Learning device, information providing device, learning method, information providing method, and program

Abstract

This learning device comprises a learning unit that uses a learning data set to train an estimation model which takes as input a set of cardiac action parameters and obtains heart condition information which is information indicating the condition of a heart corresponding to the cardiac action parameters. The cardiac action parameter set is a set of parameters representing an approximation function when a waveform of a time section included in a waveform of one period indicating a cardiac cycle of the heart is approximated by an approximation function including a difference of a cumulative distribution function or a weighted difference. The learning data set comprises multiple units (Y units) of learning data. Each of the multiple units (Y units) of learning data includes: a cardiac action parameter set of the heart which is the subject of the yth learning data unit where integers from 1 to Y are y; and heart condition information which is information indicating the condition of the heart which is the subject of the yth learning data unit.

Description

[Technical Field]

[0001] The present invention relates to a learning device, an information providing device, a learning method, an information providing method, and a program. [Background technology]

[0002] An electrocardiogram provides useful information for understanding the state of the heart. For example, an electrocardiogram can be used to determine whether a subject is in a state where there is a high possibility of developing heart failure (Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0003] [Non-Patent Document 1] Hiroshi Tanaka, "Methodology for Inverse Problems of Electrocardiograms," Medical Electronics and Biomedical Engineering, 1985, Vol. 23, No. 3, pp. 147-158 Summary of the Invention [Problem to be solved by the invention]

[0004] However, electrocardiogram waveforms are not always sufficient to understand the state of the heart. For example, even if electrocardiogram waveforms are similar, the onset of heart-related diseases may differ. As such, depending on the disease, it may be difficult to understand the state of the heart simply by looking at the electrocardiogram waveform itself. For example, in the case of heart failure, the onset can be suppressed by monitoring the state of the heart using electrocardiogram waveforms in daily life. To further improve the accuracy of onset suppression, it is possible to obtain other information using other technologies, such as blood sampling, but this is not practical in daily life. Therefore, depending on the disease, it may be necessary to understand the state of the heart based essentially only on the electrocardiogram waveform.

[0005] Furthermore, this situation is not limited to cases where the state of the heart is understood based on an electrocardiogram waveform. This situation is also common to cases where the state of the heart is understood based on one channel of time-series biological information related to the heartbeat acquired 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, or the like. Note that time-series biological information related to the heartbeat is, for example, a waveform indicating changes in cardiac potential, a waveform indicating changes in cardiac pressure, a waveform indicating changes in blood flow, or a waveform indicating changes in heart sounds. Note that an electrocardiogram waveform is also an example of time-series biological information related to the heartbeat.

[0006] In view of the above circumstances, an object of the present invention is to provide a technique for obtaining information useful for understanding the state of the heart from one channel of time-series biological information relating to the beating of the heart. [Means for solving the problem]

[0007] In one aspect of the present invention, a waveform of a time interval of an R wave included in a waveform for one cycle indicating a cardiac cycle of the heart is defined as a first target time waveform, a waveform of a time interval of a T wave included in the waveform is defined as a second target time waveform, a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverted time waveform, a cumulative distribution function of a first unimodal distribution is defined as a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution is defined as a second cumulative distribution function, and a first approximate time waveform is a time waveform resulting from the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, or a first approximated time waveform, which is a time waveform obtained by adding a first level value to the difference or weighted difference with the second cumulative distribution function, and which approximates the first target time waveform, and which includes a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value, a second approximate inverse time waveform which is a waveform obtained by subtracting the third cumulative distribution function and the fourth cumulative distribution function from a second level value, or a second approximate inverse time waveform which is a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, a second approximated time waveform that is a time waveform based on a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio between the weight of the third cumulative distribution function and the weight of the fourth cumulative distribution function, the second level value, or a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, when the second target inverse time waveform is approximated by an approximated inverse time waveform; orA second approximate time waveform is a time waveform obtained by adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, and the second group includes a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value when the second target time waveform is approximated. a residual time waveform is a time waveform of the difference between the first target time waveform and the first approximated time waveform, a time waveform of the difference between the second target time waveform and the second approximated time waveform, or a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximated inverse time waveform, a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function, and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function, and an approximated residual time waveform is a time waveform obtained by multiplying the fifth cumulative distribution function or the fifth cumulative distribution function by a weight, a parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a weight of the fifth cumulative distribution function, or an approximated residual time waveform, which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function, when the residual time waveform is approximated by the parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, the weight of the fifth cumulative distribution function, the weight of the sixth cumulative distribution function, and a ratio between the weight of the fifth cumulative distribution function and the weight of the sixth cumulative distribution function are defined as the multiple types of myocardial activity parameters included in the third group; one or more types of myocardial activity parameters included in the fourth group are parameters obtained by calculating multiple types of myocardial activity parameters from the multiple types of myocardial activity parameters included in the first group, the multiple types of myocardial activity parameters included in the second group, and the multiple types of myocardial activity parameters included in the third group; and a multiple types of myocardial activity parameters included in the first group obtained from a waveform representing one or more cardiac cycles of the heart.a learning device including a learning unit that uses a myocardial activity parameter set to be a set of one or more predetermined types of myocardial activity parameters from among the multiple types of myocardial activity parameters included in the second group, the multiple types of myocardial activity parameters included in the third group, and the one or more types of myocardial activity parameters included in the fourth group, and that the learning data set is made up of Y (Y is plural) pieces of learning data, and each of the Y pieces of learning data includes a myocardial activity parameter set of a heart that is the subject of the y-th learning data, where y is an integer between 1 and Y, inclusive, and cardiac state information that is information representing the state of the heart that is the subject of the y-th learning data, and that uses the learning data set to learn an estimation model that receives the myocardial activity parameter set as input and obtains cardiac state information that is information representing the state of the heart corresponding to the myocardial activity parameter.

[0008] In one aspect of the present invention, a waveform of a time interval of an R wave included in a waveform for one cycle indicating a cardiac cycle of the heart is defined as a first target time waveform, a waveform of a time interval of a T wave included in the waveform is defined as a second target time waveform, a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverted time waveform, a cumulative distribution function of a first unimodal distribution is defined as a first cumulative distribution function, and a cumulative distribution function of a second unimodal distribution is defined as a second cumulative distribution function, and a first approximate time waveform is a time waveform resulting from the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, or a first approximated time waveform, which is a time waveform obtained by adding a first level value to the difference or weighted difference with the second cumulative distribution function, and which approximates the first target time waveform, and which includes a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value, a second approximate inverse time waveform which is a waveform obtained by subtracting the third cumulative distribution function and the fourth cumulative distribution function from a second level value, or a second approximate inverse time waveform which is a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, a second approximated time waveform that is a time waveform based on a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio between the weight of the third cumulative distribution function and the weight of the fourth cumulative distribution function, the second level value, or a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, when the second target inverse time waveform is approximated by an approximated inverse time waveform; orA second approximate time waveform is a time waveform obtained by adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, and the second group includes a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value when the second target time waveform is approximated. a residual time waveform is a time waveform of the difference between the first target time waveform and the first approximated time waveform, a time waveform of the difference between the second target time waveform and the second approximated time waveform, or a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximated inverse time waveform, a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function, and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function, and an approximated residual time waveform is a time waveform obtained by multiplying the fifth cumulative distribution function or the fifth cumulative distribution function by a weight, a parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a weight of the fifth cumulative distribution function, or an approximated residual time waveform, which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function, when the residual time waveform is approximated by the parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, the weight of the fifth cumulative distribution function, the weight of the sixth cumulative distribution function, and a ratio between the weight of the fifth cumulative distribution function and the weight of the sixth cumulative distribution function are defined as the multiple types of myocardial activity parameters included in the third group; one or more types of myocardial activity parameters included in the fourth group are parameters obtained by calculating multiple types of myocardial activity parameters from the multiple types of myocardial activity parameters included in the first group, the multiple types of myocardial activity parameters included in the second group, and the multiple types of myocardial activity parameters included in the third group; and a multiple types of myocardial activity parameters included in the first group obtained from a waveform representing one or more cardiac cycles of the heart.The information providing device includes a state information generating unit that stores in advance an estimation model that receives a myocardial activity parameter set, which is a set of one or more predetermined types of myocardial activity parameters from among the plurality of types of myocardial activity parameters included in the second group, the plurality of types of myocardial activity parameters included in the third group, and one or more types of myocardial activity parameters included in the fourth group, and that receives as input the myocardial activity parameter set to obtain cardiac state information that is information representing the state of the heart corresponding to the myocardial activity parameter set, and that uses the estimation model to receive as input the myocardial activity parameter set of an information providing target heart, which is a heart that is the target of information provision, to obtain cardiac state information of the information providing target heart.

[0009] One aspect of the present invention is a learning method executed by a learning device, the method comprising: defining a waveform of a time interval of an R wave included in a waveform for one cycle representing a cardiac cycle of the heart as a first target time waveform; defining a waveform of a time interval of a T wave included in the first target time waveform as a second target time waveform; defining a waveform obtained by reversing the time axis of the second target time waveform as a second target inverted time waveform; defining a cumulative distribution function of a first unimodal distribution as a first cumulative distribution function; defining a cumulative distribution function of a second unimodal distribution as a second cumulative distribution function; and defining a first approximate time waveform as a time waveform based on the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function. is a first approximate time waveform that is a time waveform obtained by adding a first level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, and includes, in a first group, a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value, when approximating the first target time waveform. a third cumulative distribution function being a cumulative distribution function of a third unimodal distribution, a fourth cumulative distribution function being a cumulative distribution function of a fourth unimodal distribution, a third inverse cumulative distribution function being a function obtained by subtracting the third cumulative distribution function from 1, and a fourth inverse cumulative distribution function being a function obtained by subtracting the fourth cumulative distribution function from 1; a second approximate inverse time waveform being a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function; a second approximated inverse time waveform, which is a time waveform resulting from approximating the second target inverse time waveform, and which is a time waveform resulting from a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio between the weight of the third cumulative distribution function and the weight of the fourth cumulative distribution function, the second level value, or a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, orA second approximate time waveform is a time waveform obtained by adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, and the second group includes a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value when the second target time waveform is approximated. a residual time waveform is a time waveform of the difference between the first target time waveform and the first approximated time waveform, a time waveform of the difference between the second target time waveform and the second approximated time waveform, or a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximated inverse time waveform, a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function, and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function, and an approximated residual time waveform is a time waveform obtained by multiplying the fifth cumulative distribution function or the fifth cumulative distribution function by a weight, a parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a weight of the fifth cumulative distribution function, or an approximated residual time waveform which is a time waveform resulting from the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function; a parameter specifying the fifth unimodal distribution when the residual time waveform is approximated with a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, the weight of the fifth cumulative distribution function, the weight of the sixth cumulative distribution function, and a ratio between the weight of the fifth cumulative distribution function and the weight of the sixth cumulative distribution function; a plurality of types of myocardial activity parameters included in a third group, and one or more types of myocardial activity parameters included in a fourth group are parameters obtained by calculating a plurality of types of myocardial activity parameters from the plurality of types of myocardial activity parameters included in the first group, the plurality of types of myocardial activity parameters included in the second group, and the plurality of types of myocardial activity parameters included in the third group; and a plurality of types of myocardial activity parameters included in the first group obtained from a waveform representing one or more cardiac cycles of the heart.The learning method includes a learning step of learning an estimation model that uses the training data set to input the myocardial activity parameter sets and obtains cardiac status information that represents the cardiac status of the heart corresponding to the myocardial activity parameters, the learning model comprising a set of predetermined one or more myocardial activity parameters from among the plurality of types of myocardial activity parameters included in the second group, the plurality of types of myocardial activity parameters included in the third group, and the one or more types of myocardial activity parameters included in the fourth group, the training data set being composed of Y (Y is plural) pieces of training data, each of which includes a myocardial activity parameter set of the heart that is the subject of the y-th training data, where y is an integer between 1 and Y, inclusive, and cardiac status information that represents the cardiac status of the heart that is the subject of the y-th training data, the training data set being inputted with the myocardial activity parameter sets.

