshifumi Tanaka, "Understanding the Electrocardiogram Waveform from the Beginning: Deciphering the Action Potentials of the Myocardium", Gakken Medical Shujunsha (2012).

[0089] In the cardiac electromotive force dipole model, the outer myocardial layer and the inner myocardial layer are respectively modeled as sources of different electromotive forces. According to the cardiac electromotive force dipole model, the composite wave of the epicardial side myocardial action potential and the endocardial side myocardial action potential is roughly consistent with the time change of the potential on the body surface observed on the body surface. The graph representing the time change of the potential on the body surface is the waveform of the electrocardiogram. The epicardial side myocardial action potential is the result of directly measuring the change in the electromotive force generated by the pulsation of the outer myocardial layer by inserting a catheter electrode. The endocardial side myocardial action potential is the result of directly measuring the change in the electromotive force generated by the pulsation of the inner myocardial layer by inserting a catheter electrode. The above is a schematic description of the cardiac electromotive force dipole model.

[0090] However, the outer myocardial layer in the cardiac electromotive force dipole model is a collection of cells. Therefore, the timing of the pulsation of the cells in the outer myocardial layer during one pulsation of the outer myocardial layer is not necessarily the same in all cells, and there may be a distribution in the timing of the pulsation. The same is true for the inner myocardial layer. That is, the timing of the pulsation of the cells in the inner myocardial layer during one pulsation of the inner myocardial layer is not necessarily the same in all cells, and there may be a distribution in the timing of the pulsation. However, the possibility of such a distribution in the timing of the pulsation of the cells is not envisaged in the cardiac electromotive force dipole model.

[0091] In addition, there is also a distribution in the distances between the respective cells and the electrodes located on the body surface, and the structures of the body tissues between the respective cells and the electrodes located on the body surface are 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 is not necessarily 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 is not necessarily 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 possibility of a distribution in the conversion efficiency of the pulsation of the cells reflected in the waveform of the electrocardiogram is not assumed in the cardiac electromotive force dipole model.

[0092] In the signal analysis device 1, considering the possibility of a distribution in the timing of the pulsation of the cells and the possibility of a distribution in the conversion efficiency of the pulsation of the cells reflected in the waveform of the electrocardiogram, an analysis is performed assuming that each of the following is a Gaussian distribution: the distribution of the timing at which the start of the pulsation of each cell in the outer layer of the myocardium is manifested in the waveform of the electrocardiogram, the distribution of the timing at which the start of the pulsation of each cell in the inner layer of the myocardium is manifested in the waveform of the electrocardiogram, the distribution of the timing at which the end of the pulsation of each cell in the outer layer of the myocardium is manifested in the waveform of the electrocardiogram, and the distribution of the timing at which the end of the pulsation of each cell in the inner layer of the myocardium is manifested in the waveform of the electrocardiogram. That is, in the signal analysis device 1, an analysis is performed assuming the following: the start of the activity of the inner layer of the myocardium caused by all the cells in the inner layer of the myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, the start of the activity of the outer layer of the myocardium caused by all the cells in the outer layer of the myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, the end of the activity of the inner layer of the myocardium caused by all the cells in the inner layer of the myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, and the end of the activity of the outer layer of the myocardium caused by all the cells in the outer layer of the myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution.

[0093] Furthermore, in the signal analysis device 1, the cumulative Gaussian distribution function can be used, but the Sigmoid function, Gompertz function, logistic 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 that monotonically increases before the value reaches the maximum value and monotonically decreases after the value reaches 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 the unimodal distribution that is the cumulative source of the cumulative distribution function. Hereinafter, the parameter representing the shape of the cumulative distribution function (that is, the parameter that determines the cumulative distribution function) is called the shape parameter of the cumulative distribution function, and the parameter representing the shape of the unimodal distribution (that is, the parameter that determines the unimodal distribution) is called the shape parameter of the unimodal distribution. However, of course, the shape parameter of the cumulative distribution function and the shape parameter of the unimodal distribution are substantially the same. For example, if the cumulative distribution function used in the signal analysis device 1 is a cumulative Gaussian distribution function, the standard deviation (or variance) and mean of the Gaussian distribution that is the cumulative source of the cumulative Gaussian distribution function are the shape parameters of the unimodal distribution and also the shape parameters of the cumulative distribution function.

[0094] The signal analysis device 1 uses, as the object time waveform, the waveform in the time interval of either the R wave or the T wave included in the waveform of one cycle amount of the electrocardiogram of the target heart obtained, and uses the time waveform (hereinafter referred to as the "approximate time waveform") generated by the difference or weighted difference between the first cumulative distribution function that is the cumulative distribution function of the first unimodal distribution and the second cumulative distribution function that is the cumulative distribution function of the second unimodal distribution to approximate the object time waveform. 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 representing the characteristics of the object time waveform, that is, as myocardial activity parameters. Hereinafter, approximating the object time waveform by the approximate time waveform, that is, determining the approximate time waveform is called "fitting", and the first cumulative distribution function and the second cumulative distribution function included in the approximate time waveform are called "fitting results". Furthermore, in the case of approximating by weighted difference, the signal analysis device 1 can obtain both the weight given to the first cumulative distribution function and the weight given to the second cumulative distribution function as parameters representing the characteristics of the object time waveform (that is, myocardial activity parameters), or can also obtain the ratio of the weight given to the first cumulative distribution function to the weight given to the second cumulative distribution function as a parameter representing the characteristics of the object time waveform (that is, myocardial activity parameters).

[0095] In the case where an object time waveform is approximated by an approximate time waveform generated from the difference between a first cumulative distribution function and a second cumulative distribution function, for example, the signal analysis device 1 generates a time waveform (hereinafter referred to as "candidate time waveform") generated from the difference between the first cumulative distribution function and the second cumulative distribution function by using, respectively, combinations (M×N types) of parameters for determining the cumulative distribution function for each of the plurality of (M) candidates for the first cumulative distribution function and parameters for determining the cumulative distribution function for each of the plurality of (N) candidates for the second cumulative distribution function. The candidate time waveform among the generated M×N types of candidate time waveforms that is closest to the object time waveform is determined as the approximate time waveform, and the parameters for the candidate for determining the first cumulative distribution function and the parameters for the candidate for determining the second cumulative distribution function used in the generation of the determined approximate time waveform are obtained 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 squared error between the candidate time waveform and the object time waveform.

[0096] Alternatively, for example, the signal analysis device 1 repeats the following operations until the squared error becomes below a specified reference or repeats the following operations a specified number of times. The operations are: obtaining a candidate time waveform and updating at least any one of the parameters for determining each cumulative distribution function in a direction in which the squared error between the candidate time waveform and the object time waveform becomes smaller. The candidate time waveform is a time waveform generated from the difference between a candidate for the first cumulative distribution function approximating the object time waveform and a candidate for the second cumulative distribution function. Thus, the finally obtained candidate time waveform is determined as the approximate time waveform, and the parameters for the candidate for determining the first cumulative distribution function and the parameters for the candidate for determining the second cumulative distribution function used in the generation of the determined approximate time waveform are obtained as parameters representing the characteristics of the object time waveform.

[0097] In the case where the object time waveform is approximated by the approximate time waveform generated from the weighted difference between the first cumulative distribution function and the second cumulative distribution function, for example, the signal analysis device 1 respectively uses combinations (K×L×M×N types) of parameters for determining the cumulative distribution function for each of the multiple (M) candidates for the first cumulative distribution function, parameters for determining the cumulative distribution function for each of the multiple (N) candidates for the second cumulative distribution function, multiple (K) candidates for the weight assigned to the first cumulative distribution function, and multiple (L) candidates for the weight assigned to the second cumulative distribution function, to generate candidate time waveforms that are time waveforms generated from the weighted difference between the first cumulative distribution function and the second cumulative distribution function. The candidate time waveform that is closest to the object time waveform among the generated K×L×M×N types of candidate time waveforms is determined as the approximate time waveform, and 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 the generation of the determined approximate time waveform are obtained as parameters representing the characteristics of the object time waveform.

[0098] Alternatively, for example, the signal analysis device 1 repeats the following operation until the squared error becomes below a specified criterion or repeats the following operation a specified number of times. The operation is: obtaining a candidate time waveform, and updating 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 squared error between the candidate time waveform and the object time waveform becomes smaller. The candidate time waveform is a time waveform generated from the weighted difference between a candidate for the first cumulative distribution function that approximates the object time waveform and a candidate for the second cumulative distribution function. Thus, the finally obtained candidate time waveform is determined as the approximate time waveform, and 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 the generation of the determined approximate time waveform are obtained as parameters representing the characteristics of the object time waveform.

[0099] Hereinafter, the process of obtaining parameters representing the characteristics of the object time waveform will be referred to as the myocardial activity information parameter acquisition process, where the object time waveform includes a waveform of the quantity in one cycle of the electrocardiogram of the acquired object heart.

[0100] If the information representing time is set as x, in the case of using Gaussian distributions as the first unimodal distribution and the second unimodal distribution, the first unimodal distribution is represented by the following formula (1), the first cumulative distribution function f1(x) is represented by formula (2), the second unimodal distribution is represented by formula (3), and the second cumulative distribution function f2(x) is represented by formula (4).

[0101] [Mathematical formula 1]

[0102]

[0103] [Equation 2]

[0104]

[0105] [Equation 3]

[0106]

[0107] [Equation 4]

[0108]

[0109] Equation (1) is a Gaussian distribution (normal distribution) with a mean of μ1 and a standard deviation of σ1 (variance of σ12). Equation (3) is a Gaussian distribution (normal distribution) with a mean of μ2 and a standard deviation of σ2 (variance of σ22). Equation (2) is the cumulative distribution function of Equation (1). Equation (4) is the cumulative distribution function of Equation (3). "erf" is the Sigmoid function (error function). The unit of the information x representing time is arbitrary. For example, it is sufficient to use the sample number starting from the waveform of the amount of one cycle of the electrocardiogram or the relative time as the information x representing time.

[0110] The difference between the first cumulative distribution function and the second cumulative distribution function is represented, for example, by 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.

[0111] [Equation 5]

[0112]

[0113] That is, when approximating the target time waveform by the approximate time waveform that is the difference between the first cumulative distribution function and the second cumulative distribution function, the parameters for determining the first unimodal distribution or the parameters for determining the first cumulative distribution function, namely the mean μ1 and the standard deviation σ1, and the parameters for determining the second unimodal distribution or the second cumulative distribution function, namely the mean μ2 and the standard deviation σ2, are obtained as the parameters representing the characteristics of the target time waveform. Furthermore, instead of taking the standard deviation as a parameter, the variance can be taken as a parameter. This also applies to the subsequent descriptions where the standard deviation is taken as a parameter.

[0114] The weighted difference between the first cumulative distribution function and the second cumulative distribution function is represented, for example, by the following Equation (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 represented by the following Equation (6) is a function obtained by subtracting the function obtained by multiplying the second cumulative distribution function by the weight k2 from the function obtained by multiplying the first cumulative distribution function by the weight k1.

[0115] [Mathematical Formula 6]

[0116]

[0117] That is, when approximating an object time waveform by an approximate time waveform that is a weighted difference between a first cumulative distribution function and a second cumulative distribution function, at least the parameters for determining the first unimodal distribution or the parameters for determining the first cumulative distribution function, namely the mean μ1 and the standard deviation σ1, and the parameters for determining the second unimodal distribution or the parameters for determining the second cumulative distribution function, namely the mean μ2 and the standard deviation σ2, are obtained as parameters representing the characteristics of the object time waveform. Further, 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 to the weight k2 of the second cumulative distribution function, may also be obtained as a parameter representing the characteristics of the object time waveform.

[0118] If the function in Equation (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, then both the weight k1 and the weight k2 are positive values. However, in the case where the object heart is in a specific state, it cannot be denied that at least one of the weights k1 and k2 obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can be fitted so that both the weight k1 and the weight k2 are positive values, it is not necessary to fit so that both the weight k1 and the weight k2 are positive values.

[0119] Further, the signal analysis device 1 can take the R wave and the T wave included in the waveform of one cycle amount of the electrocardiogram of the object heart obtained, with the R wave as the first object time waveform and the T wave as the second object time waveform, and respectively 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.

[0120] For example, in the case of approximating the first object time waveform (i.e., the R wave) by the difference between the first cumulative distribution function and the second cumulative distribution function, the first object time waveform is approximated by the approximate time waveform in Equation (9), and the parameters for determining the first cumulative distribution function, namely the mean μ a and the standard deviation σ a , and the parameters for determining the second cumulative distribution function, namely the mean μ b and the standard deviation σ b are obtained as parameters representing the characteristics of the first object time waveform (i.e., the R wave), and Equation (9) is a function obtained by subtracting the second cumulative distribution function f a (x) expressed by Equation (7) from the first cumulative distribution function f b (x) expressed by Equation (8).

[0121] [Equation 7]

[0122]

[0123] [Equation 8]

[0124]

[0125] [Equation 9]

[0126]

[0127] For example, in the case of approximating the first object time waveform (i.e., the R wave) by the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the first object time waveform is approximated by the approximate time waveform of Equation (10), and at least the parameters of the first cumulative distribution function, namely the mean μ a and the standard deviation σ a , and the parameters of the second cumulative distribution function, namely the mean μ b and the standard deviation σ b are obtained as parameters representing the characteristics of the first object time waveform (i.e., the R wave). Equation (10) is obtained by subtracting the function obtained by multiplying the second cumulative distribution function f a (x) by the weight k a from the function obtained by multiplying the first cumulative distribution function f b (x) by the weight k b . Furthermore, the weight k a of the first cumulative distribution function and the weight k b of the second cumulative distribution function, or the ratio of the weight k a of the first cumulative distribution function to the weight k b of the second cumulative distribution function (k a / k b or k b / k a ) can also be obtained as parameters representing the characteristics of the parameters representing the characteristics of the first object time waveform (i.e., the R wave).

[0128] [Equation 10]

[0129]

[0130] If it is considered that the function of Equation (10) is obtained by subtracting the function obtained by multiplying the second cumulative distribution function by the weight k a from the function obtained by multiplying the first cumulative distribution function by the weight k b , then the weight k a and the weight k bare all positive values. However, in the case where the target heart is in a specific state, it cannot be denied that at least one of the weights k a and the weight k b is not a positive value. Therefore, although the signal analysis device 1 can fit such that the weight k a and the weight k b are both positive values, it is not necessary to fit such that the weight k a and the weight k b are both positive values.

[0131] Since the R wave corresponds to the excitation of all cells of the myocardium starting sequentially according to a Gaussian distribution, for the R wave, 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 as described above. On the other hand, if it is considered that the T wave corresponds to the awakening of all cells of the myocardium from the excited state sequentially according to a Gaussian distribution, then it can be interpreted that the T wave is a phenomenon in the opposite direction to the R wave on the time axis. That is, for the T wave, it is sufficient to approximate the waveform obtained by reversing the time axis of the object time waveform by the difference or weighted difference of the cumulative Gaussian distributions. Hereinafter, this will be referred to as the first method. In addition, if it is considered that the T wave corresponds to the awakening of all cells of the myocardium from the excited state according to a Gaussian distribution, then for the T wave, it can also be said that it is sufficient to approximate the object time waveform in the positive direction of time by the difference or weighted difference of two functions (the function obtained by subtracting the cumulative Gaussian distribution from 1). Hereinafter, this will be referred to as the second method. Hereinafter, specific examples of the first method and the second method will be described. In order to avoid confusion between the cumulative distribution function of the R wave described above and the cumulative distribution function of the T wave described below, hereinafter, for the T wave, 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, and the description will be made accordingly.

[0132] When approximating the second object time waveform (i.e., the T wave) by the difference between the third cumulative distribution function and the fourth cumulative distribution function using the first method, the information indicating the time in the reverse direction is set as x’, the waveform obtained by reversing the time axis of the second object time waveform is called the second object reverse time waveform, the second object reverse time waveform is approximated by the approximate reverse time waveform of Equation (13), and the parameters determining the third cumulative distribution function, namely the mean μ e and the standard deviation σ e , and the parameters determining the fourth cumulative distribution function, namely the mean μ g and the standard deviation σ g are obtained as the parameters representing the characteristics of the second object time waveform (i.e., the T wave). Equation (13) is obtained by subtracting the fourth cumulative distribution function f e (x’) expressed by Equation (12) from the third cumulative distribution function f g(x’) and obtained function.

[0133] [Equation 11]

[0134]

[0135] [Equation 12]

[0136]

[0137] [Equation 13]

[0138]

[0139] For example, in the case of approximating the second object time waveform (i.e., the T wave) by the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function using the first method, the second object inverse time waveform is approximated by the approximate inverse time waveform of Equation (14), and at least the parameters of the third cumulative distribution function, namely the mean μ e and the standard deviation σ e as well as the parameters of the fourth cumulative distribution function, namely the mean μ g and the standard deviation σ g are obtained as the parameters representing the characteristics of the second object time waveform (i.e., the T wave). Equation (14) is a function obtained by subtracting the function obtained by multiplying the fourth cumulative distribution function f e (x’) by the weight k e from the function obtained by multiplying the third cumulative distribution function f g (x’) by the weight k g Furthermore, the weight k of the third cumulative distribution function e and the weight k of the fourth cumulative distribution function g or the ratio of the weight k of the third cumulative distribution function e to the weight k of the fourth cumulative distribution function g (k e / k g or k g / k e ) can also be obtained as the parameter representing the characteristics of the second object time waveform (i.e., the T wave).

[0140] [Equation 14]

[0141]

[0142] If the function of Equation (14) is obtained by subtracting the function obtained by multiplying the fourth cumulative distribution function by the weight k e from the function obtained by multiplying the third cumulative distribution function by the weight k g judging from this, the weight k e and the weight kg are all positive values. However, in the case where the subject's heart is in a specific state, it cannot be denied that at least one of the weights k e and the weight k g obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can fit so that the weight k e and the weight k g are both positive values, it is not necessary to fit so that the weight k e and the weight k g are both positive values.

[0143] For example, when approximating the second object time waveform (i.e., the T wave) by using the difference between the function f' c (x) obtained by subtracting the third cumulative distribution function f c (x) expressed by Equation (15) from 1 (hereinafter referred to as the "third inverse cumulative distribution function") and the function f' d (x) obtained by subtracting the fourth cumulative distribution function f d (x) expressed by Equation (16) from 1 (hereinafter referred to as the "fourth inverse cumulative distribution function") according to the second method, the second object time waveform is approximated by the approximate time waveform of Equation (17), and the parameters of the third cumulative distribution function, i.e., the mean μ c and the standard deviation σ c , and the parameters of the fourth cumulative distribution function, i.e., the mean μ d and the standard deviation σ d are obtained as the parameters representing the characteristics of the second object time waveform (i.e., the T wave). Equation (17) is a function obtained by subtracting the fourth inverse cumulative distribution function f' c (x) from the third inverse cumulative distribution function f' d (x).

[0144] [Mathematical formula 15]

[0145]

[0146] [Mathematical formula 16]

[0147]

[0148] [Mathematical formula 17]

[0149]

[0150] For example, when approximating the second object time waveform (i.e., the T wave) by using the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function according to the second method, the second object time waveform is approximated by the approximate time waveform of Equation (18), and the parameters of the third cumulative distribution function, i.e., the mean μ cand standard deviation σ c and determining the parameters of the fourth cumulative distribution function, namely the mean μ d and standard deviation σ d is obtained as a parameter representing the characteristics of the second object time waveform (i.e., the T wave), and the formula (18) is obtained by subtracting the function obtained by multiplying the fourth inverse cumulative distribution function f’ c (x) by the weight k c from the function obtained by multiplying the third inverse cumulative distribution function f’ d (x) by the weight k d Moreover, the weight k of the third inverse cumulative distribution function c and the weight k of the fourth inverse cumulative distribution function d or the ratio (k c / k d or k c / k d or k d / k c ) of the weight k of the third inverse cumulative distribution function and the weight k of the fourth inverse cumulative distribution function can also be obtained as a parameter representing the characteristics of the second object time waveform (i.e., the T wave).

[0151] [Equation 18]

[0152]

[0153] If the function of formula (18) is obtained by subtracting the function obtained by multiplying the fourth inverse cumulative distribution function by the weight k c from the function obtained by multiplying the third inverse cumulative distribution function by the weight k d , both the weight k c and the weight k d are positive values. However, in the case where the object heart is in a specific state, it cannot be denied that at least one of the weights k c and the weight k d obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can fit so that both the weight k c and the weight k d are positive values, it is not necessary to fit so that both the weight k c and the weight k d are positive values.

[0154] Moreover, since the approximate time waveform of formula (17) is obtained by subtracting the third cumulative distribution function f d (x) from the fourth cumulative distribution function f c (x), it is the function obtained by subtracting the third cumulative distribution function f c (x) from the fourth cumulative distribution function f d(x), and the approximate time waveform of Equation (18) is obtained by adding a constant term to the weighted difference between the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d (x). The shape of the curve part is the same as the weighted difference between the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d (x). In addition, as described above, the T wave can be interpreted as a phenomenon in the opposite direction to the R wave on the time axis. For the T wave, the waveform obtained by reversing the time axis of the target time waveform can be approximated by the difference or weighted difference between the third cumulative distribution function f e (x) and the fourth cumulative distribution function f g (x). Therefore, in the following description of the T wave, sometimes only the cumulative distribution function is used without recording the cumulative distribution function and the inverse cumulative distribution function together.

[0155] Figure 2 Schematically shows the function k a obtained by multiplying the first cumulative distribution function f a by the weight k a f a (x), the function k b obtained by multiplying the second cumulative distribution function f b by the weight k b f b (x), and the approximate time waveform k a f a (x) - k b f b (x), which is the weighted difference between the first cumulative distribution function and the second cumulative distribution function. The single-dot dash line is the function k a obtained by multiplying the first cumulative distribution function f a by the weight k a f a (x), the double-dot dash line is the function k b obtained by multiplying the second cumulative distribution function f b by the weight k b f b (x), and the dotted line is the approximate time waveform k a f a (x) - k b f b (x). This approximate time waveform k a f a (x) - k b f b (x) is the waveform that approximates the first object time waveform (i.e., the R wave).

[0156] Figure 3 Schematically shows the third inverse cumulative distribution function f' c (x) = 1 - f c multiplied by the weight k c of the function k c f' c (x), the fourth inverse cumulative distribution function f' d (x) = 1 - f d multiplied by the weight k d of the function k d f' d (x), the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the approximate time waveform k c f' c (x) - k d f' d (x). The single dotted line is the third inverse cumulative distribution function f' c (x) = 1 - f c multiplied by the weight k c of the function k c f' c (x), the double dotted line is the fourth inverse cumulative distribution function f' d (x) = 1 - f d multiplied by the weight k d of the function k d f' d (x), the dashed line is the approximate time waveform k c f' c (x) - k d f' d (x). This approximate time waveform k c f' c (x) - k d f' d (x) is a waveform that approximates the second object time waveform (i.e., the T wave).

[0157] Figure 4 Schematically shows a graph of the results of fitting each of the first object time waveform, which is the R wave included in the waveform of the amount of one cycle of the electrocardiogram of the object heart in the embodiment, and the second object time waveform, which is the T wave, by the difference between two cumulative distribution functions. Figure 4 The horizontal axis represents time, and the vertical axis represents potential. The units of both the horizontal axis and the vertical axis are arbitrary units (arbitrary unit).

[0158] Specifically, Figure 4This is an example where the first object time waveform (i.e., the R wave) is fitted by the difference between the first cumulative distribution function and the second cumulative distribution function, and the second object time waveform (i.e., the T wave) is fitted 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 that of the second cumulative distribution function, which is from time T1 to time T3 of the time interval of the first object time waveform (i.e., the R wave). The domain of the third cumulative distribution function is the same as that of the fourth cumulative distribution function, which is from time T4 to time T6.

[0159] Figure 4 The "first fitting result" and "second fitting result" in Figure 4 are the fitting results for the R wave.

[0160] In Figure 4 , the "first fitting result" shows the first cumulative distribution function in the fitting result of the electrocardiogram to the first object time waveform (i.e., the R wave). In Figure 4 , the "second fitting result" shows the second cumulative distribution function in the fitting result of the electrocardiogram to the first object time waveform (i.e., the R wave). In Figure 4 , the "third fitting result" shows the third cumulative distribution function in the fitting result of the electrocardiogram to the second object time waveform (i.e., the T wave). In Figure 4 , the "fourth fitting result" shows the fourth cumulative distribution function in the fitting result of the electrocardiogram to the second object time waveform (i.e., the T wave). In Figure 4 , the "potential on the body surface" represents the waveform of the electrocardiogram of the fitting object.

[0161] Furthermore, in the signal analysis device 1, for the period from time T3 to time T4 that neither belongs to the time interval of the first object time waveform (i.e., the R wave) nor belongs to the time interval of the second object time waveform (i.e., the T wave), the signal analysis device 1 does not perform fitting. Furthermore, for the period when the signal analysis device 1 does not perform fitting, in Figure 4 , it is represented by the line connecting the first fitting result at time T3 and the third fitting result at time T4, and the line connecting the second fitting result at time T3 and the fourth fitting result at time T4. That is, when the signal analysis device 1 displays the fitting result, as Figure 4 shown, the signal analysis device 1 only needs to display the line connecting the first fitting result at time T3 and the third fitting result at time T4, and the line connecting the second fitting result at time T3 and the fourth fitting result at time T4 by a pre-determined function such as a constant function or a linear function.

[0162] Furthermore, when the signal analysis device 1 displays the fitting results, the weight value can also 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, although the actual first fitting result at time T3 is k a f a (T3), and the actual third fitting result at time T4 is k c f' c (T4), but α1 that satisfies k a f a (T3) = α1k c f' c (T4) can be obtained, and α1k c is used to replace the weight k c to display the fitting results, or k a / α1 can also be used to replace the weight k a to display the fitting results. 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, although the second fitting result at time T3 is k b f b (T3), and the fourth fitting result at time T4 is k d f’ d (T4), but α2 that satisfies k b f b (T3) = α2k d f’ d (T4) can be obtained, and α2k d is used to replace the weight k d to display the fitting results, or k b / α2 can also be used to replace the weight k b to display the fitting results.

[0163] Furthermore, the fitting of the first object time waveform and the fitting of the second object time waveform do not have to be performed separately. That is, the fitting of the first object time waveform and the second object time waveform can also be collectively performed as the fitting of both. For example, when the signal analysis device 1 collectively performs the fitting of the first object time waveform and the second object time waveform, it can perform a fitting that also takes into account reducing the difference between the first fitting result at time T3 and the third fitting result at time T4, and reducing the difference between the second fitting result at time T3 and the fourth fitting result at time T4.

[0164] Figure 5It is an explanatory diagram showing that the T wave is approximated by a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function, and showing the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or showing the parameters for determining each cumulative distribution function, thereby enabling visualization of the typical characteristics of the T wave. Figure 5 Four images, namely image G1, image G2, image G3, and image G4, are shown. Each of the images G1 to G4 shows a graph with the horizontal axis representing time and the vertical axis representing potential. Figure 5 For each of the images G1 to G4, the units of the horizontal axis and the vertical axis are both arbitrary units.

[0165] Figure 5 The "first function" is an example of the third inverse cumulative distribution function. Figure 5 The "second function" is an example of the fourth inverse cumulative distribution function. Figure 5 The "third function" 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 "third function" has a shape that is roughly the same as the shape of the T wave of a normal heart.

[0166] Figure 5 The "fourth function" 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" 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 "sixth function" has a shape that is roughly the same as the shape of the reduced height of the T wave in one of the three typical patterns of abnormal T waves. Figure 5 The interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in image G2 is narrower than the interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in image G1 of a normal heart. Thus, in the case of reduced height of the T wave, the delay between the activities of the outer layer of the myocardium and the inner layer of the myocardium is visualized as being smaller.

[0167] Figure 5 The "seventh function" is an example of the third inverse cumulative distribution function. Figure 5 The "eighth function" is an example of the fourth inverse cumulative distribution function. Figure 5 The "ninth function" 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 5The "ninth function" has a shape that is roughly the same as the shape of the elevated T wave, which is one of the three typical patterns of T wave abnormalities. Figure 5 The interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G3 is wider than the interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G1 of a normal heart. Thus, in the case of an elevated T wave, the delay in the activity of the outer layer of the myocardium and the activity of the inner layer of the myocardium is visualized to be larger.

[0168] Figure 5 The "tenth function" 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" represents the function obtained by subtracting the "eleventh function" from the "tenth function", that is, the function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The "twelfth function" has a shape that is roughly the same as the shape of the negative T wave, which is one of the three typical patterns of T wave abnormalities. Figure 5 The order of the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G4 is opposite to the order of the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G1 of a normal heart. Thus, in the case of a negative T wave, it is visualized that the outer layer of the myocardium finishes its activity earlier than the inner layer of the myocardium.