[0010] One aspect of the present invention is an information providing method executed by an information providing device, the method comprising: defining a waveform of a time interval of an R wave included in a waveform for one cycle representing a cardiac cycle of the heart as a first target time waveform; defining a waveform of a time interval of a T wave included in the first target time waveform as a second target time waveform; defining a waveform obtained by reversing the time axis of the second target time waveform as a second target inverted time waveform; defining a cumulative distribution function of a first unimodal distribution as a first cumulative distribution function; defining a cumulative distribution function of a second unimodal distribution as a second cumulative distribution function; and defining a first approximated time waveform as a time waveform based on the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function. or a first approximate time waveform which is a time waveform obtained by adding a first level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, and in which a first group includes a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value when the first target time waveform is approximated. a third cumulative distribution function is a cumulative distribution function of a third unimodal distribution, a fourth cumulative distribution function is a cumulative distribution function of a fourth unimodal distribution, a third inverse cumulative distribution function is a function obtained by subtracting the third cumulative distribution function from 1, and a fourth inverse cumulative distribution function is a function obtained by subtracting the fourth cumulative distribution function from 1; a second approximate inverse time waveform is a waveform obtained by the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, or a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function. a second approximated time waveform that is a time waveform based on a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio between the weight of the third cumulative distribution function and the weight of the fourth cumulative distribution function, the second level value, or a difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, when the second target inverse time waveform is approximated by a second approximated inverse time waveform that is a time waveform based on a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio between the weight of the third cumulative distribution function and the weight of the fourth cumulative distribution function, the second level value, orA second approximate time waveform is a time waveform obtained by adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, and the second group includes a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value when the second target time waveform is approximated. a residual time waveform is a time waveform of the difference between the first target time waveform and the first approximated time waveform, a time waveform of the difference between the second target time waveform and the second approximated time waveform, or a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximated inverse time waveform, a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function, and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function, and an approximated residual time waveform is a time waveform obtained by multiplying the fifth cumulative distribution function or the fifth cumulative distribution function by a weight, a parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a weight of the fifth cumulative distribution function, or an approximated residual time waveform, which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function, when the residual time waveform is approximated by the parameter specifying the fifth unimodal distribution when a time waveform is approximated, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, the weight of the fifth cumulative distribution function, the weight of the sixth cumulative distribution function, and a ratio between the weight of the fifth cumulative distribution function and the weight of the sixth cumulative distribution function are defined as the multiple types of myocardial activity parameters included in the third group; one or more types of myocardial activity parameters included in the fourth group are parameters obtained by calculating multiple types of myocardial activity parameters from the multiple types of myocardial activity parameters included in the first group, the multiple types of myocardial activity parameters included in the second group, and the multiple types of myocardial activity parameters included in the third group; and a multiple types of myocardial activity parameters included in the first group obtained from a waveform representing one or more cardiac cycles of the heart.The information providing method includes a pre-stored estimation model for obtaining cardiac status information representing the state of the heart corresponding to a myocardial activity parameter set, the myocardial activity parameter set being a set of one or more predetermined types of myocardial activity parameters selected from the plurality of types of myocardial activity parameters included in the second group, the plurality of types of myocardial activity parameters included in the third group, and one or more types of myocardial activity parameters included in the fourth group, and the method includes a status information generating step for obtaining cardiac status information of the information provision target heart, the heart being the target of information provision, using the estimation model as input.

[0011] One aspect of the present invention is a program for causing a computer to function as the learning device described above.

[0012] One aspect of the present invention is a program for causing a computer to function as the information providing device described above. [Effects of the Invention]

[0013] The present invention makes it possible to provide a technique for obtaining information useful for understanding the state of the heart from one channel of time-series biological information related to the beating of the heart. [Brief explanation of the drawings]

[0014] [Figure 1] FIG. 1 is a diagram showing an example of a hardware configuration of a signal analyzing device 1 according to a first embodiment. [Figure 2] FIG. 10 is a diagram illustrating a function obtained by multiplying a first cumulative distribution function by a weight, a function obtained by multiplying a second cumulative distribution function by a weight, and an approximate time waveform that is a weighted difference between the first cumulative distribution function and the second cumulative distribution function for a first target time waveform. [Figure 3] FIG. 10 is a diagram schematically illustrating a function obtained by multiplying the third inverse cumulative distribution function by a weight, a function obtained by multiplying the fourth inverse cumulative distribution function by a weight, and an approximate time waveform that is a weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, for a second target time waveform. [Figure 4] FIG. 4 is a diagram showing an example of the results of fitting the electrocardiogram waveform of the target heart with four cumulative distribution functions in the first embodiment. [Figure 5] FIG. 10 is an explanatory diagram illustrating that the difference between two cumulative distribution functions in the first embodiment can be fitted to a waveform that is substantially identical to the falling waveform of the T wave. [Figure 6] FIG. 10 is a diagram schematically illustrating a function for a first target time waveform obtained by multiplying a first cumulative distribution function 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 the weighted difference between the first cumulative distribution function and the second cumulative distribution function. [Figure 7] FIG. 10 is a diagram schematically illustrating, for a second target time waveform, a function obtained by multiplying a third inverse cumulative distribution function 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 the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function. [Figure 8] FIG. 1 is a diagram schematically illustrating a delta wave included in a target time waveform. [Figure 9] FIG. 2 is a diagram showing an example of the functional configuration of a control unit 11 in the first embodiment. [Figure 10] 4 is a flowchart showing an example of the flow of processing executed by the signal analyzing device 1 in the first embodiment. [Figure 11] FIG. 1 is a first diagram showing an example of an analysis result of the signal analyzing device 1 in the first embodiment. [Figure 12] FIG. 2 is a second diagram showing an example of an analysis result of the signal analyzing device 1 in the first embodiment. [Figure 13] FIG. 3 is a third diagram showing an example of an analysis result of the signal analyzing device 1 in the first embodiment. [Figure 14] FIG. 4 is a fourth diagram showing an example of an analysis result of the signal analyzing device 1 in the first embodiment. [Figure 15] FIG. 3 is a first explanatory diagram of an example of an electrocardiogram of a premature ventricular contraction analyzed by the signal analyzing device 1 of the first embodiment. [Figure 16]FIG. 2 is a second explanatory diagram of an example of an electrocardiogram of a premature ventricular contraction analyzed by the signal analyzing device 1 of the first embodiment. [Figure 17] FIG. 10 is a third explanatory diagram of an example of an electrocardiogram of a premature ventricular contraction analyzed by the signal analyzing device 1 of the first embodiment. [Figure 18] FIG. 1 is a first explanatory diagram illustrating an electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1 analyzed by the signal analysis device 1 according to the first embodiment. [Figure 19] FIG. 2 is a second explanatory diagram illustrating an analysis of an electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 according to the first embodiment. [Figure 20] FIG. 3 is a third explanatory diagram showing an analysis of an electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 according to the first embodiment. [Figure 21] FIG. 2 is a diagram showing a first example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 22] FIG. 4 is a diagram showing a second example of electrocardiogram analysis performed by the signal analyzing device 1 according to the first embodiment. [Figure 23] FIG. 10 is a diagram showing a third example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 24] FIG. 10 is a diagram showing a fourth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 25] FIG. 10 is a diagram showing a fifth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 26] FIG. 10 is a diagram showing a sixth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 27] FIG. 10 is a diagram showing a seventh example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 28] FIG. 10 is a diagram showing an eighth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 29] FIG. 13 is a diagram showing a ninth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 30]FIG. 17 is a diagram showing a tenth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 31] FIG. 11 is a diagram showing an eleventh example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 32] FIG. 12 is a diagram showing a twelfth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 33] FIG. 13 is a diagram showing a thirteenth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 34] FIG. 14 is a diagram showing a fourteenth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 35] FIG. 15 is a diagram showing a fifteenth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 36] FIG. 16 is a diagram showing a 16th example of electrocardiogram analysis performed by the signal analyzing device 1 according to the first embodiment. [Figure 37] FIG. 17 is a diagram showing a 17th example of electrocardiogram analysis performed by the signal analyzing device 1 according to the first embodiment. [Figure 38] FIG. 18 is a diagram showing an 18th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 39] FIG. 19 is a diagram showing a 19th example of electrocardiogram analysis performed by the signal analyzing device 1 according to the first embodiment. [Figure 40] FIG. 11 is a diagram showing a twentieth example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 41] FIG. 21 is a diagram showing a 21st example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 42] FIG. 22 is a diagram showing a 22nd example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 43] FIG. 23 is a diagram showing a 23rd example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 44] FIG. 24 is a diagram showing a 24th example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 45]FIG. 25 is a diagram showing a 25th example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 46] FIG. 26 is a diagram showing a 26th example of electrocardiogram analysis performed by the signal analyzing device 1 according to the first embodiment. [Figure 47] FIG. 27 is a diagram showing a 27th example of electrocardiogram analysis performed by the signal analyzing device 1 according to the first embodiment. [Figure 48] FIG. 28 is a diagram showing a 28th example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 49] FIG. 29 is a diagram showing a 29th example of an electrocardiogram analyzed by the signal analyzing device 1 in the first embodiment. [Figure 50] FIG. 10 is a diagram showing a 30th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 51] FIG. 10 is a diagram showing a 31st example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 52] FIG. 10 is a diagram showing a 32nd example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 53] FIG. 10 is a diagram showing a 33rd example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 54] FIG. 10 is a diagram showing a 34th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 55] FIG. 10 is a diagram showing a 35th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 56] FIG. 10 is a diagram showing a 36th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 57] FIG. 10 is a diagram showing a 37th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 58] FIG. 10 is a diagram showing a 38th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 59] FIG. 10 is a diagram showing a 39th example of an electrocardiogram analyzed by the signal analyzing device 1 according to the first embodiment. [Figure 60]FIG. 10 is a diagram showing an example of the functional configuration of an information providing system 200 according to a second embodiment. [Figure 61] 10 is a flowchart showing an example of the flow of processing executed by the learning device 300 in the second embodiment. [Figure 62] 10 is a flowchart showing an example of the flow of processing executed by an information providing device 400 in the second embodiment. [Figure 63] FIG. 10 is a diagram showing an example of the functional configuration of an information providing system 201 according to a third embodiment. [Figure 64] 10 is a flowchart showing an example of the flow of processing executed by a learning device 301 in the third embodiment. [Figure 65] 10 is a flowchart showing an example of the flow of processing executed by an information providing device 401 in the third embodiment. [Figure 66] FIG. 10 is a diagram showing an example of the functional configuration of an information providing system 202 in a modified example of the second and third embodiments. [Figure 67] FIG. 10 is a diagram showing an example of the functional configuration of an information providing system 202 in a modified example of the second and third embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0015] First Embodiment FIG. 1 is a diagram illustrating an example of the hardware configuration of a signal analysis device 1 according to a first embodiment. For simplicity, the signal analysis device 1 will be described below using an example in which analysis is performed based on the waveform of one channel of an electrocardiogram. However, the signal analysis device 1 can perform similar analysis based on time-series biological information related to cardiac pulsation, not limited to electrocardiogram waveforms. The time-series biological information related to cardiac pulsation may include, for example, a waveform indicating changes in cardiac potential, a waveform indicating changes in cardiac pressure, a waveform indicating changes in blood flow, or a waveform indicating changes in heart sounds. Therefore, the signal analysis device 1 is not limited to waveforms of electrical signals acquired from the body surface, but may also use any waveform indicating a cardiac cycle acquired from any point, regardless of whether it is on the body surface or inside the body, using a sensor in contact with the body surface, a sensor close to the body surface, a sensor inserted into the body, a sensor implanted inside the body, or the like.

[0016] That is, the signal analyzing device 1 may use a waveform indicating changes in cardiac pressure as time-series biological information related to the heartbeat, instead of an electrocardiogram waveform. Also, the signal analyzing device 1 may use a waveform indicating changes in blood flow as time-series biological information related to the heartbeat, instead of an electrocardiogram waveform. Also, the signal analyzing device 1 may use a waveform indicating changes in heart sounds as time-series biological information related to the heartbeat, instead of an electrocardiogram waveform. Note that the electrocardiogram waveform is also an example of time-series biological information related to the heartbeat. Note that the time-series biological information related to the heartbeat may be time-series biological information related to the periodic heartbeat.

[0017] The signal analysis device 1 acquires an electrocardiogram waveform of a heart to be analyzed (hereinafter referred to as "target heart"). Based on the acquired electrocardiogram waveform, the signal analysis device 1 acquires parameters indicating activity of the myocardium of the target heart (hereinafter referred to as "myocardial activity parameters") including at least one of parameters indicating activity of the outer layer of the myocardium of the target heart (hereinafter referred to as "epimyocardium parameters") and parameters indicating activity of the inner layer of the myocardium of the target heart (hereinafter referred to as "endomyocardium parameters").

[0018] Here, we will explain the relationship between myocardial activity and electrocardiogram waveforms. In the medical field, a model known as the cardiac electromotive dipole model (Reference 1) is known to explain the relationship between myocardial activity and electrocardiograms. According to the cardiac electromotive dipole model, the myocardium is modeled as consisting of two layers: an outer myocardium and an inner myocardium.