[0169] The function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can represent a wave with a different width in the vertical axis direction and the horizontal axis direction, such as the "third function", the "sixth function", and the "ninth function". In addition, the function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can represent a negative wave, 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 approximating the T wave with the function obtained by subtracting the fourth cumulative distribution function from the third cumulative distribution function after time axis inversion, the activities of the inner layer and the outer layer of the myocardium included in the T wave and the relationship between the activities of the inner layer and the outer layer of the myocardium can be represented. This is the same in the case of approximating the R wave with the function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0170] In this way, the signal analysis device 1 fits the waveform of the R wave or the T wave of the electrocardiogram of the target heart by the difference or weighted difference of two cumulative distribution functions. Then, the signal analysis device 1 obtains the parameters that determine the approximate time waveform as the parameters showing the activity of the myocardium, and the approximate time waveform is determined by fitting.

[0171] [Approximation by adding a value to the difference or weighted difference between two cumulative distribution functions]

[0172] The signal analysis device 1 can use, as the target time waveform, the waveform of the time interval of either the R wave or the T wave included in the waveform of the quantity of one cycle of the electrocardiogram of the target heart obtained, and use, as the approximate time waveform, the time waveform obtained by adding a value (hereinafter referred to as the "level value") to the difference or weighted difference between the first cumulative distribution function, which is the cumulative distribution function of the first unimodal distribution, and the second cumulative distribution function, which is the cumulative distribution function of the second unimodal distribution. In this case, in addition to the parameters for determining the first unimodal distribution or the parameters for determining the first cumulative distribution function, and the parameters for determining the second unimodal distribution or the parameters for determining the second cumulative distribution function when approximating the target time waveform with the approximate time waveform, the level value is also obtained as a parameter representing the characteristics of the target time waveform. Of course, in the case of approximation using the weighted difference, either the weights assigned to the first cumulative distribution function and the weights assigned to the second cumulative distribution function can be obtained as parameters representing the characteristics of the target time waveform, or the ratio of the weights assigned to the first cumulative distribution function to the weights assigned to the second cumulative distribution function can be obtained as a parameter representing the characteristics of the target time waveform.

[0173] If, in the case of using the weighted difference, for example, the signal analysis device 1 uses combinations (J×K×L×M×N types) of multiple (M) candidates for the parameters of the cumulative distribution function for each of the multiple (M) candidates for the first cumulative distribution function, multiple (N) candidates for the parameters of the cumulative distribution function for each of the multiple (N) candidates for the second cumulative distribution function, multiple (K) candidates for the weights assigned to the first cumulative distribution function, multiple (L) candidates for the weights assigned to the second cumulative distribution function, and multiple (J) candidates for the level value, to generate candidate time waveforms that are the results of adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function and the level value, determines the candidate time waveform that is closest to the target time waveform among the J×K×L×M×N types of candidate time waveforms generated as the approximate time waveform, and obtains the parameters of the candidate for determining the first cumulative distribution function, the parameters of the candidate for determining the second cumulative distribution function, the weights assigned to the first cumulative distribution function, the weights 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 target time waveform.

[0174] Alternatively, for example, the signal analysis device 1 repeats the following operations until the squared error becomes less than a specified reference or repeats the following operations a specified number of times. The operations are as follows: obtaining a candidate time waveform, and updating at least any one of the parameters that determine each cumulative distribution function, the weights assigned to each cumulative distribution function, and the level value in a direction in which the squared error between the candidate time waveform and the target time waveform becomes smaller. The candidate time waveform is a time waveform generated as a result of adding the level value to the weighted difference between a candidate for the first cumulative distribution function approximating the target time waveform and a candidate for the second cumulative distribution function; determining the finally obtained candidate time waveform as the approximate time waveform, and obtaining the parameters that determine the candidate for the first cumulative distribution function, the parameters that determine the candidate for the second cumulative distribution function, the weights assigned to the first cumulative distribution function, the weights assigned to the second cumulative distribution function, and the level value used in the generation of the determined approximate time waveform as the parameters representing the characteristics of the target time waveform.

[0175] Furthermore, the level value can be determined before fitting. In this case, when the target time waveform is an R wave, the signal analysis device 1 first obtains the potential at the start end of the target time waveform (corresponding to Figure 4 the moment T1 in Figure 4 as the level value. When the target time waveform is a T wave, the signal analysis device 1 obtains the potential at the end of the target time waveform (corresponding to

[0176] the moment T6 in Figure 4 as the level value. Then, the signal analysis device 1 uses combinations (K×L×M×N types) of the parameters that determine the cumulative distribution functions for each of the multiple (M) candidates for the first cumulative distribution function, the parameters that determine the cumulative distribution functions for each of the multiple (N) candidates for the second cumulative distribution function, the multiple (K) candidates for the weights assigned to the first cumulative distribution function, and the multiple (L) candidates for the weights assigned to the second cumulative distribution function to generate candidate time waveforms that are time waveforms generated as a result of adding the level value to the weighted difference between the first cumulative distribution function and the second cumulative distribution function. The candidate time waveform that is closest to the target time waveform among the K×L×M×N types of candidate time waveforms generated is determined as the approximate time waveform, and the parameters that determine the candidate for the first cumulative distribution function, the parameters that determine the candidate for the second cumulative distribution function, the weights assigned to the first cumulative distribution function, the weights assigned to the second cumulative distribution function, and the level value determined in the initial process used in the generation of the determined approximate time waveform are obtained as the parameters representing the characteristics of the target time waveform.

[0176] Alternatively, for example, when the target time waveform is an R wave, the signal analysis device 1 first obtains the potential at the start end of the target time waveform (corresponding to Figure 4 the moment T1 inFigure 4 The potential at time T6) in [reference] is obtained as a level value. Then, the signal analysis device 1 repeats the following operations until the squared error becomes below a specified reference or repeats the following operations for a specified number of times. The operations are as follows: obtaining a candidate time waveform, and updating at least one of the parameters that determine each cumulative distribution function and the weights assigned to each cumulative distribution function in the direction in which the squared error between the candidate time waveform and the target time waveform becomes smaller. The candidate time waveform is a time waveform generated by adding the level value to the weighted difference between the candidate of the first cumulative distribution function approximating the target time waveform and the candidate of the second cumulative distribution function; determining the finally obtained candidate time waveform as the approximate time waveform, and obtaining the parameters that determine the candidate of the first cumulative distribution function, the parameters that determine the candidate of the second cumulative distribution function, the weights assigned to the first cumulative distribution function, the weights assigned to the second cumulative distribution function, and the level value determined in the initial process as the parameters representing the characteristics of the target time waveform.

[0177] If the level value is set as β, the value obtained by adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function and the level value is represented by, for example, the following formula (19). The function represented by the following formula (19) is a function obtained by subtracting the function obtained by multiplying the second cumulative distribution function by the weight k2 from the function obtained by multiplying the first cumulative distribution function by the weight k1 and then adding the level value β.

[0178] [Mathematical formula 19]

[0179]

[0180] When approximating the target time waveform with 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, i.e., the mean μ1 and the standard deviation σ1, the parameters that determine the second unimodal distribution or the parameters that determine the second cumulative distribution function, i.e., the mean μ2 and the standard deviation σ2, and the level value β are obtained as the parameters representing the characteristics of the target time waveform. Furthermore, 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 to the weight k2 of the second cumulative distribution function can also be obtained as the parameters representing the characteristics of the target time waveform.

[0181] If the function of Equation (19) is 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, both the weight k1 and the weight k2 are positive values. However, in the case where the target heart is in a specific state, it cannot be denied that at least one of the weights k1 and k2 obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can be fitted so that both the weights k1 and k2 are positive values, it is not necessary to fit so that both the weights k1 and k2 are positive values.

[0182] The signal analysis device 1 can use the R wave in the waveforms of one cycle amount of the electrocardiogram of the target heart obtained as the first target time waveform and the T wave as the second target time waveform among the R wave and the T wave included in the waveform, and respectively obtain the above-described parameters representing the characteristics of the target time waveform for the first target time waveform and the second target time waveform.

[0183] For example, in the case where the first target time waveform (i.e., the R wave) is approximated by the function obtained by adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function and the level value, the potential at the start end of the first target time waveform is set as the level value β R , approximate the first target time waveform by the approximate time waveform of Equation (20), and at least obtain the parameter, i.e., the mean μ a and the standard deviation σ a that determine the first cumulative distribution function, the parameter, i.e., the mean μ b and the standard deviation σ b that determine the second cumulative distribution function, and the level value β R as the parameters representing the characteristics of the first target time waveform (i.e., the R wave). Equation (20) is a function obtained by subtracting the function of the second cumulative distribution function f a (x) multiplied by the weight k a from the function of the first cumulative distribution function f b (x) multiplied by the weight k b and adding the level value β R . Further, the weight k a of the first cumulative distribution function and the weight k b of the second cumulative distribution function, or the ratio (k a / k b or k a / k b ) of the weight k b of the first cumulative distribution function and the weight k a of the second cumulative distribution function may also be obtained as the parameter representing the characteristics of the parameters representing the characteristics of the first target time waveform (i.e., the R wave).

[0184] [Mathematical formula 20]

[0185]

[0186] If the function in Equation (20) is obtained by multiplying the first cumulative distribution function by the weight k a subtracting the function obtained by multiplying the second cumulative distribution function by the weight k b and adding the level value β R in terms of the function, the weight k a and the weight k b are both positive values. However, in the case where the target heart is in a specific state, it cannot be denied that at least one of the weights k a and the weight k b obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can be fitted so that the weight k a and the weight k b are both positive values, it is not necessary to be fitted so that the weight k a and the weight k b are both positive values.

[0187] For example, in the case where the waveform of the time-axis inversion of the second object time waveform (i.e., the T wave), which is the second object inverse time waveform, is approximated by a function obtained by adding the 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 object time waveform is set as the level value β T , the second object inverse time waveform is approximated by the approximate inverse time waveform of Equation (21), and at least the parameters for determining the third cumulative distribution function, i.e., the mean μ e and the standard deviation σ e , the parameters for determining the fourth cumulative distribution function, i.e., the mean μ g and the standard deviation σ g , and the level value β T are obtained as parameters representing the characteristics of the second object time waveform (i.e., the T wave). Equation (21) is a function obtained by subtracting the function obtained by multiplying the fourth inverse cumulative distribution function f e (x’) by the weight k e from the function obtained by multiplying the third cumulative distribution function f g (x’) represented by Equation (11) by the weight k g and adding the level value β T . Furthermore, the weight k e of the third cumulative distribution function and the weight k g of the fourth cumulative distribution function, or the ratio of the weight k e of the third cumulative distribution function to the weight k g of the fourth cumulative distribution function (k e / k g or k g / k e ) Obtain a parameter that represents the characteristics of the parameters representing the characteristics of the second object time waveform (i.e., the T wave).

[0188] [Equation 21]

[0189]

[0190] If the function in Equation (21) is obtained by subtracting the function of the fourth cumulative distribution function multiplied by the weight k e from the function of the third cumulative distribution function multiplied by the weight k g and adding the level value β T judging from the resulting function, the weight k e and the weight k g are both positive values. However, in the case where the target heart is in a specific state, it cannot be denied that at least one of the weights k e and the weights k g is not a positive value. Therefore, although the signal analysis device 1 can be fitted so that the weight k e and the weight k g are both positive values, it is not necessary to fit so that the weight k e and the weight k g are both positive values.

[0191] For example, in the case of approximating the second object time waveform (i.e., the T wave) by the weighted difference of the function obtained by subtracting the fourth cumulative distribution function from 1 (i.e., the fourth inverse cumulative distribution function) and the function obtained by subtracting the third cumulative distribution function from 1 (i.e., the third inverse cumulative distribution function) and adding the level value, the potential at the end of the second object time waveform is set as the level value β T , approximate the second object time waveform by the approximate time waveform of Equation (22), and obtain the parameter mean μ c and standard deviation σ c that determine the third cumulative distribution function, the parameter mean μ d and standard deviation σ d that determine the fourth cumulative distribution function, and the level value β T as parameters representing the characteristics of the second object time waveform (i.e., the T wave). Equation (22) is obtained by subtracting the function of the fourth inverse cumulative distribution function f' c (x) multiplied by the weight k c from the function of the third inverse cumulative distribution function f' d (x) multiplied by the weight k d and adding the level value β T Furthermore, the weight k of the third inverse cumulative distribution function can also bec and the weight k of the fourth inverse cumulative distribution function d 、or the weight k of the third inverse cumulative distribution function c and the weight k of the fourth inverse cumulative distribution function 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 (i.e., the T wave).

[0192] [Equation 22]

[0193]

[0194] If the function in Equation (22) is obtained by subtracting the function of the fourth inverse cumulative distribution function multiplied by the weight k c from the function of the third inverse cumulative distribution function multiplied by the weight k d and adding the level value β T From the perspective of the obtained function, both the weight k c and the weight k d are positive values. However, in the case where the target heart is in a specific state, it cannot be denied that at least one of the weights k c and the weight k d obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can be fitted so that both the weight k c and the weight k d are positive values, it is not necessary to fit so that both the weight k c and the weight k d are positive values.

[0195] Figure 6 Schematically shows the first cumulative distribution function f a of the first object time waveform (i.e., the R wave), multiplied by the weight k a and added with the level value β R to obtain the function k a f a (x)+β R 、the second cumulative distribution function f b (x) multiplied by the weight k b and added with the level value β R to obtain the function k b f b (x)+β R 、the approximate time waveform k R after adding the weighted difference between the first cumulative distribution function and the second cumulative distribution function and the level value β a f a (x)-k b fb (x) + β R The figure of. The single dotted line is the first cumulative distribution function f a (x) multiplied by the weight k a and added with the level value β R The function k of a f a (x) + β R , the double dotted line is the second cumulative distribution function f b (x) multiplied by the weight k b and added with the level value β R The function k of b f b (x) + β R , the dashed 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 is the waveform approximating the first object time waveform (i.e., the R wave).

[0196] Figure 7 Schematically shows the third inverse cumulative distribution function f' with respect to the second object time waveform (i.e., the T wave) c (x) = 1 - f c (x) multiplied by the weight k c and added with the level value β T The function k of c f' c (x) + β T , the fourth inverse cumulative distribution function f' d (x) = 1 - f d (x) multiplied by the weight k d and added with the level value β T The function k of d f' d (x) + β T , 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 after c f' c (x) - k d f' d (x) + β T The figure of. The single dotted line is the third inverse cumulative distribution function f' c (x) = 1 - f c (x) multiplied by the weight k cand add the level value β T function k c f' c (x) + β T , the double-dashed line is the fourth inverse cumulative distribution function f’ d (x) = 1 - f d (x) multiplied by the weight k d and add the level value β T 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 is a waveform that approximates the second object time waveform (i.e., the T wave).

[0197] Furthermore, the potential at the start of the R wave (accurately, the QRS wave), i.e., the level value β R is a value representing the magnitude of the DC component at the start of the R wave. In the case of coronary artery abnormalities, the level value β R sometimes drops to the negative side. In addition, the potential at the end of the T wave, i.e., the level value β T is a value representing the magnitude of the DC component at the end of the T wave. In the case of abnormalities in myocardial repolarization, the level value β T sometimes rises to the positive side.

[0198] [Approximation of the difference between the object time waveform and the approximate time waveform]

[0199] When the R wave or T wave in a special state is set as the object time waveform, in the above approximate time waveform, there are sometimes parts that cannot approximate the object time waveform (hereinafter referred to as "residual parts"). For example, in the case of early repolarization or conduction disorders (such as accessory conduction pathways) occurring in the target heart, as Figure 8 shown by the dotted line, the Δ wave sometimes includes in the object time waveform (R wave). The part of this Δ wave cannot approximate the object time waveform in the above approximate time waveform and remains as a residual part. If looking at the residual part as a time waveform also caused by a certain activity of the heart, in the signal analysis device 1, it is possible to perform analysis assuming a cumulative Gaussian distribution, or a function obtained by multiplying the cumulative Gaussian distribution by a weight, or the difference of the cumulative Gaussian distribution, or the weighted difference of the cumulative Gaussian distribution for this residual part.

[0200] That is, the signal analysis device 1 can also use the cumulative distribution function (conveniently referred to as the "fifth cumulative distribution function") of a certain unimodal distribution (conveniently referred to as the "fifth unimodal distribution") or a function obtained by multiplying the fifth cumulative distribution function by a weight to approximate the residual time waveform, which is the difference between the target time waveform and the approximate time waveform. When determining the parameters of the fifth unimodal distribution or the parameters of the fifth cumulative distribution function, they are also obtained as parameters representing the characteristics of the target time waveform.

[0201] Alternatively, the signal analysis device 1 can also use 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 unimodal distribution") 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 unimodal distribution") for the residual time waveform, which is the difference between the target time waveform and the approximate time waveform. The time waveform generated thereby (hereinafter referred to as the "approximate residual time waveform") is used to approximate the residual time waveform. When determining 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, they are also obtained as parameters representing the characteristics of the target time waveform.

[0202] More specifically, in the case of approximating the residual time waveform using the cumulative distribution function of the fifth unimodal distribution represented by Equation (23), that is, the fifth cumulative distribution function, the signal analysis device 1 uses the approximate time waveform f5(x) of the fifth cumulative distribution function represented by Equation (24) to approximate the residual time waveform, which is the difference between the target time waveform and the approximate time waveform. In addition to the above-mentioned parameters representing the characteristics of the target time waveform, the parameters determining the fifth cumulative distribution function, namely the mean μ5 and the standard deviation σ5, are also obtained as parameters representing the characteristics of the target time waveform.

[0203] [Equation 23]

[0204]

[0205] [Equation 24]

[0206]

[0207] In the case where 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, which is the difference between the target time waveform and the approximated time waveform, by an approximated time waveform k5f5(x) that is a function obtained by multiplying the weight k5 by the fifth cumulative distribution function f5(x) expressed by Equation (24). In addition to the respective parameters representing the characteristics of the target time waveform described above, the parameters determining the fifth cumulative distribution function, namely the mean μ5 and the standard deviation σ5, are also obtained as parameters representing the characteristics of the target time waveform. Furthermore, the signal analysis device 1 may also obtain the weight k5 as a parameter representing the characteristics of the target time waveform.

[0208] In the case where the residual time waveform is approximated by the difference between the fifth cumulative distribution function and the cumulative distribution function of the sixth unimodal distribution expressed by Equation (25), that is, the sixth cumulative distribution function, for example, the signal analysis device 1 approximates the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, by an approximated time waveform f5(x) - f6(x) that is a waveform obtained by subtracting the sixth cumulative distribution function expressed by Equation (26) from the fifth cumulative distribution function expressed by Equation (24). In addition to the respective parameters representing the characteristics of the target time waveform described above, the parameters determining the fifth cumulative distribution function, namely the mean μ5 and the standard deviation σ5, and the parameters determining the sixth cumulative distribution function, namely the mean μ6 and the standard deviation σ6, are also obtained as parameters representing the characteristics of the target time waveform.

[0209] [Equation 25]

[0210]

[0211] [Equation 26]

[0212]

[0213] In the case of approximating the residual time waveform 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, which is the difference between the target time waveform and the approximated time waveform, by the waveform k5f5(x) - k6f6(x) obtained by subtracting the function k6f6(x) obtained by multiplying the sixth cumulative distribution function f6(x) by the weight k6 from the function k5f5(x) obtained by multiplying the fifth cumulative distribution function f5(x) by the weight k5, that is, the approximated time waveform. In addition to the respective parameters representing the characteristics of the target time waveform as described above, the parameters determining the fifth cumulative distribution function, namely, the mean μ5 and the standard deviation σ5, and the parameters determining the sixth cumulative distribution function, namely, the mean μ6 and the standard deviation σ6, are also obtained as the parameters representing the characteristics of the target time waveform. Furthermore, 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 the parameters representing the characteristics of the target time waveform.

[0214] When approximating the residual time waveform from the approximated time waveform obtained by subtracting the function obtained by multiplying the sixth cumulative distribution function by the weight k6 from the function obtained by multiplying the fifth cumulative distribution function by the weight k5, both the weight k5 and the weight k6 are positive values. However, in the case where the target heart is in a specific state, it cannot be denied that at least one of the weights k5 and k6 obtained by fitting may not be a positive value. Therefore, although the signal analysis device 1 can be fitted so that both the weight k5 and the weight k6 are positive values, it is not necessary to be fitted so that both the weight k5 and the weight k6 are positive values.

[0215] Return to Figure 1 the description. 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 by a bus. The signal analysis device 1 functions as a device including the control unit 11, an input unit 12, a communication unit 13, a storage unit 14, and an output unit 15 by executing the program.

[0216] More specifically, the processor 91 reads out the program stored in the storage unit 14 and stores the read program in the memory 92. The processor 91 executes the program stored in the memory 92, whereby the signal analysis device 1 functions as a device including the control unit 11, an input unit 12, a communication unit 13, a storage unit 14, and an output unit 15.

[0217] The control unit 11 controls the operations of various functional units included in the signal analysis device 1. For example, the control unit 11 performs a process of obtaining myocardial activity information parameters. For example, the control unit 11 controls the operation of the output unit 15 to cause the output unit 15 to output the obtained result of the process of obtaining myocardial activity information parameters. The control unit 11 records various information generated, for example, by the execution of the process of obtaining myocardial activity information parameters, in the storage unit 14.

[0218] The input unit 12 is configured to include input devices such as a mouse, a keyboard, and a touch panel. The input unit 12 may also be configured to connect these input devices to the interface of the signal analysis device 1. The input unit 12 receives the input of various information to the signal analysis device 1.

[0219] Information indicating the shape of the distribution represented by each cumulative distribution function (hereinafter referred to as "distribution shape specification information") for a plurality of candidates of each cumulative distribution function for fitting is input to the input unit 12.

[0220] Furthermore, the distribution shape specification information may be previously stored in the storage unit 14. In such a case, it is not necessary to input the distribution shape specification information stored in the storage unit 14 from the input unit 12. Hereinafter, for simplicity of explanation, the signal analysis device 1 will be described by taking the case where the storage unit 14 has previously stored the distribution shape specification information as an example.

[0221] 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 wire or wirelessly. The external device is, for example, a device that is the source of the waveform of the electrocardiogram of the target heart. The device that is the source of the waveform of the electrocardiogram of the target heart is, for example, an electrocardiogram measuring device. The communication unit 13, for example, when the external device is an electrocardiogram measuring device, obtains the waveform of the electrocardiogram from the electrocardiogram measuring device through communication. Furthermore, the waveform of the electrocardiogram may be input to the input unit 12.

[0222] The storage unit 14 is constituted by using a non-temporary 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 information input via the input unit 12 or the communication unit 13, for example. 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 various information generated, for example, by the execution of the process of obtaining myocardial activity information parameters.

[0223] The output unit 15 outputs various types of information. The output unit 15 is configured to include, for example, display devices such as a CRT (Cathode Ray Tube) monitor, a liquid crystal display, and an organic EL (Electro-Luminescence) display. The output unit 15 can also be configured to connect these display devices to the interface of the signal analysis device 1. The output unit 15 outputs, for example, the information input to the input unit 12. The output unit 15 can also display, for example, the electrocardiogram input to the input unit 12 or the communication unit 13. The output unit 15 can also display, for example, the execution result of the myocardial activity information parameter acquisition process.

[0224] Figure 9 This is a diagram showing an example of the functional structure of the control unit 11 in the 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.

[0225] The electrocardiogram acquisition unit 110 acquires the waveform of one cycle 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 the waveforms of multiple beats (the waveforms of multiple cycles) are arranged in time series. Even when an abnormality occurs in the target heart, the waveforms of all beats included in the electrocardiogram waveform are not special waveforms, and only the waveforms of any few beats included in the electrocardiogram waveform become characteristic waveforms. Preferably, in the myocardial activity information parameter acquisition process, the characteristic waveform is used as the target. Therefore, the electrocardiogram acquisition unit 110 acquires the waveform of one cycle of the characteristic waveform from the electrocardiogram waveform of the target heart. For example, the electrocardiogram acquisition unit 110 can use a known technique for determining similarity or specificity to acquire the waveform of one cycle of the characteristic waveform from the electrocardiogram waveform of the target heart. In addition, for example, the electrocardiogram acquisition unit 110 can also cause the output unit 15 to display the electrocardiogram waveform, cause the input unit 12 to receive the designation of the waveform of one cycle by a user such as a doctor, and acquire the waveform of one cycle corresponding to the designation received by the input unit 12 from the electrocardiogram waveform.

[0226] The electrocardiogram acquisition unit 110 outputs the waveform of one cycle's worth 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 in the fitting information acquisition unit 120, for example, 250 Hz. When the waveform of the input electrocardiogram is sampled at 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's worth from the digital time series data of the waveform of the input electrocardiogram and output it. When 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's worth from the digital time series data of the waveform of the input electrocardiogram, transform it to the prescribed sampling frequency, and then output it.

[0227] Furthermore, the electrocardiogram acquisition unit 110 determines the time intervals of the R wave and the T wave included in the waveform of one cycle's worth of the electrocardiogram, also acquires the information for determining the time interval of the R wave and the 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 can determine the start end, 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 acquire the sample numbers, the relative times from the start end of the waveform, etc. 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, as the information for determining the time interval of the R wave and the information for determining the time interval of the T wave. Hereinafter, the R wave in this specification accurately refers to the QRS wave. Although there are actually various interpretations as to which point in the waveform is the start end of the R wave (i.e., the start end of the QRS wave), the electrocardiogram acquisition unit 110 only needs to use the point determined by any known technique as the start end of the R wave.

[0228] Furthermore, in the waveform of the electrocardiogram of the target heart, there may be characteristic waveforms that change over time. Therefore, the electrocardiogram acquisition unit 110 can also acquire the waveforms of multiple cycles' worth from the waveform of the electrocardiogram of the target heart as the waveforms for which the myocardial activity information parameter acquisition processing is to be performed. That is, the electrocardiogram acquisition unit 110 can also acquire a waveform (trend graph) of a predetermined long-term time series from the waveform of the electrocardiogram of the target heart, and output the waveform of each cycle included in the acquired waveform, the information for determining the time interval of the R wave included in the waveform, and the information for determining the time interval of the T wave included in the waveform to the analysis unit 130.

[0229] The fitting information acquisition unit 120 acquires distribution shape specification information. When the distribution shape specification information is stored in the storage unit 14, the fitting information acquisition unit 120 reads out the distribution shape specification information from the storage unit 14.

[0230] The analysis unit 130 includes a fitting unit 131 and a myocardial activity information parameter acquisition unit 132.

[0231] The fitting unit 131 uses a candidate of the cumulative distribution function shown by the distribution shape specification 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 amount of the electrocardiogram acquired by the electrocardiogram acquisition unit 110, that is, the target time waveform.

[0232] The myocardial activity information parameter acquisition unit 132 acquires a parameter representing the characteristics of the target time waveform based on the fitting result by the fitting unit 131. As a parameter representing the characteristics of the target time waveform, the myocardial activity information parameter acquisition unit 132 acquires, for example, a parameter for determining the first unimodal distribution or a parameter for determining the first cumulative distribution function, and a parameter for determining the second unimodal distribution or a parameter for determining the second cumulative distribution function when approximating the target time waveform by an approximate time waveform, where the approximate time waveform is a time waveform generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, that is, the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, that is, the second cumulative distribution function. The parameter representing the characteristics of the target time waveform acquired by the myocardial activity information parameter acquisition unit 132 is an example of a myocardial activity parameter.

[0233] In this way, the analysis unit 130 acquires the myocardial activity parameter based on the target time waveform, which is 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 amount of the electrocardiogram of the target heart, and the distribution candidate information.

[0234] The storage unit 14 records various information generated by the processing executed by the control unit 11 in the storage unit 14.

[0235] Figure 10 It is a flowchart showing an example of the processing flow executed by the signal analysis device 1 in the embodiment. The electrocardiogram acquisition unit 110 acquires the waveform of one cycle amount of the electrocardiogram of the target heart, the information for determining the time interval of the R wave, and the 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 specification information (step S102). Next, the fitting unit 131 uses the candidate of the cumulative distribution function shown by the distribution shape specification information to fit the target time waveform, which is 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 amount of the electrocardiogram acquired in step S101 (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).

[0236] In step S105, it is also possible to display a graph of the fitting results for the time waveforms of each object. In addition, the process of step S102 may be executed before the execution of the process of step S103, or may also be executed before the execution of step S101. The processes of steps S103 and S104 are examples of the processes executed by the analysis unit 130.

[0237] Figure 11 FIG. 1 is a first diagram showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 11 is an example of the analysis result of the waveform of the electrocardiogram of a normal functioning object heart by the signal analysis device 1. Figure 11 The horizontal axis represents time, and the vertical axis represents potential. The unit of the vertical axis is an arbitrary unit.

[0238] Figure 11 Shows the use of k a / α1 instead of the weight k a so that the first fitting result at time T3 and the third fitting result at time T4 become the same value and use k b / α2 instead of the weight k b to show an example of the results of the first and second fittings of the depolarizing R wave of the electrocardiogram of a normal functioning object heart and the results of the third and fourth fittings of the repolarizing T wave of the electrocardiogram of a normal functioning object heart such that the second fitting result at time T3 and the fourth fitting result at time T4 become the same value. Hereinafter, the result of connecting the first fitting result and the third fitting result with the changed weights via a straight line is referred to as the endocardial activity approximation function, and the result of connecting the second fitting result and the fourth fitting result with the changed weights via a straight line is referred to as the epicardial activity approximation function.