[0019] Reference 1: Yoshifumi Tanaka, "Understanding the Electrocardiogram Waveform from Its Origin: Deciphering the Myocardial Action Potential," Gakken Medical Shujunsha (2012)

[0020] In the cardiac electromotive dipole model, the epimyocardium and endomyocardium are modeled as different sources of electromotive force. According to the cardiac electromotive dipole model, the composite wave of the epicardial action potential and endocardial action potential approximately matches the time change of the body surface potential observed at the body surface. A graph showing the time change of the body surface potential is the waveform of an electrocardiogram. The epicardial action potential is the result of directly measuring the change in electromotive force caused by the pulsation of the epimyocardium by inserting a catheter electrode. The endocardial action potential is the result of directly measuring the change in electromotive force caused by the pulsation of the endomyocardium by inserting a catheter electrode. This concludes the outline of the cardiac electromotive dipole model.

[0021] In the cardiac electromotive dipole model, the myocardium epilayer is a collection of cells. Therefore, the timing of the pulsation of cells in the epilayer during one pulsation of the myocardium is not necessarily the same for all cells, and there may be a distribution in the pulsation timing. The same is true for the endomyocardium. In other words, the timing of the pulsation of cells in the endomyocardium during one pulsation of the myocardium is not necessarily the same for all cells, and there may be a distribution in the pulsation timing. However, the cardiac electromotive dipole model does not take into account the possibility that such a distribution in the pulsation timing of cells exists.

[0022] Furthermore, there is a distribution in the distance between each cell and the electrode on the body surface, and the structure of the body tissue between each cell and the electrode on the body surface is not uniform. Therefore, the conversion efficiency at which the excitation of cells in the outer myocardium is reflected in the electrocardiogram waveform is not necessarily uniform for all cells, and there may be a distribution in the conversion efficiency at which pulsation is reflected in the electrocardiogram waveform. Similarly, the conversion efficiency at which the excitation of cells in the inner myocardium is reflected in the electrocardiogram waveform is not necessarily uniform for all cells, and there may be a distribution in the conversion efficiency at which pulsation is reflected in the electrocardiogram waveform. However, the cardiac electromotive force dipole model does not take into account the possibility that there may be a distribution in the conversion efficiency at which cell pulsation is reflected in the electrocardiogram waveform.

[0023] The signal analysis device 1 takes into consideration the possibility that there is a distribution in the timing of cell pulsation and the possibility that there is a distribution in the conversion efficiency at which cell pulsation is reflected in the electrocardiogram waveform, and performs an analysis assuming that the distribution of the timing at which the start of pulsation of each cell in the outer layer of myocardium appears in the electrocardiogram waveform, the distribution of the timing at which the start of pulsation of each cell in the inner layer of myocardium appears in the electrocardiogram waveform, the distribution of the timing at which the end of pulsation of each cell in the outer layer of myocardium appears in the electrocardiogram waveform, and the distribution of the timing at which the end of pulsation of each cell in the inner layer of myocardium appears in the electrocardiogram waveform are each Gaussian distributed. That is, the signal analysis device 1 performs an analysis assuming that the start of activity in the endomyosinte myocardium by all cells in the endomyosinte myocardium is included as a cumulative Gaussian distribution in the electrocardiogram waveform, the start of activity in the epimyosinte myocardium by all cells in the epimyosinte myocardium is included as a cumulative Gaussian distribution in the electrocardiogram waveform, the end of activity in the endomyosinte myocardium by all cells in the endomyosinte myocardium is included as a cumulative Gaussian distribution in the electrocardiogram waveform, and the end of activity in the epimyosinte myocardium by all cells in the epimyosinte myocardium is included as a cumulative Gaussian distribution in the electrocardiogram waveform.

[0024] Although the signal analyzing device 1 preferably uses a cumulative Gaussian distribution function, a sigmoid function, a Gompertz function, a logistic function, or the like may be used instead of the cumulative Gaussian distribution function. That is, the signal analyzing device 1 may use, instead of the cumulative Gaussian distribution function, a cumulative distribution function of a unimodal distribution, i.e., a cumulative distribution function corresponding to a distribution in which the value monotonically increases until it reaches a maximum value and then monotonically decreases after the maximum value is reached. However, the cumulative distribution function used by the signal analyzing device 1 must be a cumulative distribution function whose shape can be specified by a parameter representing the shape of the cumulative distribution function or a parameter representing the shape of the unimodal distribution that is the accumulation source of the cumulative distribution function. Hereinafter, a parameter representing the shape of the cumulative distribution function (i.e., a parameter specifying the cumulative distribution function) is referred to as a shape parameter of the cumulative distribution function, and a parameter representing the shape of the unimodal distribution (i.e., a parameter specifying the unimodal distribution) is referred to as a shape parameter of the unimodal distribution. However, it goes without saying that the shape parameters of the cumulative distribution function and the shape parameters of the unimodal distribution are essentially the same. For example, if the cumulative distribution function used by the signal analyzing device 1 is a cumulative Gaussian distribution function, the standard deviation (or variance) and mean value of the Gaussian distribution, which is the accumulation source of the cumulative Gaussian distribution function, are shape parameters of the unimodal distribution and also shape parameters of the cumulative distribution function.

[0025] The signal analysis device 1 sets a waveform of either an R wave or a T wave in a time interval included in one cycle of the acquired electrocardiogram waveform of the target heart as a target time waveform, and obtains, as parameters representing characteristics of the target time waveform, i.e., myocardial activity parameters, parameters specifying the first unimodal distribution or the first cumulative distribution function and parameters specifying the second unimodal distribution or the second cumulative distribution function when approximating the target time waveform with a time waveform based on the difference or weighted difference between a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, and a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution (hereinafter referred to as an "approximate time waveform"). Hereinafter, approximating the target time waveform with the approximate time waveform, i.e., specifying the approximate time waveform, is referred to as "fitting," and the first cumulative distribution function and the second cumulative distribution function included in the approximate time waveform are referred to as a "fitting result." In addition, when approximating by a weighted difference, the signal analysis device 1 may acquire the weight to be assigned to the first cumulative distribution function and the weight to be assigned to the second cumulative distribution function as parameters representing the characteristics of the target time waveform (i.e., myocardial activity parameters), or may acquire the ratio between the weight to be assigned to the first cumulative distribution function and the weight to be assigned to the second cumulative distribution function as parameters representing the characteristics of the target time waveform (i.e., myocardial activity parameters).

[0026] In the case of approximating a target time waveform with an approximated time waveform based on the difference between a first cumulative distribution function and a second cumulative distribution function, for example, the signal analyzing device 1 generates a time waveform based on the difference between the first cumulative distribution function and the second cumulative distribution function (hereinafter referred to as a "candidate time waveform") using each of M × N combinations of parameters specifying a cumulative distribution function for each of a plurality (M) of first cumulative distribution function candidates and parameters specifying a cumulative distribution function for each of a plurality (N) of second cumulative distribution function candidates, identifies the candidate time waveform closest to the target time waveform from the generated M × N candidate time waveforms as the approximate time waveform, and acquires the parameters specifying the candidate first cumulative distribution function and the parameter specifying the candidate second cumulative distribution function used to generate the identified approximate time waveform as parameters representing the characteristics of the target time waveform. The process of identifying the candidate time waveform closest to the target time waveform as the approximate time waveform may be performed, for example, by a process of identifying a candidate time waveform that minimizes the squared error between the candidate time waveform and the target time waveform.

[0027] Alternatively, for example, the signal analysis device 1 obtains a candidate time waveform, which is a time waveform resulting from the difference between a candidate first cumulative distribution function and a candidate second cumulative distribution function that approximate the target time waveform, and updates at least one of the parameters that specify each cumulative distribution function in a direction that reduces the squared error between the candidate time waveform and the target time waveform, until the squared error becomes equal to or less than a predetermined standard, or by repeating this a predetermined number of times, identifies the finally obtained candidate time waveform as an approximate time waveform, and acquires the parameters that specify the candidate first cumulative distribution function and the parameters that specify the candidate second cumulative distribution function used to generate the identified approximate time waveform as parameters that represent the characteristics of the target time waveform.

[0028] In the case of approximating a target time waveform with an approximated time waveform based on a weighted difference between a first cumulative distribution function and a second cumulative distribution function, for example, the signal analysis device 1 generates a candidate time waveform, which is a time waveform based on a weighted difference between the first cumulative distribution function and the second cumulative distribution function, using each of combinations (K×L×M×N ways) of parameters specifying the cumulative distribution function for each of a plurality (M) of candidates for the first cumulative distribution function, parameters specifying the cumulative distribution function for each of a plurality (N) of candidates for the second cumulative distribution function, a plurality (K) of candidates for weights to be assigned to the first cumulative distribution function, and a plurality (L) of candidates for weights to be assigned to the second cumulative distribution function, and identifies the candidate time waveform that is closest to the target time waveform from the generated K×L×M×N ways of candidate time waveforms as the approximated time waveform, and acquires the parameters specifying the candidate first cumulative distribution function, the parameters specifying the candidate second cumulative distribution function, the weight to be assigned to the first cumulative distribution function, and the weight to be assigned to the second cumulative distribution function that were used to generate the identified approximated time waveform as parameters representing the characteristics of the target time waveform.

[0029] Alternatively, for example, the signal analysis device 1 obtains a candidate time waveform, which is a time waveform obtained by the weighted difference between a candidate first cumulative distribution function and a candidate second cumulative distribution function that approximate the target time waveform, and updates at least one of the parameters that specify each cumulative distribution function and the weights that are assigned to each cumulative distribution function in a direction that reduces the squared error between the candidate time waveform and the target time waveform, until the squared error becomes equal to or less than a predetermined standard, or repeats this a predetermined number of times, thereby specifying the finally obtained candidate time waveform as an approximated time waveform, and acquiring the parameters that specify the candidate first cumulative distribution function, the parameters that specify the candidate second cumulative distribution function, the weights that are assigned to the first cumulative distribution function, and the weights that are assigned to the second cumulative distribution function that were used to generate the specified approximate time waveform as parameters that represent the characteristics of the target time waveform.

[0030] Hereinafter, the process of acquiring parameters representing the characteristics of a target time waveform included in one cycle of the acquired electrocardiogram waveform of the target heart will be referred to as myocardial activity information parameter acquisition process.

[0031] If the information representing time is x, and Gaussian distributions are used as the first and second unimodal distributions, the first unimodal distribution is expressed by the following formula (1), the first cumulative distribution function f1(x) is expressed by formula (2), the second unimodal distribution is expressed by formula (3), and the second cumulative distribution function f2(x) is expressed by formula (4).

[0032]

number

[0033]

number

[0034]

number

[0035]

number

[0036] Equation (1) has a mean of μ1 and a standard deviation of σ1 (variance of σ1 2 ) is a Gaussian distribution (normal distribution). Equation (3) shows that the mean is μ2 and the standard deviation is σ2 (variance is σ2 2 ) is a Gaussian distribution (normal distribution). Equation (2) is the cumulative distribution function of equation (1). Equation (4) is the cumulative distribution function of equation (3). "erf" is a sigmoid function (error function). The unit of the information x representing time is arbitrary; for example, the sample number or relative time starting from one cycle of the electrocardiogram waveform can be used as the information x representing time.

[0037] The difference between the first cumulative distribution function and the second cumulative distribution function is expressed, for example, by the following formula (5): The function expressed by the following formula (5) is a function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0038]

number

[0039] That is, when the target time waveform is approximated by an approximated time waveform that is the difference between the first cumulative distribution function and the second cumulative distribution function, the mean μ1 and standard deviation σ1, which are parameters that specify the first unimodal distribution or the first cumulative distribution function, and the mean μ2 and standard deviation σ2, which are parameters that specify the second unimodal distribution or the second cumulative distribution function, are acquired as parameters that represent the characteristics of the target time waveform. Note that instead of acquiring the standard deviation as a parameter, the variance may be acquired as a parameter. The same applies to the following descriptions regarding acquiring the standard deviation as a parameter.

[0040] The weighted difference between the first cumulative distribution function and the second cumulative distribution function is expressed, for example, by the following formula (6), where the weight of the first cumulative distribution function is k1 and the weight of the second cumulative distribution function is k2. The function expressed by the following formula (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.

[0041]

number

[0042] That is, when the target time waveform is approximated by an approximated time waveform which is a weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least the mean μ1 and standard deviation σ1 which are parameters specifying the first unimodal distribution or the first cumulative distribution function, and the mean μ2 and standard deviation σ2 which are parameters specifying the second unimodal distribution or the second cumulative distribution function are acquired as parameters representing the characteristics of the target time waveform. Note that 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) between the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function may also be acquired as a parameter representing the characteristics of the target time waveform.