[0239] Figure 11 Corresponding to the depolarization of a normal heart, the endocardium starts to activate earlier (i.e., the activity of ion channels) than the epicardium and progresses rapidly. The epicardium starts to activate slightly later than the timing of the start of the ion channel activity of the endocardium, and the difference between the timing of the start of the ion channel activity of the endocardium and the timing of the start of the epicardial activity becomes a positive and sharp R wave. That is, the average value and standard deviation of the partial endocardial activity approximation function of the first fitting result in the endocardial activity approximation function, and the average value and standard deviation of the partial second fitting result in the epicardial activity approximation function are parameters representing the timing of the ion channel activity and the progress of the activity during the depolarization of a normal heart.

[0240] In addition, Figure 11Corresponding to the repolarization phase of a normal heart, the inactivation of ion channel activity in the outer myocardial layer starts earlier than that in the inner myocardial layer. The inactivation in the inner myocardial layer starts after that in the outer myocardial layer, and both the inactivation in the outer myocardial layer and the inactivation in the inner myocardial layer advance slowly. The difference between the inactivation in the inner myocardial layer and the inactivation in the outer myocardial layer becomes a positive and gentle T wave. That is, the average value and standard deviation of the part of the fourth fitting result in the approximate function of the outer myocardial layer activity, and the average value and standard deviation of the part of the third fitting result in the approximate function of the inner myocardial layer activity are parameters representing the timing of the inactivation of ion channel activity and the advancement of inactivation in the repolarization phase of a normal heart. As described above, the approximate function of the inner myocardial layer activity and the approximate function of the outer myocardial layer activity obtained by the first to fourth fittings correspond to the collective activation of depolarized ion channels and the timing and advancement of the collective inactivation of ion channels in the repolarization phase. The average value and standard deviation of each fitting result of each function are parameters representing myocardial activity.

[0241] Figure 11 The shapes of the approximate function of the inner myocardial layer activity and the approximate function of the outer myocardial layer activity 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 the heart of a subject with normal movement. This shows that according to the signal analysis device 1, information representing myocardial activity can be obtained only from an electrocardiogram without inserting a catheter electrode.

[0242] Figure 12 is the second figure showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 12 is an example of the analysis result of the waveform of the electrocardiogram of the heart of a subject with normal movement by the signal analysis device 1.

[0243] Figure 12 shows three results, namely, graph G5, graph G6, and result G7. In Figure 12 , the "inner layer side cumulative distribution function" shows the fitting result of the function representing the collective channel activity timing distribution of the ion channels existing in the inner myocardial layer. In Figure 12 , the "outer layer side cumulative distribution function" shows the fitting result of the function representing the collective channel activity timing distribution of the ion channels existing in the outer myocardial layer. In Figure 12 , the "potential on the body surface" is a function representing the time change of the potential on the body surface and is the waveform of the electrocardiogram. Figure 12 The horizontal axis of Figures 12 to 14 represents time, and the vertical axis represents potential. The units of both the horizontal axis and the vertical axis are arbitrary units (arbitrary unit). Furthermore, Figures 12 to 14 the time length represented by the interval of one scale on each horizontal axis is the same. In addition, regarding the vertical axis, 1 represents the maximum value of the cumulative Gaussian distribution.

[0244] Graph G5 represents the entire waveform of an electrocardiogram generated during one heartbeat. Graph G6 shows an enlarged view of the T-wave region, which is a part of Graph G5. The T-wave region is the region shown as Region A1 in Figure 12 Result G7 shows the statistics of two Gaussian distributions, namely, the Gaussian distribution that is the cumulative source of the inner-layer side cumulative distribution function and the Gaussian distribution that is the cumulative source of the outer-layer side cumulative distribution function. Each value of Result G7 represents the statistics of the two Gaussian distributions. Specifically, the statistics of the two Gaussian distributions refer to the mean and standard deviation of the Gaussian distributions that are the cumulative sources of the inner-layer side cumulative distribution function and the outer-layer side cumulative distribution function.

[0245] Figure 13 is the third figure showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 13 is an example of the analysis result of the waveform of the electrocardiogram of the target heart with a T-prolongation type 3 action by the signal analysis device 1.

[0246] Figure 13 shows three results, namely Graph G8, Graph G9, and Result G10. In Figure 13 , 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. In Figure 13 , 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. In Figure 13 , the "potential on the body surface" is a function representing the time change of the potential on the body surface and is the waveform of the electrocardiogram. Figure 13 The horizontal axis of represents time, and the vertical axis represents potential. The units of both the horizontal axis and the vertical axis are arbitrary units (abbreviation: au).

[0247] Graph G8 represents the entire waveform of an electrocardiogram generated during one heartbeat. Graph G9 shows an enlarged view of the T-wave region, which is a part of Graph G8. The T-wave region is the region shown as Region A2 in Figure 13 Result G10 shows the statistics of two Gaussian distributions, namely, the Gaussian distribution that is the cumulative source of the inner-layer side cumulative distribution function and the Gaussian distribution that is the cumulative source of the outer-layer side cumulative distribution function. Each value of Result G10 represents the statistics of the two Gaussian distributions, that is, the mean and standard deviation of the Gaussian distributions that are the cumulative sources of the inner-layer side cumulative distribution function and the outer-layer side cumulative distribution function.

[0248] Figure 13The shapes of the inner-layer side cumulative distribution function and the outer-layer side cumulative distribution function are substantially consistent with the results of directly measuring the change in the electromotive force generated by the pulsation of the outer layer of the myocardium of the heart of a subject with a QT prolongation type 3 action by inserting a catheter electrode. This shows that, according to the signal analysis device 1, information indicating myocardial activity can be obtained only from an electrocardiogram without inserting a catheter electrode.

[0249] Figure 14 It is the fourth figure showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 14 It is an example of the analysis result of the electrocardiogram waveform of the heart of a subject with a QT prolongation type 1 action by the signal analysis device 1.

[0250] Figure 14 It shows three results: the graph G11, the graph G12, and the result G13. In Figure 14 , 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. In Figure 14 , 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. In Figure 14 , the "potential on the body surface" is a function representing the time change of the potential on the body surface and is the waveform of the electrocardiogram. Figure 14 The horizontal axis of represents time, and the vertical axis represents potential. The units of both the horizontal axis and the vertical axis are arbitrary units (arbitrary unit).

[0251] The graph G11 represents all the waveforms of the electrocardiogram generated in one pulsation. The graph G12 shows an enlarged view of the T wave region, which is a part of the graph G11. The T wave region is the region shown as region A3 in Figure 13 . The result G13 shows the statistics of two Gaussian distributions, namely the Gaussian distribution that is the cumulative source of the inner-layer side cumulative distribution function and the Gaussian distribution that is the cumulative source of the outer-layer side cumulative distribution function. Each value of the result G13 represents the statistics of the two Gaussian distributions. Specifically, the statistics of the two Gaussian distributions refer to the mean and standard deviation of the Gaussian distributions that are the cumulative sources of the inner-layer side cumulative distribution function and the outer-layer side cumulative distribution function.

[0252] Figure 14 The shapes of the inner-layer side cumulative distribution function and the outer-layer side cumulative distribution function are substantially consistent with the results of directly measuring the change in the electromotive force generated by the pulsation of the outer layer of the myocardium of the heart of a subject with a QT prolongation type 3 action by inserting a catheter electrode. This shows that, according to the signal analysis device 1, information indicating myocardial activity can be obtained only from an electrocardiogram without inserting a catheter electrode.

[0253] Furthermore,Figure 14 This is also an example of the estimation result of the channel current characteristics associated with sudden death by the signal analysis device 1.

[0254] Using Figures 15 to 17 to illustrate that for the electrocardiogram of ventricular premature contractions, the signal analysis device 1 can also obtain information representing myocardial activity only from the electrocardiogram. Figures 15 to 17 The horizontal axis of represents time (seconds), and the vertical axis represents potential (mV).

[0255] Figure 15 Figure 1 is a first explanatory diagram for illustrating an example of the analysis of the electrocardiogram of ventricular premature contractions by the signal analysis device 1 of the embodiment. Figure 16 Figure 2 is a second explanatory diagram for illustrating an example of the analysis of the electrocardiogram of ventricular premature contractions by the signal analysis device 1 of the embodiment. Figure 17 Figure 3 is a third explanatory diagram for illustrating an example of the analysis of the electrocardiogram of ventricular premature contractions by the signal analysis device 1 of the embodiment.

[0256] More specifically, Figure 15 shows the cardiac potential on the body surface. That is, Figure 15 shows a normal single heartbeat and two consecutive ventricular premature contractions recorded by the electrocardiogram. More specifically, Figure 16 shows the myocardial inner layer activity approximation function including the inner layer cumulative distribution function of depolarization and the inner layer cumulative distribution function of repolarization phase of ventricular premature contractions analyzed by the signal analysis device 1, and the myocardial outer layer activity approximation function including the outer layer cumulative distribution function of depolarization and the outer layer cumulative distribution function of repolarization phase of ventricular premature contractions analyzed by the signal analysis device 1. More specifically, Figure 17 shows an example of the waveform of ventricular premature contractions in the actually measured electrocardiogram.

[0257] Figure 16 shows that the inner layer cumulative distribution function of depolarization precedes the outer layer cumulative distribution function, the standard deviations of both are greater than those of normal heartbeats, and the spread of excitation is slow. This analysis result is consistent with the characteristics of the waveform of the broad-based R wave.

[0258] Figure 16 Corresponding to the fact that in the repolarization phase, the inner layer cumulative distribution function starts to inactivate earlier than the outer layer cumulative distribution function, and as the magnitude relationship of the average value of the two cumulative distribution functions in the repolarization phase, it shows the order of inactivation of the inner layer and the outer layer. From Figure 16 subtracting the outer layer cumulative distribution function from the inner layer cumulative distribution function of gives a function that is consistent with the characteristics of the large negative T wave in the repolarization phase, as shown in Figure 17As shown, the waveform obtained by subtracting the cumulative distribution function on the outer layer side from the cumulative distribution function on the inner layer side is substantially consistent with the waveform of ventricular premature contraction in the actually measured electrocardiogram.

[0259] Furthermore, Figures 15 to 17 The result of shows an example in which the analysis by the signal analysis device 1 corresponds to the generation of large waves or negative potentials due to altered conduction, early repolarization, or delayed repolarization of myocardial excitation. Furthermore, in Figures 15 to 17 the mean μ of the cumulative distribution function on the inner layer side in the depolarization phase is -1, and the standard deviation σ is 0.32. In addition, in Figures 15 to 17 the mean μ of the cumulative distribution function on the outer layer side of depolarization is -0.8, and the standard deviation σ is 0.21. In addition, in Figures 15 to 17 the mean μ of the cumulative distribution function on the inner layer side in the repolarization phase is 1, and the standard deviation σ is 1. In addition, in Figures 15 to 17 the mean μ of the cumulative distribution function on the outer layer side in the repolarization phase is 2.99, and the standard deviation σ is 0.7.

[0260] Using Figures 18 to 20 it is also possible to obtain information indicating myocardial activity only from the electrocardiogram by the signal analysis device 1 for the electrocardiogram of the target heart in the depolarization period of Brugada syndrome type 1. Figures 18 to 20 The vertical axis of represents the potential in millivolts.

[0261] Figure 18 FIG. is a first explanatory diagram for explaining an example of analyzing the electrocardiogram of the target heart in the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 in the embodiment. Figure 19 FIG. is a second explanatory diagram for explaining an example of analyzing the electrocardiogram of the target heart in the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 in the embodiment. Figure 20 FIG. is a third explanatory diagram for explaining an example of analyzing the electrocardiogram of the target heart in the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 in the embodiment.

[0262] More specifically, Figure 18 shows the electrocardiogram of the second chest induction of Brugada syndrome. In Figure 18 the inner frame W1 shows the depolarization phase, and the inner frame W2 shows the division of the repolarization phase. This is the same for Figure 19 as well. That is, in Figure 19 the inner frame W1 shows the depolarization phase, and the inner frame W2 shows the repolarization phase.

[0263] More specifically, Figure 19 shows the cumulative distribution function on the inner layer side and the cumulative distribution function on the outer layer side in the depolarization and repolarization phases analyzed by the signal analysis device 1. In Figure 19In the illustrated example, the repolarization phase of the inner layer side cumulative distribution function starts after the depolarization phase, showing the characteristics of early repolarization. On the other hand, in Figure 19 's example, regarding the potential amplitude of the outer layer side cumulative distribution function, differences are seen in the depolarization phase and the repolarization phase. In addition, Figure 19 shows the gap and anisotropy between the depolarization phase and the repolarization phase of the outer layer side cumulative distribution function. In this way, the signal analysis device 1 can represent the early repolarization, the anisotropy between depolarization and repolarization, which are the characteristics of the waveform shown in the electrocardiogram of the target heart with Brugada syndrome, by the mean and standard deviation of the depolarization phase and the repolarization phase of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function, and the ratio of the weight given to the outer layer side cumulative distribution function to the weight given to the inner layer side cumulative distribution function (inner-outer layer ratio).

[0264] Figure 20 is the comparison between the analysis result and the measured value. More specifically, Figure 20 shows Figure 19 the difference between the inner layer side cumulative distribution function and the outer layer side cumulative distribution function in the depolarization phase and the repolarization phase. In addition, Figure 20 also shows the measured value of the electrocardiogram. Except for the very end, the analysis result is roughly consistent with the measured value. The very end means the potential at a late time.

[0265] Furthermore, in Figures 18 to 20 , the mean μ of the inner layer side cumulative distribution function in the depolarization phase is 15, and the standard deviation σ is 0.15. In addition, in Figures 18 to 20 , the mean μ of the outer layer side cumulative distribution function in the depolarization phase is 14, and the standard deviation σ is 0.25. In addition, in Figures 18 to 20 , the inner-outer layer ratio in the depolarization phase is 0.45. In addition, in Figures 18 to 20 , the mean μ of the inner layer side cumulative distribution function in the repolarization phase is 25, and the standard deviation σ is 0.25. In addition, in Figures 18 to 20 , the mean μ of the outer layer side cumulative distribution function in the repolarization phase is 20, and the standard deviation σ is 0.5.

[0266] Figures 21 to 59 shows the result of the analysis by the signal analysis device 1 using the publicly available electrocardiogram database <https: / / physionet.org / about / database / >. Figures 21 to 59 shows the inner layer side cumulative distribution function, the outer layer side cumulative distribution function, and the fitting result obtained from the result of the analysis of the cardiac potential by the signal analysis device 1.