[0043] Since the function of equation (6) is a function obtained by subtracting a function obtained by multiplying the second cumulative distribution function by a weight k2 from a function obtained by multiplying the first cumulative distribution function by a weight k1, both weights k1 and k2 are positive values. However, if the target heart is in an unusual 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, the signal analyzing device 1 may perform fitting so that both weights k1 and k2 are positive values, but it is not essential to perform fitting so that both weights k1 and k2 are positive values.

[0044] In addition, the signal analysis device 1 may acquire parameters representing the characteristics of the above-mentioned target time waveforms for each of the first target time waveform and the second target time waveform, out of the R waves and T waves contained in one cycle of the acquired waveform of the electrocardiogram of the target heart, with the R waves being the first target time waveform and the T waves being the second target time waveform.

[0045] For example, when approximating the first target time waveform (i.e., R wave) by the difference between the first cumulative distribution function and the second cumulative distribution function, the first cumulative distribution function f a (x) to the second cumulative distribution function f b The first target time waveform is approximated by the approximate time waveform of Equation (9), which is a function obtained by subtracting (x), and the mean μ a and standard deviation σ a and the mean μ, which is a parameter specifying the second cumulative distribution function. b and standard deviation σ b and are acquired as parameters representing the characteristics of the first target time waveform (that is, the R wave).

[0046]

number

[0047]

number

[0048]

number

[0049] For example, when approximating the first target time waveform (i.e., R wave) by the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the first cumulative distribution function f a (x) has weight k a The second cumulative distribution function f expressed by equation (8) is obtained from the function multiplied by b (x) has weight k b The first target time waveform is approximated by the approximate time waveform of Equation (10), which is a function obtained by subtracting a function multiplied by a and standard deviation σ a and the mean μ, which is a parameter specifying the second cumulative distribution function. b and standard deviation σ b and are at least obtained as parameters representing the characteristics of the first target time waveform (i.e., R wave). Note that the weight k a and the weight k of the second cumulative distribution function b , or the weight k of the first cumulative distribution function a and the weight k of the second cumulative distribution function b The ratio (k a / k b , or k b / k a ) may also be acquired as a parameter representing the characteristic of the parameter representing the characteristic of the first target time waveform (that is, the R wave).

[0050]

number

[0051] The function of equation (10) is weighted by the first cumulative distribution function k a The weight k is added to the second cumulative distribution function from the function multiplied by b Since it is a function obtained by subtracting a function multiplied by a and weight k b Both are positive values. However, if the target heart is in a specific state, the weight k obtained by fittinga and weight k b Therefore, the signal analyzing device 1 determines whether at least one of the weights k a and weight k b It is possible to fit so that both are positive values, but the weight k a and weight k b It is not essential to perform the fitting so that both are positive values.

[0052] Since the R wave corresponds to the sequential onset of excitation of all myocardial cells according to a Gaussian distribution, as described above, the forward time waveform of the R wave can be approximated by an approximate time waveform that is the difference or weighted difference of two cumulative Gaussian distributions. On the other hand, since the T wave corresponds to the sequential wake-up of all myocardial cells according to a Gaussian distribution, the T wave can be interpreted as a phenomenon occurring in the opposite direction to the R wave on the time axis. In other words, the waveform obtained by reversing the time axis of the target time waveform of the T wave can be approximated by the difference or weighted difference of the cumulative Gaussian distributions. Hereinafter, this will be referred to as the first method. Furthermore, since the T wave corresponds to the wake-up of all myocardial cells from an excited state according to a Gaussian distribution, it can also be said that the forward time waveform of the T wave can be approximated by the difference or weighted difference of two functions (a function obtained by subtracting the cumulative Gaussian distribution from 1). Hereinafter, this will be referred to as the second method. Specific examples of the first and second methods will be described below. In order to avoid confusion between the cumulative distribution function for the R wave described above and the cumulative distribution function for the T wave described below, the first cumulative distribution function described above will be referred to as the third cumulative distribution function, and the second cumulative distribution function described above will be referred to as the fourth cumulative distribution function for the T wave.

[0053] When the second target time waveform (i.e., T wave) is approximated by the difference between the third cumulative distribution function and the fourth cumulative distribution function using the first method, the information representing the time in the reverse direction is defined as x', and the waveform obtained by reversing the time axis of the second target time waveform is called the second target inverted time waveform. Then, the third cumulative distribution function f e (x') to the fourth cumulative distribution function fg The second target inverse time waveform is approximated by the approximate inverse time waveform of Equation (13), which is a function obtained by subtracting (x'), to obtain the mean μ e and standard deviation σ e and the mean μ, which is a parameter that specifies the fourth cumulative distribution function. g and standard deviation σ g and are acquired as parameters representing the characteristics of the second target time waveform (i.e., T wave).

[0054]

number

[0055]

number

[0056]

number

[0057] For example, when the second target time waveform (i.e., T wave) is approximated by the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function using the first method, the third cumulative distribution function f e (x') has weight k e The fourth cumulative distribution function f g (x') has weight k g The second target inverse time waveform is approximated by the approximate inverse time waveform of Equation (14), which is a function obtained by subtracting the function multiplied by e and standard deviation σ e and the mean μ, which is a parameter that specifies the fourth cumulative distribution function. g and standard deviation σ g and are at least obtained as parameters representing the characteristics of the second target time waveform (i.e., T wave). Note that the weight k of the third cumulative distribution function e and the weight k of the fourth cumulative distribution function g , or the weight k of the third cumulative distribution function eand the weight k of the fourth cumulative distribution function g The ratio (k e / k g , or k g / k e ), may also be acquired as a parameter representing the characteristics of the second target time waveform (that is, the T wave).

[0058]

number

[0059] The function of equation (14) is weighted by the third cumulative distribution function k e The weight k is added to the fourth cumulative distribution function from the function multiplied by g Since it is a function obtained by subtracting a function multiplied by e and weight k g Both are positive values. However, if the target heart is in a specific state, the weight k obtained by fitting e and weight k g Therefore, the signal analyzing device 1 determines whether at least one of the weights k e and weight k g It is possible to fit so that both are positive values, but the weight k e and weight k g It is not essential to perform the fitting so that both are positive values.

[0060] For example, the second target time waveform (i.e., T wave) is converted into a third cumulative distribution function f expressed by Equation (15) from 1 using the second method. c Function f' with (x) subtracted c (x) (hereinafter referred to as the "third inverse cumulative distribution function") and the fourth cumulative distribution function f expressed by 1 to Equation (16). d Function f' with (x) subtracted d (x) (hereinafter referred to as the "fourth inverse cumulative distribution function"), the third inverse cumulative distribution function f' c (x) to the fourth inverse cumulative distribution function f' dThe second target time waveform is approximated by the approximate time waveform of Equation (17), which is a function obtained by subtracting (x), and the mean μ c and standard deviation σ c and the mean μ, which is a parameter that specifies the fourth cumulative distribution function. d and standard deviation σ d and are acquired as parameters representing the characteristics of the second target time waveform (i.e., T wave).

[0061]

number

[0062]

number

[0063]

number

[0064] For example, when the second target time waveform (i.e., T wave) is approximated by the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function using the second method, the third inverse cumulative distribution function f' c (x) has weight k c The fourth inverse cumulative distribution function f' is obtained from the function multiplied by d (x) has weight k d The second target time waveform is approximated by the approximate time waveform of Equation (18), which is a function obtained by subtracting a function multiplied by c and standard deviation σ c and the mean μ, which is a parameter that specifies the fourth cumulative distribution function. d and standard deviation σ d and are obtained as parameters representing the characteristics of the second target time waveform (i.e., T wave). Note that 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 weight k of the third inverse cumulative distribution function c and the weight k of the fourth inverse cumulative distribution function d The ratio (kc / k d , or k d / k c ), may also be acquired as a parameter representing the characteristics of the second target time waveform (that is, the T wave).

[0065]

number

[0066] The function of equation (18) is the third inverse cumulative distribution function with weight k c The weight k is applied to the fourth inverse cumulative distribution function from the function multiplied by d Since it is a function obtained by subtracting a function multiplied by c and weight k d Both are positive values. However, if the target heart is in a specific state, the weight k obtained by fitting c and weight k d Therefore, the signal analyzing device 1 determines whether at least one of the weights k c and weight k d It is possible to fit so that both are positive values, but the weight k c and weight k d It is not essential to perform the fitting so that both are positive values.

[0067] The approximate time waveform of equation (17) is the fourth cumulative distribution function f d (x) to the third cumulative distribution function f c Since it is a function obtained by subtracting (x), the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d (x). The approximate time waveform of equation (18) is the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d The weighted difference of (x) plus a constant term is given by the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d(x) is the same as the weighted difference of (x). As mentioned 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 waveform is calculated as the third cumulative distribution function f e (x) and the fourth cumulative distribution function f g The inverse cumulative distribution function (ICDF) is used to calculate the T wave amplitude.

[0068] FIG. 2 shows the first cumulative distribution function f a (x) and weight k a Function k multiplied by a f a (x) and the second cumulative distribution function f b (x) and weight k b Function k multiplied by b f b (x) and the approximate time waveform k, which is the weighted difference between the first and second cumulative distribution functions. a f a (x)-k b f b (x) and the dashed line represents the first cumulative distribution function f a (x) and weight k a Function k multiplied by a f a (x), and the dashed line represents the second cumulative distribution function f b (x) and weight k b Function k multiplied by b f b (x), and the dashed 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 a waveform that approximates the first target time waveform (that is, the R wave).

[0069] FIG. 3 shows the third inverse cumulative distribution function f' for the second time waveform of interest (i.e., the T wave).c (x)=1-f c (x) and weight k c Function k multiplied by c f' c (x), the fourth inverse cumulative distribution function f' d (x)=1-f d (x) and weight k d Function k multiplied by d f' d (x), the approximate time waveform k, which is the weighted difference between the third and fourth inverse cumulative distribution functions c f' c (x)-k d f' d (x) and (x). The dashed line represents the third inverse cumulative distribution function f'. c (x)=1-f c (x) and weight k c Function k multiplied by c f' c (x), and the dashed-dotted line is the fourth inverse cumulative distribution function f' d (x)=1-f d (x) and weight k d Function k multiplied by d f' d (x), and 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 target time waveform (ie, the T wave).

[0070] 4 is a diagram showing a result of fitting the first and second target time waveforms by the difference between two cumulative distribution functions, where the R wave and the T wave included in one cycle of the electrocardiogram waveform of the target heart in the first embodiment are defined as the first and second target time waveforms, respectively. The horizontal axis of FIG. 4 represents time, and the vertical axis represents potential. Both the horizontal and vertical axes are expressed in arbitrary units.

[0071] 4 specifically shows an example in which a first target time waveform (i.e., R wave) is fitted using the difference between the first and second cumulative distribution functions, and a second target time waveform (i.e., T wave) is fitted using the difference between the third and fourth cumulative distribution functions. The domains of the first and second cumulative distribution functions are the same, covering the time interval of the first target time waveform (i.e., R wave), from time T1 to time T3. The domains of the third and fourth cumulative distribution functions are the same, covering the time interval of the first target time waveform (i.e., R wave), from time T4 to time T6.

[0072] The "first fitting result" and "second fitting result" in Fig. 4 are the results of fitting to the R wave. The "third fitting result" and "fourth fitting result" in Fig. 4 are the results of fitting to the T wave.

[0073] In FIG. 4, "first fitting result" indicates a first cumulative distribution function among the results of fitting to a first target time waveform (i.e., R wave) of an electrocardiogram. In FIG. 4, "second fitting result" indicates a second cumulative distribution function among the results of fitting to a first target time waveform (i.e., R wave) of an electrocardiogram. In FIG. 4, "third fitting result" indicates a third cumulative distribution function among the results of fitting to a second target time waveform (i.e., T wave) of an electrocardiogram. In FIG. 4, "fourth fitting result" indicates a fourth cumulative distribution function among the results of fitting to a second target time waveform (i.e., T wave) of an electrocardiogram. In FIG. 4, "body surface potential" indicates the electrocardiogram waveform being fitted.

[0074] The signal analyzing device 1 does not perform fitting during the period from time T3 to time T4, which does not belong to either the time interval of the first target time waveform (i.e., R wave) or the time interval of the second target time waveform (i.e., T wave). The periods during which fitting is not performed by the signal analyzing device 1 are represented in Fig. 4 by a line connecting the first fitting result at time T3 and the third fitting result at time T4, and a line connecting the second fitting result at time T3 and the fourth fitting result at time T4. That is, when the signal analyzing device 1 displays the fitting results, the signal analyzing device 1 may display lines connecting the first fitting result at time T3 and the third fitting result at time T4, and the second fitting result at time T3 and the fourth fitting result at time T4, using predetermined functions such as a constant function or a linear function, as shown in Fig. 4.

[0075] When the signal analyzing device 1 displays the fitting results, the weighting value may be corrected so that the first fitting result at time T3 and the third fitting result at time T4 can be displayed with the same value. That is, the actual first fitting result at time T3 is k a f a (T3), and the actual third fitting result at time T4 is k c f' c (T4), but k a f a (T3)=α1k c f' c Find α1 that satisfies (T4) and use weight k c Instead of α1k c The fitting results may be displayed using the weight k a Instead of k a / α1 may be used to display the fitting results. Similarly, when the signal analyzing device 1 displays the fitting results, the weighting values ​​may be corrected so that the second fitting result at time T3 and the fourth fitting result at time T4 can be displayed with the same value. That is, the second fitting result at time T3 is k bf b (T3), and the fourth fitting result at time T4 is k d f' d (T4), but k b f b (T3)=α2k d f' d Find α2 that satisfies (T4) and use weight k d Instead of α2k d The fitting results may be displayed using the weight k b Instead of k b / α2 may be used to display the fitting results.

[0076] Note that fitting to the first target time waveform and fitting to the second target time waveform do not have to be performed separately. That is, fitting to the first target time waveform and fitting to the second target time waveform may be performed together. For example, when fitting to the first target time waveform and fitting to the second target time waveform are performed together, the signal analyzing device 1 may perform fitting taking into consideration both 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.

[0077] FIG. 5 is an explanatory diagram illustrating that typical features of a T wave can be visualized by approximating the T wave with a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function, and then displaying the third and fourth inverse cumulative distribution functions and parameters specifying each cumulative distribution function. FIG. 5 shows four images, image G1, image G2, image G3, and image G4. Each of images G1 to G4 shows a graph with the horizontal axis representing time and the vertical axis representing potential. The horizontal and vertical axes of each of images G1 to G4 in FIG. 5 are all in arbitrary units.

[0078] The "first function" in FIG. 5 is an example of the third inverse cumulative distribution function. The "second function" in FIG. 5 is an example of the fourth inverse cumulative distribution function. The "third function" in FIG. 5 represents a function obtained by subtracting the "second function" from the "first function," i.e., a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. The "third function" in FIG. 5 has a shape that is substantially identical to the shape of a T wave in a normal heart.

[0079] The "fourth function" in FIG. 5 is an example of the third inverse cumulative distribution function. The "fifth function" in FIG. 5 is an example of the fourth inverse cumulative distribution function. The "sixth function" in FIG. 5 represents a function obtained by subtracting the "fifth function" from the "fourth function," i.e., a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. The "sixth function" in FIG. 5 has a shape that is substantially identical to the shape of a T wave depression in one of three typical abnormal T wave patterns. The interval between the falling edge of the third inverse cumulative distribution function and the falling edge of the fourth inverse cumulative distribution function in image G2 of FIG. 5 is narrower than the interval between the falling edge of the third inverse cumulative distribution function and the falling edge of the fourth inverse cumulative distribution function in image G1 of a normal heart. This visualizes the shorter delay between the activity of the epimyocardium and the activity of the endomyocardium in the T wave depression.

[0080] The "seventh function" in FIG. 5 is an example of the third inverse cumulative distribution function. The "eighth function" in FIG. 5 is an example of the fourth inverse cumulative distribution function. The "ninth function" in FIG. 5 represents a function obtained by subtracting the "eighth function" from the "seventh function," i.e., a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. The "ninth function" in FIG. 5 has a shape that is substantially identical to the shape of one of three typical abnormal T wave patterns of T wave enhancement. The interval between the falling edge of the third inverse cumulative distribution function and the falling edge of the fourth inverse cumulative distribution function in image G3 of FIG. 5 is wider than the interval between the falling edge of the third inverse cumulative distribution function and the falling edge of the fourth inverse cumulative distribution function in image G1 of a normal heart. This visualizes the large delay between the activity of the epimyocardium and the activity of the endomyocardium in the T wave enhancement.

[0081] The "tenth function" in FIG. 5 is an example of the third inverse cumulative distribution function. The "eleventh function" in FIG. 5 is an example of the fourth inverse cumulative distribution function. The "twelfth function" in FIG. 5 represents a function obtained by subtracting the "eleventh function" from the "tenth function," i.e., a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. The "twelfth function" in FIG. 5 has a shape that is substantially identical to the shape of a negative T wave, one of three typical abnormal T wave patterns. The order of the falling edges of the third inverse cumulative distribution function and the fourth inverse cumulative distribution function in image G4 of FIG. 5 is reversed from the order of the falling edges of the third inverse cumulative distribution function and the fourth inverse cumulative distribution function in image G1 of a normal heart. This makes it possible to visualize that, in a negative T wave, activity in the epimyocardium ends earlier than in the endomyocardium.

[0082] A function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can express waves with different widths along the vertical and horizontal axes, such as the "third function," "sixth function," and "ninth function." Furthermore, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can express negative waves, such as the "twelfth function." That is, by approximating a T wave with a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function, or by approximating a T wave with a function obtained by reversing the time axis and subtracting the fourth cumulative distribution function from the third cumulative distribution function, it is possible to express the activity of the endomyocardium and epimyocardium contained in the T wave, as well as the relationship between the activity of the endomyocardium and epimyocardium. The same is true when approximating an R wave with a function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0083] In this way, the signal analyzing device 1 fits the waveform of the R wave or T wave of the electrocardiogram of the target heart using the difference or weighted difference of the two cumulative distribution functions, and then acquires parameters that specify the approximate time waveform determined by the fitting as parameters that indicate myocardial activity.

[0084] [Approximation by adding a value to the difference or weighted difference of two cumulative distribution functions] The signal analysis device 1 may use a waveform of either an R wave or a T wave included in one cycle of the acquired electrocardiogram of the target heart as the target time waveform, and may use a time waveform obtained by adding a value (hereinafter referred to as a "level value") to the difference or weighted difference between a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, and a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, as the approximate time waveform. In this case, when the target time waveform is approximated with the approximate time waveform, in addition to a parameter specifying the first unimodal distribution or a parameter specifying the first cumulative distribution function, and a parameter specifying the second unimodal distribution or a parameter specifying the second cumulative distribution function, the level value is also acquired as a parameter representing the characteristics of the target time waveform. Of course, when approximating with a weighted difference, the weights assigned to the first cumulative distribution function and the second cumulative distribution function may also be acquired as parameters representing the characteristics of the target time waveform, or the ratio of the weights assigned to the first cumulative distribution function and the second cumulative distribution function may also be acquired as a parameter representing the characteristics of the target time waveform.

[0085] In the case of using a weighted difference, for example, the signal analyzing device 1 calculates the first cumulative distribution function and the second cumulative distribution function using each of the combinations (J×K×L×M×N ways) of a parameter specifying a cumulative distribution function for each of a plurality (M) of candidates for the first cumulative distribution function, a parameter specifying a cumulative distribution function for each of a plurality (N) of candidates for the second cumulative distribution function, a plurality (K) of candidates for weights to be assigned to the first cumulative distribution function, a plurality (L) of candidates for weights to be assigned to the second cumulative distribution function, and a plurality (J) of candidates for level values. A candidate time waveform is generated, which is a time waveform obtained by adding a level value to the weighted difference with the second cumulative distribution function, and the candidate time waveform that is closest to the target time waveform among the generated J×K×L×M×N candidate time waveforms is identified as an approximate time waveform, and parameters that identify the candidate first cumulative distribution function used to generate the identified approximate time waveform, parameters that identify the candidate second cumulative distribution function, weights to be assigned to the first cumulative distribution function, weights to be assigned to the second cumulative distribution function, and the level value are obtained as parameters representing the characteristics of the target time waveform.

[0086] Alternatively, for example, the signal analysis device 1 obtains a candidate time waveform, which is a time waveform obtained by adding a level value to the weighted difference between a candidate first cumulative distribution function and a candidate second cumulative distribution function that approximate the target time waveform, and updates at least one of the parameters specifying each cumulative distribution function and the weight and level value assigned to each cumulative distribution function in a direction that reduces the squared error between the candidate time waveform and the target time waveform, until the squared error becomes equal to or less than a predetermined standard, or repeats this a predetermined number of times, identifies the finally obtained candidate time waveform as an approximated time waveform, and acquires the parameters specifying the candidate first cumulative distribution function and the parameters specifying the candidate second cumulative distribution function, the weight assigned to the first cumulative distribution function, the weight assigned to the second cumulative distribution function, and the level value used to generate the identified approximated time waveform as parameters representing the characteristics of the target time waveform.

[0087] The level value may be determined before fitting. In this case, the signal analyzing device 1 first acquires the potential at the start of the target time waveform (corresponding to time T1 in FIG. 4) as the level value when the target time waveform is an R wave, and acquires the potential at the end of the target time waveform (corresponding to time T6 in FIG. 4) as the level value when the target time waveform is a T wave. Then, the signal analyzing device 1 generates candidate time waveforms, which are time waveforms obtained by adding a level value to the weighted difference between the first and second cumulative distribution functions, using each of the combinations (K×L×M×N ways) of parameters specifying the cumulative distribution function for each of the multiple (M) candidates for the first cumulative distribution function, parameters specifying the cumulative distribution function for each of the multiple (N) candidates for the second cumulative distribution function, multiple (K) candidates for weights to be assigned to the first cumulative distribution function, and multiple (L) candidates for weights to be assigned to the second cumulative distribution function, and identifies the candidate time waveform that is closest to the target time waveform from the generated K×L×M×N ways of candidate time waveforms as an approximate time waveform, and acquires the parameters specifying the candidate first cumulative distribution function, the parameters specifying the candidate second cumulative distribution function, the weights to be assigned to the first cumulative distribution function, the weights to be assigned to the second cumulative distribution function, and the level value determined in the initial processing as parameters representing the characteristics of the target time waveform.

[0088] Alternatively, for example, the signal analysis device 1 first acquires the potential at the beginning of the target time waveform (corresponding to time T1 in Figure 4) as the level value when the target time waveform is an R wave, and acquires the potential at the end of the target time waveform (corresponding to time T6 in Figure 4) as the level value when the target time waveform is a T wave. The signal analyzing device 1 then obtains a candidate time waveform, which is a time waveform obtained by adding a level value to the weighted difference between a candidate first cumulative distribution function and a candidate second cumulative distribution function that approximate the target time waveform, and updates at least one of the parameters specifying each cumulative distribution function and the weight assigned to each cumulative distribution function in a direction that reduces the squared error between the candidate time waveform and the target time waveform, until the squared error becomes equal to or less than a predetermined standard, or repeats this a predetermined number of times, identifies the finally obtained candidate time waveform as an approximate time waveform, and acquires the parameters specifying the candidate first cumulative distribution function and the parameters specifying the candidate second cumulative distribution function, the weight assigned to the first cumulative distribution function, and the weight assigned to the second cumulative distribution function that were used to generate the identified approximate time waveform, as parameters representing the characteristics of the target time waveform.

[0089] If the level value is β, the weighted difference between the first and second cumulative distribution functions plus the level value is expressed, for example, by the following equation (19): The function expressed by the following equation (19) is a function obtained by subtracting a function obtained by multiplying the second cumulative distribution function by a weight k2 from a function obtained by multiplying the first cumulative distribution function by a weight k1, and adding the level value β to the result.

[0090]

number

[0091] When a target time waveform is approximated by an approximated time waveform of Equation (19) in which a level value is added to the weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least the following parameters are acquired as characteristics of the target time waveform: a mean μ1 and a standard deviation σ1, which are parameters specifying the first unimodal distribution or the first cumulative distribution function; a mean μ2 and a standard deviation σ2, which are parameters specifying the second unimodal distribution or the second cumulative distribution function; and a level value β. Note that the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function, or the ratio (k1 / k2 or k2 / k1) of the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function may also be acquired as a parameter indicating the characteristics of the target time waveform.

[0092] Since the function of equation (19) is a function obtained by subtracting a function obtained by multiplying the second cumulative distribution function by a weight k2 from a function obtained by multiplying the first cumulative distribution function by a weight k1 and adding a level value β, both weights k1 and k2 are positive values. However, if the target heart is in an unusual 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, the signal analyzing device 1 may perform fitting so that both weights k1 and k2 are positive values, but it is not essential to perform fitting so that both weights k1 and k2 are positive values.

[0093] The signal analysis device 1 may acquire parameters representing the characteristics of the above-mentioned target time waveforms for each of the first target time waveform and the second target time waveform, out of the R waves and T waves contained in one cycle of the acquired waveform of the electrocardiogram of the target heart.