[0267] Figures 21 to 59These are diagrams showing an example of an electrocardiogram analyzed by the signal analysis device 1 in the embodiment. The determined points described in each diagram represent the Q point, R point, S point, T start point, and T end point of the cardiac potential in order from the left part of the diagram. The determined points are determined by the inflection point sensing and peak detection algorithms. Figures 21 to 59 Each of these diagrams shows the inner layer cumulative distribution function and the outer layer cumulative distribution function in each interval obtained for the depolarization phase (QRS wave) interval and the repolarization phase (T wave) interval. Figures 21 to 59 It shows that for various QRS waves and T waves, the signal analysis device 1 can show substantially the same shape by adjusting the mean and standard deviation of the inner layer cumulative distribution function and the outer layer cumulative distribution function. Figures 21 to 59 The lower diagram in shows the original waveform of the electrocardiogram and the fitting result. Further, Figures 21 to 59 each of these results is the result of sampling at 300 Hz. Therefore, Figures 21 to 59 the origin of the horizontal axis in each of these diagrams represents 0 seconds, and the value 1 represents 3.33 milliseconds.

[0268] The signal analysis device 1 configured in this way takes, as the target time waveform, the waveform of the time interval including either the R wave or the T wave in the waveform showing the amount of one cycle of the cardiac cycle of the target heart, and obtains, as parameters representing the characteristics of the target time waveform, the parameters for determining the first unimodal distribution or the parameters for determining the first cumulative distribution function, and the parameters for determining the second unimodal distribution or the parameters for determining the second cumulative distribution function when approximating the target time waveform by an approximate time waveform, where the approximate time waveform is a time waveform generated by the difference or weighted difference between the first cumulative distribution function, which is the cumulative distribution function of the first unimodal distribution, and the second cumulative distribution function, which is the cumulative distribution function of the second unimodal distribution. The first cumulative distribution function is information showing the activity of the inner layer of the myocardium of the target heart, and the second cumulative distribution function is information showing the activity of the outer layer of the myocardium of the target heart. Therefore, the parameters representing the shape of the first cumulative distribution function are parameters showing the activity of the inner layer of the myocardium of the target heart (inner layer myocardial parameters), and the parameters representing the shape of the second cumulative distribution function are parameters showing the activity of the outer layer of the myocardium of the target heart (outer layer myocardial parameters). Through the conventional analysis of the electrocardiogram waveform, information that clearly shows the characteristics of the activity of the inner layer of the myocardium of the target heart and information that clearly shows the characteristics of the activity of the outer layer of the myocardium of the target heart, such as these parameters, cannot be obtained. Therefore, according to the signal analysis device 1, useful information for grasping the cardiac state can be obtained from the electrocardiogram waveform.

[0269] (Only obtain some parameters)

[0270] In a case where only the characteristics of the activity of the inner myocardial layer of the subject's heart are to be grasped, only the inner myocardial layer parameters can be obtained by the signal analysis device 1. In a case where only the characteristics of the activity of the outer myocardial layer of the subject's heart are to be grasped, only the outer myocardial layer parameters can be obtained by the signal analysis device 1. In addition, as the inner myocardial layer parameters or the outer myocardial layer parameters, only a part of the parameters representing the shape of the cumulative distribution function can be obtained by the signal analysis device 1.

[0271] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 acquires the time waveform, i.e., the first approximate time waveform, generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or the time waveform, i.e., the first approximate time waveform, generated by adding a level value to the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, to approximate the waveform of the time interval of the R wave of the subject's heart, i.e., the first subject time waveform. At least any one of at least a part of the parameters for determining the first unimodal distribution, at least a part of the parameters for determining the first cumulative distribution function, at least a part of the parameters for determining the second unimodal distribution, and at least a part of the parameters for determining the second cumulative distribution function is used as the parameter indicating the myocardial activity of the subject's heart, i.e., the myocardial activity parameter.

[0272] For example, if 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 any 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. Further, the mean value of the Gaussian distribution is the moment when the frequency value in the unimodal distribution becomes the maximum, and is the moment when the slope of the cumulative distribution function of the unimodal distribution becomes the maximum. Therefore, for example, the myocardial activity information parameter acquisition unit 132 only needs to acquire at least any one of the moment corresponding to the maximum value of the first unimodal distribution, the moment corresponding to the maximum slope of the first cumulative distribution function, the moment corresponding to the maximum value of the second unimodal distribution, and the moment 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.

[0273] For example, when the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 obtains the waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, which is the second approximate inverse time waveform, or the waveform generated by the result obtained by adding the level value to the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, to approximate the waveform of the time interval of the T wave of the target heart, which is the second target time waveform, and then obtaining the waveform of the time axis inversion of the second target time waveform, i.e., the second target inverse time waveform, at least any one of at least a part of the parameters for determining the third unimodal distribution, at least a part of the parameters for determining the third cumulative distribution function, at least a part of the parameters for determining the fourth unimodal distribution, and at least a part of the parameters for determining the fourth cumulative distribution function is used as the parameter indicating the myocardial activity of the target heart, i.e., the myocardial activity parameter.

[0274] For example, if both the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 can obtain at least any one of the mean value of the third unimodal distribution, the standard deviation or variance of the third unimodal distribution, 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, regardless of whether the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 can obtain at least any one of the moment corresponding to the maximum value of the distribution of the third unimodal distribution, the moment corresponding to the maximum slope of the third cumulative distribution function, the moment corresponding to the maximum value of the fourth unimodal distribution, and the moment corresponding to the maximum slope of the fourth cumulative distribution function as the myocardial activity parameter. Furthermore, when the myocardial activity information parameter acquisition unit 132 obtains the moment as the myocardial activity parameter, even when approximating the waveform of the time axis inversion, it obtains the moment (the value of x in the above example), rather than the information indicating the moment in the reverse direction (the value of x' in the above example).

[0275] 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, the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function, and acquires the waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the second approximate time waveform, or the waveform generated by the result of adding the level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the second approximate time waveform, to approximate the waveform of the time interval of the T wave of the target heart, i.e., the second target time waveform. At least any one of at least a part of the parameters for determining the third unimodal distribution, at least a part of the parameters for determining the third cumulative distribution function, at least a part of the parameters for determining the fourth unimodal distribution, and at least a part of the parameters for determining the fourth cumulative distribution function is used as the parameter indicating the myocardial activity of the target heart, i.e., the myocardial activity parameter.

[0276] For example, if both the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 can acquire at least any one of the mean value of the third unimodal distribution, the standard deviation or variance of the third unimodal distribution, 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, regardless of whether the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 can acquire at least any one of the moment corresponding to the maximum value of the third unimodal distribution, the moment corresponding to the maximum slope of the third cumulative distribution function, the moment corresponding to the maximum value of the fourth unimodal distribution, and the moment corresponding to the maximum slope of the fourth cumulative distribution function as the myocardial activity parameter.

[0277] 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 for determining the fifth unimodal distribution and at least a part of the parameters for determining the fifth cumulative distribution function when approximating the time waveform of the difference between the first target time waveform and the first approximate time waveform, i.e., the residual time waveform, by the cumulative distribution function of the fifth unimodal distribution, i.e., the fifth cumulative distribution function, or the time waveform generated by the result of multiplying the fifth cumulative distribution function by a weight, i.e., the approximate residual time waveform, or the time waveform of the difference between the second target time waveform and the second approximate time waveform, i.e., the residual time waveform, or the time waveform of the difference between the second target time waveform and the waveform obtained by reversing the time axis of the second approximate inverse time waveform, i.e., the second approximate time waveform, i.e., the residual time waveform, as the parameter indicating the myocardial activity of the target heart, i.e., the myocardial activity parameter.

[0278] For example, if the fifth unimodal distribution is a Gaussian distribution, the myocardial activity information parameter acquisition unit 132 may acquire at least any one of the mean value of the fifth unimodal distribution, the standard deviation of the fifth unimodal distribution, or the variance as the myocardial activity parameter. Further, for example, regardless of whether the fifth unimodal distribution is a Gaussian distribution, the myocardial activity information parameter acquisition unit 132 may acquire either the time corresponding to the maximum value of the fifth unimodal distribution or the time corresponding to the maximum slope of the fifth cumulative distribution function as the myocardial activity parameter.

[0279] Further, for example, in a case where the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 approximates the residual time waveform by the time waveform, i.e., the approximate residual time waveform, generated by the difference or weighted difference between the cumulative distribution function of the fifth unimodal distribution, i.e., the fifth cumulative distribution function, and the cumulative distribution function of the sixth unimodal distribution, i.e., the sixth cumulative distribution function, the myocardial activity information parameter acquisition unit 132 acquires at least any one of at least a part of the parameters for determining the fifth unimodal distribution, at least a part of the parameters for determining the fifth cumulative distribution function, at least a part of the parameters for determining the sixth unimodal distribution, and at least a part of the parameters for determining the sixth cumulative distribution function as the parameter indicating the myocardial activity of the target heart, i.e., the myocardial activity parameter.

[0280] For example, if both the fifth unimodal distribution and the sixth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 may acquire at least any one of the mean value of the fifth unimodal distribution, the standard deviation of the fifth unimodal distribution or the variance, the mean value of the sixth unimodal distribution, and the standard deviation of the sixth unimodal distribution or the variance as the myocardial activity parameter. Further, for example, regardless of whether the fifth unimodal distribution and the sixth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 may acquire at least any 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.

[0281] [Acquisition of myocardial activity parameters by calculation of parameters obtained by fitting]

[0282] The activity characteristics of the myocardium of the target heart are not only manifested in the parameters obtained by the above-mentioned fitting, but sometimes are also clearly manifested in the values obtained by the operation of the parameters obtained by fitting with each other. Therefore, it is also possible to obtain, as myocardial activity parameters, the values obtained by the operation of the parameters obtained by fitting with each other by the signal analysis device 1. The parameters obtained by fitting refer to at least any one of the parameters for determining the unimodal distribution or the parameters for determining the cumulative distribution function, the weights, and the level values. In the case where the unimodal distribution is a Gaussian distribution, the parameters for determining the unimodal distribution or the parameters for determining the cumulative distribution function refer to at least any one of the mean value and the standard deviation (or variance). 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 the parameters for determining the unimodal distribution, and the moment corresponding to the maximum slope of the cumulative distribution function of the unimodal distribution is an example of the parameters for determining the cumulative distribution function.

[0283] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain the parameters showing the activity of the myocardium of the target heart (parameters different from the above-mentioned parameters) by operating on the myocardial activity parameters obtained by the above-mentioned fitting for the waveform of the time interval of the R wave included in the waveform of the amount of one cardiac cycle showing the target heart (that is, the parameters showing the activity of the myocardium of the target heart in the time interval of the R wave), and the myocardial activity parameters obtained by the above-mentioned fitting for the waveform of the time interval of the T wave included in the waveform of the amount of one cardiac cycle (that is, the parameters showing the activity of the myocardium of the target heart in the time interval of the T wave).

[0284] In addition, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain the parameters showing the activity of the myocardium of the target heart (parameters different from the above-mentioned parameters) by operating on the parameters showing the inner layer activity of the myocardium of the target heart obtained by the above-mentioned fitting for the waveform of the time interval of the R wave included in the waveform of the amount of one cardiac cycle showing the target heart, and the parameters showing the outer layer activity of the myocardium of the target heart obtained by this fitting.

[0285] In addition, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain the parameters showing the activity of the myocardium of the target heart (parameters different from the above-mentioned parameters) by operating on the parameters showing the inner layer activity of the myocardium of the target heart obtained by the above-mentioned fitting for the waveform of the time interval of the T wave included in the waveform of the amount of one cardiac cycle showing the target heart, and the parameters showing the outer layer activity of the myocardium of the target heart obtained by this fitting.

[0286] Hereinafter, an example will be described in which, when all of the first to fourth unimodal distributions are Gaussian distributions, the value obtained by operating the means obtained by fitting with each other is used as a myocardial activity parameter. When the first to fourth unimodal distributions are not Gaussian distributions, the "mean value" in the following example may be replaced with "the time when the value becomes the maximum in the unimodal distribution", that is, "the time corresponding to the maximum value of the unimodal distribution", "the time when the slope becomes the maximum in the cumulative distribution function", that is, "the time corresponding to the maximum slope of the cumulative distribution function", etc. for implementation.

[0287] (1) A parameter representing the time from depolarization to repolarization

[0288] It is known that in the case where 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 of the time from depolarization to repolarization of the myocardium sometimes indicates the possibility of a certain disease state occurring in 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.

[0289] (1A) A parameter representing the time from depolarization of the inner layer of the myocardium to repolarization of the inner layer of the myocardium

[0290] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can obtain the difference between the mean 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, that is, the first target time waveform, and the mean 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 quantity, that is, the second target time waveform, as a myocardial activity parameter. For example, as in the above example, assuming that the mean value of the first unimodal distribution is μ a , and the mean value of the third unimodal distribution is μ c , the myocardial activity information parameter acquisition unit 132 can also obtain |μ a - μ c | as a myocardial activity parameter. Furthermore, if μ c is later in time than μ a , the myocardial activity information parameter acquisition unit 132 can also obtain μ c - μ a as a myocardial activity parameter. This myocardial activity parameter is a parameter representing the time from depolarization of the inner layer of the myocardium to repolarization of the inner layer of the myocardium.

[0291] (1B) A parameter representing the time from the depolarization of the outer layer of the myocardium to the repolarization of the outer layer of the myocardium

[0292] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can obtain the mean of the second unimodal distribution obtained by the above fitting of the waveform of the time interval of the R wave included in the waveform of the quantity of one cardiac cycle showing the heart of the object, that is, the first object time waveform, and the mean of the fourth unimodal distribution obtained by the above fitting of the waveform of the time interval of the T wave included in the waveform of the quantity of one cardiac cycle, that is, the second object time waveform, as the myocardial activity parameter. For example, as in the above example, assuming that the mean of the second unimodal distribution is μ b , and the mean of the fourth unimodal distribution is μ d , the myocardial activity information parameter acquisition unit 132 can also obtain |μ b - μ d | as the myocardial activity parameter. Furthermore, if looking at μ d being later in time than μ b , the myocardial activity information parameter acquisition unit 132 can also obtain μ d - μ b as the myocardial activity parameter. This myocardial activity parameter is a parameter representing the time from the depolarization of the outer layer of the myocardium to the repolarization of the outer layer of the myocardium.