[0094] For example, when approximating the first target time waveform (i.e., R wave) with a function obtained by adding a level value to the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the potential at the beginning of the first target time waveform is set to a level value β R The first cumulative distribution function f a (x) has weight k aThe second cumulative distribution function f expressed by equation (8) is obtained from the function multiplied by b (x) has weight k b The level value β is obtained by subtracting the function multiplied by R The first target time waveform is approximated by the approximate time waveform of Equation (20), which is a function obtained by adding the mean μ a and standard deviation σ a and the mean μ, which is a parameter specifying the second cumulative distribution function. b and standard deviation σ b and the level value β R and are at least obtained as parameters representing the characteristics of the first target time waveform (i.e., R wave). Note that the weight k a and the weight k of the second cumulative distribution function b , or the weight k of the first cumulative distribution function a and the weight k of the second cumulative distribution function b The ratio (k a / k b , or k b / k a ) may also be acquired as a parameter representing the characteristic of the parameter representing the characteristic of the first target time waveform (that is, the R wave).

[0095]

number

[0096] The function of equation (20) is weighted by the first cumulative distribution function k a The weight k is added to the second cumulative distribution function from the function multiplied by b The level value β is obtained by subtracting the function multiplied by R Since it is a function of adding weight k a and weight k b Both are positive values. However, if the target heart is in a specific state, the weight k obtained by fitting a and weight k b Therefore, the signal analyzing device 1 determines whether at least one of the weights k a and weight k b It is possible to fit so that both are positive values, but the weight ka and weight k b It is not essential to perform the fitting so that both are positive values.

[0097] For example, when approximating a second target inverse time waveform, which is a waveform obtained by reversing the time axis of the second target time waveform (i.e., T wave), with a function obtained by adding a level value to the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, the potential at the end of the second target time waveform is approximated by a level value β T The third cumulative distribution function f e (x') has weight k e The fourth inverse cumulative distribution function f g (x') has weight k g The level value β is obtained by subtracting the function multiplied by T The second target inverse time waveform is approximated by the approximate inverse time waveform of Equation (21), which is a function obtained by adding the mean μ e and standard deviation σ e and the mean μ, which is a parameter that specifies the fourth cumulative distribution function. g and standard deviation σ g and the level value β T and are at least obtained as parameters representing the characteristics of the second target time waveform (i.e., T wave). Note that the weight k of the third cumulative distribution function e and the weight k of the fourth cumulative distribution function g , or the weight k of the third cumulative distribution function e and the weight k of the fourth cumulative distribution function g The ratio (k e / k g , or k g / k e ), may also be acquired as a parameter representing the characteristic of the parameter representing the characteristic of the second target time waveform (that is, T wave).

[0098]

number

[0099] The function of equation (21) is weighted to the third cumulative distribution function by k eThe weight k is added to the fourth cumulative distribution function from the function multiplied by g The level value β is obtained by subtracting the function multiplied by T Since it is a function of adding weight k e and weight k g Both are positive values. However, if the target heart is in a specific state, the weight k obtained by fitting e and weight k g Therefore, the signal analyzing device 1 determines whether at least one of the weights k e and weight k g It is possible to fit so that both are positive values, but the weight k e and weight k g It is not essential to perform the fitting so that both are positive values.

[0100] For example, when the second target time waveform (i.e., T wave) is approximated by a function obtained by adding a level value to the weighted difference between the function obtained by subtracting the third cumulative distribution function from 1 (i.e., the third inverse cumulative distribution function) and the function obtained by subtracting the fourth cumulative distribution function from 1 (i.e., the fourth inverse cumulative distribution function), the potential at the end of the second target time waveform is approximated by the level value β T The third inverse cumulative distribution function f' is c (x) has weight k c The fourth inverse cumulative distribution function f' is obtained from the function multiplied by d (x) has weight k d The level value β is obtained by subtracting the function multiplied by T The second target time waveform is approximated by the approximate time waveform of Equation (22), which is a function obtained by adding the mean μ c and standard deviation σ c and the mean μ, which is a parameter that specifies the fourth cumulative distribution function. d and standard deviation σ d and the level value β T and are obtained as parameters representing the characteristics of the second target time waveform (i.e., T wave). Note that 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 weight k of the third inverse cumulative distribution function c and the weight k of the fourth inverse cumulative distribution functiond The ratio (k c / k d , or k d / k c ), may also be acquired as a parameter representing the characteristics of the second target time waveform (that is, the T wave).

[0101]

number

[0102] The function of equation (22) is the third inverse cumulative distribution function with weight k c The weight k is applied to the fourth inverse cumulative distribution function from the function multiplied by d The level value β is obtained by subtracting the function multiplied by T Since it is a function of adding weight k c and weight k d Both are positive values. However, if the target heart is in a specific state, the weight k obtained by fitting c and weight k d Therefore, the signal analyzing device 1 determines whether at least one of the weights k c and weight k d It is possible to fit so that both are positive values, but the weight k c and weight k d It is not essential to perform the fitting so that both are positive values.

[0103] FIG. 6 shows the first cumulative distribution function f a (x) and weight k a Multiply by the level value β R The function k added a f a (x)+β R and the second cumulative distribution function f b (x) and weight k b Multiply by the level value β R The function k added b f b (x)+β R The weighted difference between the first and second cumulative distribution functions is given by the level value β RApproximate time waveform k a f a (x)-k b f b (x)+β R The dashed line indicates the first cumulative distribution function f a (x) and weight k a Multiply by the level value β R The function k added a f a (x)+β R and the dashed-dotted line represents the second cumulative distribution function f b (x) and weight k b Multiply by the level value β R The function k added 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 This approximate time waveform k a f a (x)-k b f b (x)+β R is a waveform that approximates the first target time waveform (that is, the R wave).

[0104] FIG. 7 shows the third inverse cumulative distribution function f' for the second target time waveform (i.e., the T wave). c (x)=1-f c (x) and weight k c Multiply by the level value β T The function k added c f' c (x)+β T , the fourth inverse cumulative distribution function f' d (x)=1-f d (x) and weight k d Multiply by the level value β T The function k added d f' d (x)+β T , the weighted difference between the third and fourth inverse cumulative distribution functions is given a level value β T Approximate time waveform k c f'c (x)-k d f' d (x)+β T The dashed dotted line represents the third inverse cumulative distribution function f'. c (x)=1-f c (x) and weight k c Multiply by the level value β T The function k added c f' c (x)+β T and the dashed-dotted line represents the fourth inverse cumulative distribution function f' d (x)=1-f d (x) and weight k d Multiply by the level value β T The function k added d f' d (x)+β T The dashed line is the approximate time waveform k c f' c (x)-k d f' d (x)+β T This approximate time waveform k c f' c (x)-k d f' d (x)+β T is a waveform that approximates the second target time waveform (i.e., T wave).

[0105] In addition, the level value β, which is the potential at the beginning of the R wave (more precisely, the QRS wave), R is a value that represents the magnitude of the DC component at the beginning of the R wave, and if there is an abnormality in the coronary artery, the level value β R The level value β, which is the potential at the end of the T wave, may also decrease to the negative side. T is a value that represents the magnitude of the DC component at the end of the T wave, and if there is an abnormality in the myocardial repolarization, the level value β T may rise to the positive side.

[0106] [Approximation of the difference between the target time waveform and the approximate time waveform] When an R wave or a T wave in a special state is used as the target time waveform, the above-described approximate time waveform may leave a portion (hereinafter referred to as a "residual portion") that cannot be approximated by the target time waveform. For example, when early repolarization or a conduction disorder (such as an accessory pathway) occurs in the target heart, a Δ wave as shown by the dashed line in FIG. 8 may be included in the target time waveform (R wave). This Δ wave portion cannot be approximated by the above-described approximate time waveform, and remains as a residual portion. Since this residual portion is also a time waveform resulting from some activity of the heart, the signal analysis device 1 may perform analysis on this residual portion assuming that it is a cumulative Gaussian distribution, or a function obtained by multiplying a cumulative Gaussian distribution by a weight, or a difference between cumulative Gaussian distributions, or a weighted difference between cumulative Gaussian distributions.

[0107] That is, the signal analysis device 1 may acquire, as parameters representing the characteristics of the target time waveform, parameters specifying a fifth unimodal distribution or a fifth cumulative distribution function when the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, is approximated by a cumulative distribution function (conveniently referred to as a "fifth unimodal distribution") of a certain unimodal distribution (conveniently referred to as a "fifth cumulative distribution function") or a function obtained by multiplying the fifth cumulative distribution function by a weight.

[0108] Alternatively, the signal analysis device 1 may acquire, as parameters representing the characteristics of the target time waveform, parameters specifying the fifth unimodal distribution or the fifth cumulative distribution function, and parameters specifying the sixth unimodal distribution or the sixth cumulative distribution function, when the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, is approximated by a time waveform (hereinafter referred to as an "approximated residual time waveform") that is 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 other than the fifth unimodal distribution (conveniently referred to as the "sixth unimodal distribution").

[0109] More specifically, when approximating the residual time waveform with the fifth cumulative distribution function, which is the cumulative distribution function of the fifth unimodal distribution expressed by equation (23), the signal analyzing device 1 approximates the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, with the approximated time waveform f5(x) of the fifth cumulative distribution function expressed by equation (24), and acquires, in addition to the parameters expressing the characteristics of the target time waveform described above, the mean μ5 and the standard deviation σ5, which are parameters specifying the fifth cumulative distribution function, as parameters expressing the characteristics of the target time waveform.

[0110]

number

[0111]

number

[0112] When approximating the residual time waveform with a function obtained by multiplying the fifth cumulative distribution function by a weight, the signal analyzing device 1 may approximate the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, with an approximated time waveform k5f5(x) obtained by multiplying the fifth cumulative distribution function f5(x) expressed in equation (24) by a weight k5, and may acquire, in addition to the parameters representing the characteristics of the target time waveform described above, the mean μ5 and the standard deviation σ5, which are parameters specifying the fifth cumulative distribution function, as parameters representing the characteristics of the target time waveform. The signal analyzing device 1 may also acquire the weight k5 as a parameter representing the characteristics of the target time waveform.

[0113] When approximating the residual time waveform by the difference between the fifth cumulative distribution function and the sixth cumulative distribution function, which is the cumulative distribution function of the sixth unimodal distribution expressed by equation (25), for example, the signal analyzing device 1 approximates the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, by the approximated time waveform f5(x)-f6(x), which is the waveform obtained by subtracting the sixth cumulative distribution function expressed by equation (26) from the fifth cumulative distribution function expressed by equation (24), and acquires, in addition to the parameters expressing the characteristics of the target time waveform described above, the mean μ5 and standard deviation σ5, which are parameters specifying the fifth cumulative distribution function, and the mean μ6 and standard deviation σ6, which are parameters specifying the sixth cumulative distribution function, as parameters expressing the characteristics of the target time waveform.

[0114]

number

[0115]

number

[0116] When approximating the residual time waveform using the weighted difference between the fifth and sixth cumulative distribution functions, the signal analyzing device 1 approximates the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, using an approximated time waveform k5f5(x)-k6f6(x), which is a waveform obtained by subtracting a function k6f6(x), which is obtained by multiplying the sixth cumulative distribution function f6(x) by a weight k6, from a function k5f5(x), which is obtained by multiplying the fifth cumulative distribution function f5(x) by a weight k5. In addition to the above-described parameters representing the characteristics of the target time waveform, the signal analyzing device 1 may also acquire the mean μ5 and standard deviation σ5, which are parameters specifying the fifth cumulative distribution function, and the mean μ6 and standard deviation σ6, which are parameters specifying the sixth cumulative distribution function, as parameters representing the characteristics of the target time waveform. The signal analyzing device 1 may also acquire the weights k5 and k6, or the ratio of the weights k5 and k6 (k5 / k6 or k6 / k5), as parameters representing the characteristics of the target time waveform.

[0117] Since the residual time waveform is approximated by an approximated time waveform obtained by multiplying the sixth cumulative distribution function by a weight k6 from a function obtained by multiplying the fifth cumulative distribution function by a weight k5, both weights k5 and k6 are positive values. However, if the target heart is in an unusual state, it cannot be denied that at least one of weights k5 and k6 obtained by fitting may not be a positive value. Therefore, the signal analyzing device 1 may perform fitting so that both weights k5 and k6 are positive values, but it is not essential to perform fitting so that both weights k5 and k6 are positive values.

[0118] Returning to the explanation of Fig. 1, the signal analyzing device 1 includes a control unit 11 having a processor 91 such as a CPU and a memory 92 connected by a bus, and executes a program. By executing the program, the signal analyzing 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.