[0293] (1C) A parameter representing the time from the depolarization of the outer layer of the myocardium to the repolarization of the inner layer of the myocardium

[0294] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can obtain the mean of the second unimodal distribution obtained by the above fitting of the waveform of the time interval of the R wave included in the waveform of the quantity of one cardiac cycle showing the heart of the object, that is, the first object time waveform, and the mean of the third unimodal distribution obtained by the above fitting of the waveform of the time interval of the T wave included in the waveform of the quantity of one cardiac cycle, that is, the second object time waveform, as the myocardial activity parameter. For example, as in the above example, assuming that the mean of the second unimodal distribution is μ b , and the mean of the third unimodal distribution is μ c , the myocardial activity information parameter acquisition unit 132 can also obtain |μ b - μ c | as the myocardial activity parameter. Furthermore, if looking at μ c being later in time than μ b , the myocardial activity information parameter acquisition unit 132 can also obtain μ c - μ bAs a myocardial activity parameter. This myocardial activity parameter is a parameter representing the time from the depolarization of the outer layer of the myocardium to the repolarization of the inner layer of the myocardium.

[0295] (1D) A parameter representing the time from the depolarization of the inner layer of the myocardium to the repolarization of the outer layer of the myocardium

[0296] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can obtain, as the myocardial activity parameter, the difference between the mean 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 of the amount of one cardiac cycle showing the heart of the object, that is, the first object time waveform, and the mean 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 of the amount of one cardiac cycle, that is, the second object time waveform. For example, as in the above example, assuming that the mean value of the first unimodal distribution is μ a , and the mean value of the fourth unimodal distribution is μ d , the myocardial activity information parameter acquisition unit 132 can also obtain |μ a -μ d | as the myocardial activity parameter. Furthermore, if μ d is later in time than μ a , the myocardial activity information parameter acquisition unit 132 can also obtain μ d -μ a as the myocardial activity parameter. This myocardial activity parameter is a parameter representing the time from the depolarization of the inner layer of the myocardium to the repolarization of the outer layer of the myocardium.

[0297] (2) A parameter representing the time difference and order between the inner layer activity and the outer layer activity during depolarization

[0298] When the time difference between the inner layer activity and the outer layer activity during depolarization is longer than normal, there may be obstacles such as delay or blockage in the conduction of myocardial excitation, or the site of excitation onset or the order of excitation transmission may be different from the normal mode, especially indicating disorders of the myocardial stimulation conduction system, myocardial ischemia, and the presence of premature contractions. Therefore, the signal analysis device 1 can obtain, as the myocardial activity parameter, a parameter representing the time difference and order between the inner layer activity and the outer layer activity during depolarization. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain, as the myocardial activity parameter, the difference between the mean value of the first unimodal distribution and the mean 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 the amount of one cardiac cycle showing the heart of the object, that is, the first object time waveform. For example, as in the above example, assuming that the mean value of the first unimodal distribution is μ a , and the mean value of the second unimodal distribution is μ bWhen, the myocardial activity information parameter acquisition unit 132 may also acquire μ a -μ b or μ b -μ a as the myocardial activity parameter.

[0299] (3) Parameters indicating the time difference and order between the inner layer activity and the outer layer activity during repolarization

[0300] When the time difference between the inner layer activity and the outer layer activity during repolarization is prolonged or shortened, some abnormality may occur in the myocardium. Therefore, the signal analysis device 1 may acquire the parameters indicating the time difference and order between the inner layer activity and the outer layer activity during repolarization as the myocardial activity parameters. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may also acquire the difference between the mean value of the third unimodal distribution and the mean value of the fourth unimodal distribution of the waveform of the time interval of the T wave included in the waveform of the amount of one cycle of the cardiac cycle of the target heart shown, that is, the second target time waveform, obtained by the above fitting, as the myocardial activity parameter. For example, as in the above example, assuming that the mean value of the third unimodal distribution is μ c , and the mean value of the fourth unimodal distribution is μ d When, the myocardial activity information parameter acquisition unit 132 may also acquire μ c -μ d or μ d -μ c as the myocardial activity parameter.

[0301] (Calibration of myocardial activity parameters)

[0302] Furthermore, among the myocardial activity parameters obtained by the myocardial activity information parameter acquisition unit 132, in the myocardial activity parameters related to the width in the time direction, similar to the parameters related to the width in the time direction in the conventional parameters representing cardiac activity (such as the QT interval), it is characterized by seeing the influence of the variation caused by the heart rate or the influence of individual differences such as age and gender. It is known that for the conventional parameters related to the width in the time direction, correction is performed to reduce the influence of the variation caused by the heart rate or the influence of individual differences, and the corrected value is used as a parameter for evaluating cardiac activity. Therefore, it is also possible to perform correction on the myocardial activity parameters obtained by the myocardial activity information parameter acquisition unit 132 to reduce the influence of the variation caused by the heart rate or the influence of individual differences, and use the corrected value as a parameter for evaluating myocardial activity. For example, for the variation caused by the heart rate, for the myocardial activity parameters obtained by the above fitting, based on the time interval between adjacent R peaks (hereinafter referred to as the "RR interval"), correction can be performed through linear or non-linear operations, and the corrected value is used as the myocardial activity parameter. In addition, for individual differences, for the myocardial activity parameters obtained by the above fitting, the relationship between the RR interval and the above myocardial activity parameters can be obtained from the electrocardiogram data of each individual for correction, and the corrected value is used as the myocardial activity parameter. This correction can be performed by other devices after the signal analysis device 1 outputs the myocardial activity parameters obtained by fitting, or can be performed by the signal analysis device 1. When the signal analysis device 1 performs the correction of the myocardial activity parameters, for example, the myocardial activity information parameter acquisition unit 132 corrects the myocardial activity parameters obtained by the above fitting, and outputs the corrected value as the myocardial activity parameter. Furthermore, when the correction is performed within the signal analysis device 1, the myocardial activity parameters obtained by the above fitting are, as a result, intermediate parameters obtained within the device, but similar to the case of outputting to the outside of the device, they are parameters representing myocardial activity, and this does not change.

[0303] (Modified Example)

[0304] Furthermore, regarding the electrocardiogram, it is preferably an electrocardiogram close to the induction of the cardiac electromotive force vector. Regarding the electrocardiogram close to the induction of the cardiac electromotive force vector, it is preferably an electrocardiogram capable of obtaining three-dimensional information of cardiac potentials such as II induction, V4 induction, V5 induction, etc., for example. The more channels the electrocardiogram has, the more information it contains. Therefore, the more channels the electrocardiogram has, the better. That is, it is also possible to take the waveforms of each channel of the multi-channel electrocardiogram as the object, and perform analysis in the signal analysis device 1 to obtain myocardial activity parameters as the analysis results for each channel.

[0305] Furthermore, the signal analysis device 1 can also be installed using a plurality of information processing devices communicably connected via a network. In this case, each functional unit included in the signal analysis device 1 can also be dispersedly installed in the plurality of information processing devices.

[0306] Furthermore, the electrocardiogram acquisition unit 110 is an example of the biological information acquisition unit.

[0307] Furthermore, all or part of each function of the signal analysis device 1 can also be implemented using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The program can be recorded in a computer-readable recording medium. The computer-readable recording medium refers to, for example, a removable medium such as a floppy disk, a magneto-optical disk, a ROM, or a CD-ROM, or a storage device such as a hard disk built into a computer system. The program can also be transmitted via an electrical communication line.

[0308] As described above, the embodiments of the present invention have been described in detail with reference to the accompanying drawings. However, the specific structure is not limited to this embodiment and also includes designs within the scope not departing from the gist of the present invention.

[0309] Description of Reference Numerals

[0310] 1... Signal analysis device, 11... Control unit, 12... Input unit, 13... Communication unit, 14... Storage unit, 15... Output unit, 91... Processor, 92... Memory, 110... Electrocardiogram acquisition unit, 120... Fitting information acquisition unit, 130... Analysis unit, 131... Fitting unit, 132... Myocardial activity information parameter acquisition unit, 140... Recording unit.

Claims

1. A signal analysis device, wherein, Comprising: A biological information acquisition unit that acquires, as a first object time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cycle of a cardiac cycle of a heart of an analysis target, and acquires, as a second object time waveform, a waveform of a time interval of a T wave included in the waveform of the amount of one cycle; and An analysis unit, The analysis unit performs the following operations: When approximating the first object time waveform by acquiring a time waveform, i.e., a first approximate time waveform, generated by a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or a time waveform, i.e., a first approximate time waveform, generated by a result obtained by adding a level value to a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, at least any one of at least a part of parameters for determining the first unimodal distribution, at least a part of parameters for determining the first cumulative distribution function, at least a part of parameters for determining the second unimodal distribution, and at least a part of parameters for determining the second cumulative distribution function is determined as a parameter showing the activity of the myocardium of the heart in the time interval of the R wave; A waveform obtained by reversing the time axis of the second object time waveform is used as a second object reverse time waveform; When approximating the second object reverse time waveform by acquiring a waveform, i.e., a second approximate reverse time waveform, generated by a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, or a waveform, i.e., a second approximate reverse time waveform, generated by a result obtained by adding a level value to a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, at least any one of at least a part of parameters for determining the third unimodal distribution, at least a part of parameters for determining the third cumulative distribution function, at least a part of parameters for determining the fourth unimodal distribution, and at least a part of parameters for determining the fourth cumulative distribution function is determined as a parameter showing the activity of the myocardium of the heart in the time interval of the T wave; By performing an operation on a parameter showing the activity of the myocardium of the heart in the time interval of the R wave and a parameter showing the activity of the myocardium of the heart in the time interval of the T wave, a parameter showing the activity of the myocardium of the heart is acquired.

2. A signal parsing device, wherein, Comprising: A biological information acquisition unit that acquires, as a first object time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cycle of a cardiac cycle of a heart of an analysis target, and acquires, as a second object time waveform, a waveform of a time interval of a T wave included in the waveform of the amount of one cycle; and An analysis unit, The analysis unit performs the following operations: When approximating the first target time waveform by obtaining a time waveform, i.e., a first approximate time waveform, generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or by obtaining a time waveform, i.e., a first approximate time waveform, generated by the result of adding a level value to the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, at least any one of at least a part of the parameters for determining the first unimodal distribution, at least a part of the parameters for determining the first cumulative distribution function, at least a part of the parameters for determining the second unimodal distribution, and at least a part of the parameters for determining the second cumulative distribution function is used as a parameter showing the activity of the myocardium of the heart in the time interval of the R wave; The cumulative distribution function of the third unimodal distribution is used as the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is used as the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is used as the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is used as the fourth inverse cumulative distribution function; When approximating the second target time waveform by obtaining a time waveform, i.e., a second approximate time waveform, generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or by obtaining a time waveform, i.e., a second approximate time waveform, generated by the result of adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, at least any one of at least a part of the parameters for determining the third unimodal distribution, at least a part of the parameters for determining the third cumulative distribution function, at least a part of the parameters for determining the fourth unimodal distribution, and at least a part of the parameters for determining the fourth cumulative distribution function is used as a parameter showing the activity of the myocardium of the heart in the time interval of the T wave; A parameter showing the activity of the myocardium of the heart is obtained by performing an operation on the parameter showing the activity of the myocardium of the heart in the time interval of the R wave and the parameter showing the activity of the myocardium of the heart in the time interval of the T wave; 3. The signal parsing device according to claim 1 or 2, wherein The analysis unit performs the following operations: Any one of the moment corresponding to the maximum value of the first unimodal distribution, the moment corresponding to the maximum slope of the first cumulative distribution function, the moment corresponding to the maximum value of the second unimodal distribution, and the moment corresponding to the maximum slope of the second cumulative distribution function is obtained as a first moment, and the first moment is one of the parameters showing the activity of the myocardium of the heart in the time interval of the R wave; Obtain any one of the moment corresponding to the maximum value of the third unimodal distribution, the moment corresponding to the maximum slope of the third cumulative distribution function, the moment corresponding to the maximum value of the fourth unimodal distribution, and the moment corresponding to the maximum slope of the fourth cumulative distribution function as the second moment, and the second moment is one of the parameters indicating the activity of the myocardium of the heart in the time interval of the T wave; Obtain the difference between the first moment and the second moment as a parameter indicating the activity of the myocardium of the heart.

4. A signal parsing device, wherein, Comprise: A biological information acquisition unit that acquires the waveform of the time interval of the R wave included in the waveform of the amount of one cardiac cycle of the heart to be analyzed as the object time waveform; and An analysis unit, The analysis unit performs the following operations: Obtain the approximate time waveform generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or the approximate time waveform generated by the result obtained by adding the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, plus the level value, and approximate the object time waveform. At least any one of at least a part of the parameters for determining the first unimodal distribution and at least a part of the parameters for determining the first cumulative distribution function is used as a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave, and at least any one of at least a part of the parameters for determining the second unimodal distribution and at least a part of the parameters for determining the second cumulative distribution function is used as a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave; Obtain a parameter indicating the activity of the myocardium of the heart through the operation of the parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave.

5. The signal analysis device according to claim 4, wherein, The analysis unit performs the following operations: Obtain any one of the moment corresponding to the maximum value of the first unimodal distribution and the moment corresponding to the maximum slope of the first cumulative distribution function as the first moment, and the first moment is one of the parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave; Obtain any one of the moment corresponding to the maximum value of the second unimodal distribution and the moment corresponding to the maximum slope of the second cumulative distribution function as the second moment, and the second moment is one of the parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave; Obtain the difference between the first moment and the second moment as a parameter indicating the activity of the myocardium of the heart.

6. A signal parsing device, wherein, Comprise: A biological information acquisition unit that acquires the waveform of the time interval of the T wave included in the waveform of the amount of one cardiac cycle of the heart to be analyzed as the object time waveform; and An analysis unit, The analysis unit performs the following operations: The waveform obtained by reversing the time axis of the object time waveform is used as the object reverse time waveform. When approximating the object reverse time waveform by obtaining the waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or the waveform generated by the result obtained by adding the level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, i.e., the approximate reverse time waveform, at least any one of at least a part of the parameters determining the third unimodal distribution and at least a part of the parameters determining the third cumulative distribution function is used as a parameter showing the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and at least any one of at least a part of the parameters determining the fourth unimodal distribution and at least a part of the parameters determining the fourth cumulative distribution function is obtained as a parameter showing the activity of the outer layer of the myocardium of the heart in the time interval of the T wave. By performing an operation on the parameter showing the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and the parameter showing the activity of the outer layer of the myocardium of the heart in the time interval of the T wave, a parameter showing the activity of the myocardium of the heart is obtained.

7. A signal parsing device, wherein, Comprising: A biological information acquisition unit that acquires the waveform of the time interval of the T wave included in the waveform showing the amount of one cycle of the cardiac cycle of the heart to be analyzed as the object time waveform; and An analysis unit, The analysis unit performs the following operations: Regarding the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function. When approximating the object time waveform by obtaining the time waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the approximate time waveform, or the time waveform generated by the result obtained by adding the level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the approximate time waveform, at least any one of at least a part of the parameters determining the third unimodal distribution and at least a part of the parameters determining the third cumulative distribution function is used as a parameter showing the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and at least any one of at least a part of the parameters determining the fourth unimodal distribution and at least a part of the parameters determining the fourth cumulative distribution function is obtained as a parameter showing the activity of the outer layer of the myocardium of the heart in the time interval of the T wave. The parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave is obtained by calculating the parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave, so as to obtain the parameter indicating the activity of the myocardium of the heart.

8. The signal parsing device according to claim 6 or 7, wherein The analysis unit performs the following operations: Either the moment corresponding to the maximum value of the third unimodal distribution or the moment corresponding to the maximum slope of the third cumulative distribution function is obtained as a first moment, and the first moment is one of the parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave; Either the moment corresponding to the maximum value of the fourth unimodal distribution or the moment corresponding to the maximum slope of the fourth cumulative distribution function is obtained as a second moment, and the second moment is one of the parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; The difference between the first moment and the second moment is obtained as the parameter indicating the activity of the myocardium of the heart.

9. A signal parsing method, wherein, It includes: A biological information acquisition step of obtaining the waveform of the time interval of the R wave included in the waveform of the quantity of one cardiac cycle of the heart of the analysis object as a first object time waveform, and obtaining the waveform of the time interval of the T wave included in the waveform of the quantity of one cardiac cycle as a second object time waveform; and An analysis step, In the analysis step, When approximating the first object time waveform by obtaining the time waveform, i.e., the first approximate time waveform, generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or the time waveform, i.e., the first approximate time waveform, generated by the result of adding the level value to the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, at least any one of at least a part of the parameters determining the first unimodal distribution, at least a part of the parameters determining the first cumulative distribution function, at least a part of the parameters determining the second unimodal distribution, and at least a part of the parameters determining the second cumulative distribution function is used as the parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave; The waveform obtained by reversing the time axis of the second object time waveform is used as the second object reverse time waveform; When approximating the second target inverse time waveform by obtaining a waveform, i.e., a second approximate inverse time waveform, generated by a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, or by obtaining a waveform, i.e., a second approximate inverse time waveform, generated by a result obtained by adding a level value to a difference or weighted difference between a cumulative distribution function of a third unimodal distribution, i.e., a third cumulative distribution function, and a cumulative distribution function of a fourth unimodal distribution, i.e., a fourth cumulative distribution function, at least any one of at least a part of parameters for determining the third unimodal distribution, at least a part of parameters for determining the third cumulative distribution function, at least a part of parameters for determining the fourth unimodal distribution, and at least a part of parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; By performing an operation on a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave, a parameter indicating the activity of the myocardium of the heart is obtained.

10. A signal parsing method, wherein, Comprising: A biological information acquisition step of obtaining a waveform of a time interval of an R wave included in a waveform of an amount of one cardiac cycle of the heart of an analysis target as a first target time waveform, and obtaining a waveform of a time interval of a T wave included in the waveform of the amount of one cardiac cycle as a second target time waveform; and An analysis step, In the analysis step, When approximating the first target time waveform by obtaining a time waveform, i.e., a first approximate time waveform, generated by a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, or by obtaining a time waveform, i.e., a first approximate time waveform, generated by a result obtained by adding a level value to a difference or weighted difference between a cumulative distribution function of a first unimodal distribution, i.e., a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution, i.e., a second cumulative distribution function, at least any one of at least a part of parameters for determining the first unimodal distribution, at least a part of parameters for determining the first cumulative distribution function, at least a part of parameters for determining the second unimodal distribution, and at least a part of parameters for determining the second cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave; Regarding the third unimodal distribution, its cumulative distribution function is regarded as the third cumulative distribution function; regarding the fourth unimodal distribution, its cumulative distribution function is regarded as the fourth cumulative distribution function; a function obtained by subtracting the third cumulative distribution function from 1 is regarded as the third inverse cumulative distribution function; and a function obtained by subtracting the fourth cumulative distribution function from 1 is regarded as the fourth inverse cumulative distribution function; At least any one of at least a part of the parameters for determining the third unimodal distribution, at least a part of the parameters for determining the third cumulative distribution function, at least a part of the parameters for determining the fourth unimodal distribution, and at least a part of the parameters for determining the fourth cumulative distribution function, when approximating the second target time waveform by using the time waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function as the second approximate time waveform, or the time waveform generated by the result obtained by adding the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function and a level value as the second approximate time waveform, is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; By performing an operation on the parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the myocardium of the heart in the time interval of the T wave, a parameter indicating the activity of the myocardium of the heart is obtained.

11. The signal parsing method according to claim 9 or 10, wherein, The analysis step includes: Obtaining any one of the moment corresponding to the maximum value of the first unimodal distribution, the moment corresponding to the maximum slope of the first cumulative distribution function, the moment corresponding to the maximum value of the second unimodal distribution, and the moment corresponding to the maximum slope of the second cumulative distribution function as a first moment, where the first moment is one of the parameters indicating the activity of the myocardium of the heart in the time interval of the R wave; Obtaining any one of the moment corresponding to the maximum value of the third unimodal distribution, the moment corresponding to the maximum slope of the third cumulative distribution function, the moment corresponding to the maximum value of the fourth unimodal distribution, and the moment corresponding to the maximum slope of the fourth cumulative distribution function as a second moment, where the second moment is one of the parameters indicating the activity of the myocardium of the heart in the time interval of the T wave; Obtaining the difference between the first moment and the second moment as a parameter indicating the activity of the myocardium of the heart.

12. A signal parsing method, wherein, Including: A biological information acquisition step of acquiring the waveform of the time interval of the R wave included in the waveform of the quantity indicating one cycle of the cardiac cycle of the heart of the analysis target as the target time waveform; and An analysis step, In the analysis step, At least any one of at least a part of the parameters that determine the first unimodal distribution and at least a part of the parameters that determine the first cumulative distribution function is used as a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave, and the approximate time waveform is obtained by taking the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, to obtain the time waveform, i.e., the approximate time waveform, or by taking the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, and then adding a level value to obtain the time waveform, i.e., the approximate time waveform, to approximate the target time waveform. At least any one of at least a part of the parameters that determine the second unimodal distribution and at least a part of the parameters that determine the second cumulative distribution function is used as a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave; By performing an operation on the parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave, a parameter indicating the activity of the myocardium of the heart is obtained.

13. The signal parsing method according to claim 12, wherein, The analysis step includes: Taking either the moment corresponding to the maximum value of the first unimodal distribution or the moment corresponding to the maximum slope of the first cumulative distribution function as the first moment, and the first moment is one of the parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave; Taking either the moment corresponding to the maximum value of the second unimodal distribution or the moment corresponding to the maximum slope of the second cumulative distribution function as the second moment, and the second moment is one of the parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave; Taking the difference between the first moment and the second moment as a parameter indicating the activity of the myocardium of the heart.

14. A signal parsing method, wherein, Including: A biological information acquisition step of acquiring the waveform of the time interval of the T wave included in the waveform of the quantity indicating one cycle of the cardiac cycle of the heart of the analysis target as the target time waveform; And An analysis step, In the analysis step, Taking the waveform obtained by reversing the time axis of the target time waveform as the target reverse time waveform, When approximating the target inverse time waveform by obtaining a waveform, i.e., an approximate inverse time waveform, generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or by obtaining a waveform, i.e., an approximate inverse time waveform, generated by the result of adding a level value to the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, at least any one of at least a part of the parameters for determining the third unimodal distribution and at least a part of the parameters for determining the third cumulative distribution function is used as a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and at least any one of at least a part of the parameters for determining the fourth unimodal distribution and at least a part of the parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; By performing an operation on the parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and the parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave, a parameter indicating the activity of the myocardium of the heart is obtained.

15. A signal parsing method, wherein, Comprising: A biological information acquisition step of acquiring, as a target time waveform, a waveform of the time interval of the T wave included in a waveform representing an amount of one cycle of the cardiac cycle of the heart of the analysis target; And An analysis step, In the analysis step, The cumulative distribution function of the third unimodal distribution is used as the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is used as the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is used as the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is used as the fourth inverse cumulative distribution function, When approximating the target time waveform by obtaining a time waveform, i.e., an approximate time waveform, generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or by obtaining a time waveform, i.e., an approximate time waveform, generated by the result of adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, at least any one of at least a part of the parameters for determining the third unimodal distribution and at least a part of the parameters for determining the third cumulative distribution function is used as a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and at least any one of at least a part of the parameters for determining the fourth unimodal distribution and at least a part of the parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; A parameter indicating the activity of the myocardium of the heart is obtained by calculating a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave.

16. The signal parsing method according to claim 14 or 15, wherein, The analyzing step includes: obtaining, as a first time, either a time corresponding to the maximum value of the third unimodal distribution or a time corresponding to the maximum slope of the third cumulative distribution function, the first time being one of the parameters indicating the activity of the inner layer of the myocardium of the heart in the time interval of the T wave; obtaining, as a second time, either a time corresponding to the maximum value of the fourth unimodal distribution or a time corresponding to the maximum slope of the fourth cumulative distribution function, the second time being one of the parameters indicating the activity of the outer layer of the myocardium of the heart in the time interval of the T wave; obtaining the difference between the first time and the second time as a parameter indicating the activity of the myocardium of the heart.

17. A recording medium stores a computer program that executes the following steps: A biological information acquisition step of acquiring, as a first target time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cardiac cycle of a heart to be analyzed, and acquiring, as a second target time waveform, a waveform of a time interval of a T wave included in the waveform of the amount of one cardiac cycle; and An analyzing step, In the analyzing step, when approximating the first target time waveform with a first approximate time waveform that is a time waveform generated by a difference or weighted difference between a first cumulative distribution function that is a cumulative distribution function of a first unimodal distribution and a second cumulative distribution function that is a cumulative distribution function of a second unimodal distribution, or a time waveform generated by a result obtained by adding a level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least any one of at least a part of the parameters for determining the first unimodal distribution, at least a part of the parameters for determining the first cumulative distribution function, at least a part of the parameters for determining the second unimodal distribution, and at least a part of the parameters for determining the second cumulative distribution function is obtained as a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave; using, as a second target reverse time waveform, a waveform obtained by reversing the time axis of the second target time waveform; When approximating the second target inverse time waveform with the waveform obtained by taking the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, which is the second approximate inverse time waveform, or with the waveform obtained by adding a level value to the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, which is the second approximate inverse time waveform, at least any one of at least a part of the parameters for determining the third unimodal distribution, at least a part of the parameters for determining the third cumulative distribution function, at least a part of the parameters for determining the fourth unimodal distribution, and at least a part of the parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; By performing an operation on the parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the myocardium of the heart in the time interval of the T wave, a parameter indicating the activity of the myocardium of the heart is obtained.

18. A recording medium that records a computer program, and the computer program executes the following steps: A biological information acquisition step of acquiring the waveform of the time interval of the R wave included in the waveform of the amount of one cardiac cycle of the heart of the analysis target as the first target time waveform, and acquiring the waveform of the time interval of the T wave included in the waveform of the amount of one cardiac cycle as the second target time waveform; and An analysis step, In the analysis step, When approximating the first target time waveform with the time waveform obtained by taking the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, which is the first approximate time waveform, or with the time waveform obtained by adding a level value to the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, which is the first approximate time waveform, at least any one of at least a part of the parameters for determining the first unimodal distribution, at least a part of the parameters for determining the first cumulative distribution function, at least a part of the parameters for determining the second unimodal distribution, and at least a part of the parameters for determining the second cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the R wave; Regarding the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, regarding the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, regarding the function obtained by subtracting the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and regarding the function obtained by subtracting the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function; When approximating the second object time waveform with the time waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the second approximate time waveform, or the time waveform generated by the result obtained by adding the level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, i.e., the second approximate time waveform, at least any one of at least a part of the parameters for determining the third unimodal distribution, at least a part of the parameters for determining the third cumulative distribution function, at least a part of the parameters for determining the fourth unimodal distribution, and at least a part of the parameters for determining the fourth cumulative distribution function is used as a parameter indicating the activity of the myocardium of the heart in the time interval of the T wave; By performing an operation on the parameter indicating the activity of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the myocardium of the heart in the time interval of the T wave, a parameter indicating the activity of the myocardium of the heart is obtained.

19. A recording medium storing a computer program that executes the following steps: A biological information acquisition step of acquiring, as an object time waveform, a waveform of a time interval of an R wave included in a waveform of an amount of one cardiac cycle of the heart of an analysis object; and An analysis step, In the analysis step, When approximating the object time waveform with the time waveform generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, i.e., the approximate time waveform, or the time waveform generated by the result obtained by adding the level value to the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, at least any one of at least a part of the parameters for determining the first unimodal distribution and at least a part of the parameters for determining the first cumulative distribution function is used as a parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave, and at least any one of at least a part of the parameters for determining the second unimodal distribution and at least a part of the parameters for determining the second cumulative distribution function is obtained as a parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave; By performing an operation on the parameter indicating the activity of the inner layer of the myocardium of the heart in the time interval of the R wave and the parameter indicating the activity of the outer layer of the myocardium of the heart in the time interval of the R wave, a parameter indicating the activity of the myocardium of the heart is obtained.

20. A recording medium storing a computer program that executes the following steps: A biological information acquisition step of acquiring, as an object time waveform, a waveform of a time interval of a T wave included in a waveform of an amount of one cardiac cycle of the heart of an analysis object; and An analysis step, In the analysis step, The waveform obtained by reversing the time axis of the object time waveform is used as the object reverse time waveform. When approximating the object reverse time waveform by obtaining the waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, which is the approximate reverse time waveform, or the waveform generated by the result obtained by adding the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, plus the level value, which is the approximate reverse time waveform, at least any one of at least a part of the parameters for determining the third unimodal distribution and at least a part of the parameters for determining the third cumulative distribution function is used as a parameter showing the activity of the inner layer of the myocardium of the heart in the time interval of the T wave, and at least any one of at least a part of the parameters for determining the fourth unimodal distribution and at least a part of the parameters for determining the fourth cumulative distribution function is obtained as a parameter showing the activity of the outer layer of the myocardium of the heart in the time interval of the T wave. By performing an operation on the parameter showing the activity of the inner layer of the myocardium of the heart in the time interval of the T wave and the parameter showing the activity of the outer layer of the myocardium of the heart in the time interval of the T wave, a parameter showing the activity of the myocardium of the heart is obtained.

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