[0119] More specifically, the processor 91 reads out a program stored in the storage unit 14 and stores the read out program in the memory 92. When the processor 91 executes the program stored in the memory 92, the signal analyzing device 1 functions as a device including a control unit 11, an input unit 12, a communication unit 13, a storage unit 14, and an output unit 15.

[0120] The control unit 11 controls the operation of various functional units included in the signal analyzing device 1. The control unit 11 executes, for example, a myocardial activity information parameter acquisition process. The control unit 11 controls, for example, the operation of the output unit 15, and causes the output unit 15 to output the acquisition results of the myocardial activity information parameter acquisition process. The control unit 11 records, for example, various pieces of information generated by the execution of the myocardial activity information parameter acquisition process in the storage unit 14.

[0121] The input unit 12 includes input devices such as a mouse, a keyboard, and a touch panel. The input unit 12 may be configured as an interface that connects these input devices to the signal analyzing device 1. The input unit 12 accepts input of various types of information to the signal analyzing device 1.

[0122] To the input unit 12, for example, information indicating the shape of the distribution represented by each cumulative distribution function (hereinafter referred to as "distribution shape designation information") is input for a plurality of candidates for each cumulative distribution function used in fitting.

[0123] The distribution shape designation information may be stored in advance in the storage unit 14. In such a case, the distribution shape designation information already stored in the storage unit 14 does not need to be input from the input unit 12. For ease of explanation, the signal analyzing device 1 will be described below taking as an example a case where the distribution shape designation information has been stored in advance in the storage unit 14.

[0124] The communication unit 13 includes a communication interface for connecting the signal analysis device 1 to an external device. The communication unit 13 communicates with the external device via wired or wireless communication. The external device is, for example, a device that transmits an electrocardiogram waveform of the target heart. The device that transmits the electrocardiogram waveform of the target heart is, for example, an electrocardiogram measurement device. For example, when the external device is an electrocardiogram measurement device, the communication unit 13 acquires the electrocardiogram waveform from the electrocardiogram measurement device through communication. The electrocardiogram waveform may be input to the input unit 12.

[0125] The storage unit 14 is configured using a non-transitory computer-readable storage medium device such as a magnetic hard disk drive 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, for example, the input unit 12 or the communication unit 13. The storage unit 14 stores, for example, an electrocardiogram input via the input unit 12 or the communication unit 13. The storage unit 14 stores, for example, various information generated by executing a myocardial activity information parameter acquisition process.

[0126] The output unit 15 outputs various types of information. The output unit 15 includes a display device such as a CRT (Cathode Ray Tube) display, a liquid crystal display, or an organic EL (Electro-Luminescence) display. The output unit 15 may be configured as an interface that connects these display devices to the signal analysis device 1. The output unit 15 outputs information input to the input unit 12, for example. The output unit 15 may display an electrocardiogram input to the input unit 12 or the communication unit 13, for example. The output unit 15 may display the execution result of the myocardial activity information parameter acquisition process, for example.

[0127] 9 is a diagram showing an example of the functional configuration of the control unit 11 in the first embodiment. The control unit 11 includes an electrocardiogram acquisition unit 110, a fitting information acquisition unit 120, an analysis unit 130, and a recording unit 140.

[0128] The electrocardiogram acquisition unit 110 acquires one cycle of the waveform from the electrocardiogram waveform of the target heart input to the input unit 12 or the communication unit 13 and outputs the waveform to the analysis unit 130. The electrocardiogram waveform of the target heart is a waveform in which waveforms of multiple beats (multiple cycles) are arranged in time series. Even when an abnormality occurs in the target heart, it is often the case that not all the waveforms of the beats included in the electrocardiogram waveform are special waveforms, but only a small number of waveforms included in the electrocardiogram waveform are characteristic waveforms. In the myocardial activity information parameter acquisition process, it is preferable to target this characteristic waveform. Therefore, the electrocardiogram acquisition unit 110 acquires one cycle of the characteristic waveform from the electrocardiogram waveform of the target heart. For example, the electrocardiogram acquisition unit 110 may acquire one cycle of the characteristic waveform from the electrocardiogram waveform of the target heart using a known technique for determining similarity and uniqueness. For example, the electrocardiogram acquisition unit 110 may display an electrocardiogram waveform on the output unit 15, have the input unit 12 accept a specification of one cycle of the waveform from a user such as a doctor, and acquire one cycle of the waveform corresponding to the specification accepted by the input unit 12 from the electrocardiogram waveform.

[0129] The electrocardiogram acquisition unit 110 outputs one cycle of waveform as digital time series data sampled at a predetermined sampling frequency. The predetermined sampling frequency is the sampling frequency of the signal used in processing in the fitting information acquisition unit 120, and is, for example, 250 Hz. When the input electrocardiogram waveform is sampled at the predetermined sampling frequency, the electrocardiogram acquisition unit 110 may extract and output digital time series data of one cycle of waveform from the digital time series data of the input electrocardiogram waveform. When the input electrocardiogram waveform is sampled at a sampling frequency different from the predetermined sampling frequency, the electrocardiogram acquisition unit 110 may extract and output digital time series data of one cycle of waveform from the digital time series data of the input electrocardiogram waveform, convert it to the predetermined sampling frequency, and then output it.

[0130] The electrocardiogram acquisition unit 110 further identifies the time interval of the R wave and the time interval of the T wave included in one cycle of the waveform of the electrocardiogram, acquires information identifying the time interval of the R wave and information identifying the time interval of the T wave, and outputs the information to the analysis unit 130. For example, the electrocardiogram acquisition unit 110 may identify the beginning and end of the R wave, the beginning and end of the T wave, and acquire, as information identifying the time interval of the R wave and the time interval of the T wave, sample numbers corresponding to the identified beginning and end of the R wave, the beginning and end of the T wave, and relative times from the beginning of the waveform. Note that, in this specification, the R wave accurately refers to the QRS wave. Although there are various interpretations as to which point in the waveform the beginning of the R wave (i.e., the beginning of the QRS wave) is, the electrocardiogram acquisition unit 110 may determine the beginning of the R wave as identified by any known technology.

[0131] In addition, a characteristic waveform that changes over time may appear in the electrocardiogram waveform of the target heart. Therefore, the electrocardiogram acquiring unit 110 may acquire waveforms for multiple cycles from the electrocardiogram waveform of the target heart as waveforms to be subjected to the myocardial activity information parameter acquisition process. That is, the electrocardiogram acquiring unit 110 may acquire a predetermined long-term time-series waveform (trend graph) from the electrocardiogram waveform of the target heart, and output to the analyzing unit 130, for each waveform cycle included in the acquired waveform, information specifying the waveform, the time interval of the R wave included in the waveform, and information specifying the time interval of the T wave included in the waveform.

[0132] The fitting information acquisition unit 120 acquires the distribution shape designation information. If the distribution shape designation information is stored in the storage unit 14, the fitting information acquisition unit 120 reads the distribution shape designation information from the storage unit 14.

[0133] The analysis unit 130 includes a fitting unit 131 and a myocardial activity information parameter acquisition unit 132 .

[0134] The fitting unit 131 uses candidates for cumulative distribution functions indicated by the distribution shape designation information to perform fitting on a target time waveform, which is the waveform of at least one of the time intervals of the R wave and the T wave included in one cycle of the waveform of the electrocardiogram acquired by the electrocardiogram acquisition unit 110.

[0135] The myocardial activity information parameter acquiring unit 132 acquires parameters representing characteristics of the target time waveform based on the fitting result by the fitting unit 131. The myocardial activity information parameter acquiring unit 132 acquires, as parameters representing the characteristics of the target time waveform, parameters specifying the first unimodal distribution or the first cumulative distribution function and parameters specifying the second unimodal distribution or the second cumulative distribution function when the target time waveform is approximated by an approximated time waveform that is a time waveform based on the 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, for example. The parameters representing the characteristics of the target time waveform acquired by the myocardial activity information parameter acquiring unit 132 are examples of myocardial activity parameters.

[0136] In this way, the analysis unit 130 acquires myocardial activity parameters based on the target time waveform, which is the waveform of at least one time interval of an R wave or a T wave included in one cycle of the waveform of the electrocardiogram of the target heart, and the distribution candidate information.

[0137] The storage unit 14 records various information generated by the processing executed by the control unit 11 in the storage unit 14 .

[0138] 10 is a flowchart showing an example of the flow of processing executed by the signal analyzing device 1 in the first embodiment. The electrocardiogram acquiring unit 110 acquires, via the input unit 12 or the communication unit 13, information specifying the waveform of one cycle of an electrocardiogram of a target heart, information specifying the time interval of an R wave, and information specifying the time interval of a T wave (step S101). Next, the fitting information acquiring unit 120 acquires distribution shape designation information (step S102). Next, the fitting unit 131 performs fitting on the target time waveform, which is the waveform of at least one of the time intervals of an R wave and a T wave included in one cycle of the electrocardiogram waveform acquired in step S101, using a candidate cumulative distribution function indicated by the distribution shape designation information (step S103). Next, the myocardial activity information parameter acquiring unit 132 acquires myocardial activity parameters based on the fitting result (step S104). The acquired myocardial activity parameters are output to the output unit 15 (step S105).

[0139] In step S105, a graph of the fitting result for each target time waveform may be displayed. Furthermore, the process of step S102 may be executed before the process of step S103, or may be executed before the process of step S101. The processes of steps S103 and S104 are an example of processes executed by the analysis unit 130.

[0140] Fig. 11 is a first diagram showing an example of an analysis result of the signal analyzing device 1 in the first embodiment. More specifically, Fig. 11 shows an example of an analysis result by the signal analyzing device 1 for an electrocardiogram waveform of a target heart that is functioning normally. The horizontal axis of Fig. 11 represents time, and the vertical axis represents potential. The vertical axis is in arbitrary units.

[0141] FIG. 11 shows the results of the first and second fittings performed on the R wave of the depolarization phase of the electrocardiogram of a target heart in normal operation, and the results of the fourth and third fittings performed on the T wave of the repolarization phase of the electrocardiogram of a target heart in normal operation, with weights ka Instead of k a / α1, and weight k is set so that the second fitting result at time T3 and the fourth fitting result at time T4 have the same value. b Instead of k b An example is shown below, where the first and third fitting results with modified weights are connected via a straight line and referred to as the endomyocardial activity approximation function, and the second and fourth fitting results with modified weights are connected via a straight line and referred to as the epimyocardial activity approximation function.

[0142] 11 corresponds to the fact that, during normal cardiac depolarization, activity (i.e., ion channel activity) in the endomyocardium begins earlier and progresses more rapidly than in the epimyocardium, activity in the epimyocardium begins slightly later than the timing at which ion channel activity in the endomyocardium begins, and the difference between the timing at which ion channel activity in the endomyocardium begins and the timing at which activity in the epimyocardium begins is a positive, sharp R wave. That is, the mean value and standard deviation of the partial endomyocardium activity approximating function of the first fitting result of the endomyocardium activity approximating function and the mean value and standard deviation of the part of the epimyocardium activity approximating function of the second fitting result are parameters that represent the timing and progress of ion channel activity during normal cardiac depolarization.

[0143] Figure 11 also shows that during the repolarization phase of a normal heart, ion channel inactivation begins earlier in the epimyocardium than in the endomyocardium, endomyocardium inactivation begins later than epimyocardium inactivation, both epimyocardium and endomyocardium inactivation proceed slowly, and the difference between endomyocardium and epimyocardium inactivation results in a positive, slow T wave. The mean and standard deviation of the fourth fitting result of the epimyocardium activity approximating function and the third fitting result of the endomyocardium activity approximating function are parameters that represent the timing and progression of ion channel inactivation during the repolarization phase of a normal heart. As described above, the endomyocardium activity approximating function and epimyocardium activity approximating function obtained by fittings 1 to 4 correspond to the collective activation of ion channels during depolarization and the collective inactivation of ion channels during the repolarization phase. The mean and standard deviation of each fitting result of each function are parameters that represent myocardial activity.

[0144] 11. The shapes of the endomyocardial activity approximated function and the epimyocardial activity approximated function are almost identical to the measurement results of the myocardial electromotive force measured directly by inserting a catheter electrode into the myocardium of a target heart in normal functioning. This indicates that the signal analysis device 1 can obtain information representing myocardial activity from the electrocardiogram alone, without inserting a catheter electrode.

[0145] Fig. 12 is a second diagram showing an example of the analysis result of the signal analyzing device 1 in the first embodiment. More specifically, Fig. 12 shows an example of the analysis result by the signal analyzing device 1 of the electrocardiogram waveform of a target heart that is operating normally.

[0146] FIG. 12 shows three results: graph G5, graph G6, and result G7. In FIG. 12, the "inner layer side cumulative distribution function" shows the fitting result of a function that represents the collective channel activity timing distribution of ion channels present in the inner layer of the myocardium. In FIG. 12, the "outer layer side cumulative distribution function" shows the fitting result of a function that represents the collective channel activity timing distribution of ion channels present in the outer layer of the myocardium. In FIG. 12, the "body surface potential" is a function that represents the time change of the body surface potential, which is an electrocardiogram waveform. The horizontal axis of FIG. 12 represents time, and the vertical axis represents potential. The units of the horizontal and vertical axes are both arbitrary units. Note that the length of time represented by one scale interval on the horizontal axis is the same in each of FIGS. 12 to 14. Also, on the vertical axis of each of FIGS. 12 to 14, 1 represents the maximum value of the cumulative Gaussian distribution.

[0147] Graph G5 represents all of the electrocardiogram waveforms generated during one beat. Graph G6 is a portion of graph G5, showing an enlarged view of the T-wave region. The T-wave region is the region indicated as region A1 in FIG. 12. Result G7 represents the statistics of two Gaussian distributions: the Gaussian distribution that is the accumulation source of the inner layer side cumulative distribution function and the Gaussian distribution that is the accumulation 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 are the mean and standard deviation of the Gaussian distributions that are the accumulation sources of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function.

[0148] Fig. 13 is a third diagram showing an example of the analysis result of the signal analyzing device 1 in the first embodiment. More specifically, Fig. 13 shows an example of the analysis result by the signal analyzing device 1 for the electrocardiogram waveform of a target heart whose behavior is T prolongation type 3.

[0149] Figure 13 shows three results: graph G8, graph G9, and result G10. In Figure 13, the "inner layer side cumulative distribution function" shows the fitting result of a function that represents the collective channel activity timing distribution of channels present in the inner layer of the myocardium. In Figure 13, the "outer layer side cumulative distribution function" shows the fitting result of a function that represents the collective channel activity timing distribution of channels present in the outer layer of the myocardium. In Figure 13, the "body surface potential" is a function that represents the time change in the body surface potential, which is an electrocardiogram waveform. The horizontal axis of Figure 13 represents time, and the vertical axis represents the potential. Both the horizontal and vertical axes are in arbitrary units.

[0150] Graph G8 represents all of the electrocardiogram waveforms that occur during one beat. Graph G9 represents a portion of graph G8, an enlarged view of the T-wave region. The T-wave region is the region indicated as region A2 in FIG. 13. Result G10 represents the statistics of two Gaussian distributions: the Gaussian distribution that is the accumulation source of the inner layer side cumulative distribution function and the Gaussian distribution that is the accumulation source of the outer layer side cumulative distribution function. Each value in result G10 represents the statistics of the two Gaussian distributions, namely the mean and standard deviation of the Gaussian distributions that are the accumulation sources of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function.

[0151] The shapes of the inner layer cumulative distribution function and the outer layer cumulative distribution function in Fig. 13 are approximately consistent with the results of directly measuring the change in electromotive force caused by the pulsation of the outer layer of the myocardium of a target heart with T prolongation type 3 behavior by inserting a catheter electrode. This indicates that the signal analysis device 1 can acquire information representing myocardial activity from the electrocardiogram alone, without inserting a catheter electrode.

[0152] Fig. 14 is a fourth diagram showing an example of the analysis result of the signal analyzing device 1 in the first embodiment. More specifically, Fig. 14 shows an example of the analysis result by the signal analyzing device 1 for the electrocardiogram waveform of a target heart whose behavior is QT prolongation type 1.

[0153] Figure 14 shows three results: graph G11, graph G12, and result G13. In Figure 14, the "inner layer side cumulative distribution function" shows the fitting result of a function that represents the collective channel activity timing distribution of channels present in the inner layer of the myocardium. In Figure 14, the "outer layer side cumulative distribution function" shows the fitting result of a function that represents the collective channel activity timing distribution of channels present in the outer layer of the myocardium. In Figure 14, the "body surface potential" is a function that represents the time change in the body surface potential, which is an electrocardiogram waveform. The horizontal axis of Figure 14 represents time, and the vertical axis represents the potential. Both the horizontal and vertical axes are in arbitrary units.

[0154] Graph G11 represents all of the electrocardiogram waveforms generated in one beat. Graph G12 is a portion of graph G11, showing an enlarged view of the T-wave region. The T-wave region is the region indicated as region A3 in FIG. 13. Result G13 represents the statistics of two Gaussian distributions: the Gaussian distribution that is the accumulation source of the inner layer side cumulative distribution function and the Gaussian distribution that is the accumulation source of the outer layer side cumulative distribution function. Each value of result G13 represents the statistics of the two Gaussian distributions. Specifically, the statistics of the two Gaussian distributions are the mean and standard deviation of the Gaussian distributions that are the accumulation sources of the inner layer side cumulative distribution function and the outer layer side cumulative distribution function.

[0155] The shapes of the inner layer cumulative distribution function and the outer layer cumulative distribution function in Fig. 14 are approximately consistent with the results of directly measuring the change in electromotive force caused by the pulsation of the outer layer of the myocardium of a target heart operating with long QT type 3 by inserting a catheter electrode. This indicates that the signal analysis device 1 can acquire information representing myocardial activity from the electrocardiogram alone, without inserting a catheter electrode.

[0156] FIG. 14 also shows an example of the estimation results of the channel current characteristics associated with sudden death by the signal analysis device 1.

[0157] 15 to 17, it will be explained that even for an electrocardiogram of a ventricular premature contraction, information representing myocardial activity can be obtained from the electrocardiogram alone by the signal analysis device 1. In Figs. 15 to 17, the horizontal axis represents time (seconds) and the vertical axis represents potential (mV).

[0158] Fig. 15 is a first explanatory diagram for explaining an example of analysis of an electrocardiogram of a premature ventricular contraction by the signal analyzing device 1 of the first embodiment. Fig. 16 is a second explanatory diagram for explaining an example of analysis of an electrocardiogram of a premature ventricular contraction by the signal analyzing device 1 of the first embodiment. Fig. 17 is a third explanatory diagram for explaining an example of analysis of an electrocardiogram of a premature ventricular contraction by the signal analyzing device 1 of the first embodiment.

[0159] More specifically, Fig. 15 shows cardiac potentials on the body surface. That is, Fig. 15 shows a normal single heartbeat and two consecutive ventricular premature contractions recorded on an electrocardiogram. More specifically, Fig. 16 shows an endomyocardial activity approximating function including an inner layer cumulative distribution function of the depolarization of a premature ventricular contraction and an inner layer cumulative distribution function of the repolarization phase analyzed by the signal analysis device 1, and an outer layer activity approximating function including an outer layer cumulative distribution function of the depolarization of a premature ventricular contraction and an outer layer cumulative distribution function of the repolarization phase analyzed by the signal analysis device 1. More specifically, Fig. 17 shows an example of an actually measured waveform of a premature ventricular contraction on an electrocardiogram.

[0160] Figure 16 shows that the inner cumulative distribution function of depolarization precedes the outer cumulative distribution function, and the standard deviation of both is larger than that of a normal heartbeat, indicating that the excitation spreads more slowly. This analysis result is consistent with the waveform characteristics of the broad-tailed R wave.

[0161] Figure 16 shows that the inner cumulative distribution function begins deactivation earlier than the outer cumulative distribution function during the repolarization phase, and the order of inner and outer deactivation is shown as the magnitude relationship between the average values ​​of the two cumulative distribution functions during the repolarization phase. The function obtained by subtracting the outer cumulative distribution function from the inner cumulative distribution function in Figure 16 matches the characteristics of the large negative T wave during the repolarization phase, and as shown in Figure 17, the waveform obtained by subtracting the outer cumulative distribution function from the inner cumulative distribution function approximately matches the waveform of an actually measured ventricular premature contraction on an electrocardiogram.

[0162] The results in Figures 15 to 17 show that the analysis by the signal analysis device 1 is applicable to cases in which rogue waves or negative potentials occur due to altered conduction of myocardial excitation, early repolarization, or delayed repolarization. In Figures 15 to 17, the mean μ of the inner cumulative distribution function during the depolarization phase is -1, and the standard deviation σ is 0.32. In Figures 15 to 17, the mean μ of the outer cumulative distribution function during the depolarization phase is -0.8, and the standard deviation σ is 0.21. In Figures 15 to 17, the mean μ of the inner cumulative distribution function during the repolarization phase is 1, and the standard deviation σ is 1. In Figures 15 to 17, the mean μ of the outer cumulative distribution function during the repolarization phase is 2.99, and the standard deviation σ is 0.7.

[0163] 18 to 20, it will be explained that the signal analysis device 1 can obtain information representing myocardial activity from the electrocardiogram alone, even for the electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1. The vertical axes of Figures 18 to 20 represent potential in millivolts.

[0164] Fig. 18 is a first explanatory diagram for explaining an example of analysis of an electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 in the first embodiment. Fig. 19 is a second explanatory diagram for explaining an example of analysis of an electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 in the first embodiment. Fig. 20 is a third explanatory diagram for explaining an example of analysis of an electrocardiogram of a target heart during the depolarization period of Brugada syndrome type 1 by the signal analysis device 1 in the first embodiment.

[0165] More specifically, Figure 18 shows an electrocardiogram of chest lead 2 in Brugada syndrome. In Figure 18, the inner frame W1 indicates the depolarization phase, and the inner frame W2 indicates the repolarization phase. This is also true for Figure 19. In Figure 19, the inner frame W1 indicates the depolarization phase, and the inner frame W2 indicates the repolarization phase.

[0166] More specifically, FIG. 19 shows the inner and outer cumulative distribution functions during the depolarization and repolarization phases analyzed by the signal analysis device 1. In the example shown in FIG. 19, the repolarization phase of the inner cumulative distribution function begins after the depolarization phase, indicating a characteristic of early repolarization. Meanwhile, in the example shown in FIG. 19, a difference is observed in the potential amplitude of the outer cumulative distribution function between the depolarization and repolarization phases. FIG. 19 also shows the gap and anisotropy between the depolarization and repolarization phases of the outer cumulative distribution function. In this way, the signal analysis device 1 can express early repolarization, which is a characteristic of the waveform shown in the electrocardiogram of a patient's heart suffering from Brugada syndrome, and the anisotropy between depolarization and repolarization by the means and standard deviations of the inner and outer cumulative distribution functions for the depolarization and repolarization phases, and the ratio of the weight assigned to the outer cumulative distribution function to the weight assigned to the inner cumulative distribution function (inner-outer ratio).

[0167] Figure 20 compares the analysis results with the actual measurements. More specifically, Figure 20 shows the difference between the inner layer cumulative distribution function and the outer layer cumulative distribution function during the depolarization and repolarization phases of Figure 19. Figure 20 also shows the actual measurements of the electrocardiogram. The analysis results and the actual measurements are almost identical, except for the very end. The very end refers to the potential at a later time.

[0168] 18 to 20, the mean μ of the inner layer side cumulative distribution function during the depolarization phase is 15, and the standard deviation σ is 0.15. Also, in FIGS. 18 to 20, the mean μ of the outer layer side cumulative distribution function during the depolarization phase is 14, and the standard deviation σ is 0.25. Also, in FIGS. 18 to 20, the inner / outer layer ratio during the depolarization phase is 0.45. Also, in FIGS. 18 to 20, the mean μ of the inner layer side cumulative distribution function during the repolarization phase is 25, and the standard deviation σ is 0.25. Also, in FIGS. 18 to 20, the mean μ of the outer layer side cumulative distribution function during the repolarization phase is 20, and the standard deviation σ is 0.5.

[0169] Figures 21 to 59 show the publicly available ECG data library.<https: / / physionet.org / about / database / > 21 to 59 show the inner layer side cumulative distribution function and outer layer side cumulative distribution function obtained as a result of performing analysis of cardiac potentials by the signal analysis device 1, and the fitting results.

[0170] 21 to 59 each show an example of electrocardiogram analysis performed by the signal analysis device 1 according to the first embodiment. The determined points in each figure represent, from left to right, the Q point, R point, S point, T start point, and T end point of the electrocardiogram, respectively. The determined points were determined using an inflection point detection and peak detection algorithm. Each of FIGS. 21 to 59 shows the inner cumulative distribution function and outer cumulative distribution function obtained for the depolarization phase (QRS wave) and the repolarization phase (T wave) in each section. FIGS. 21 to 59 show that the signal analysis device 1 can generate substantially identical waveforms for various QRS waves and T waves by adjusting the mean and standard deviation of the inner cumulative distribution function and outer cumulative distribution function. The bottom figures in FIGS. 21 to 59 show the original electrocardiogram waveforms and the fitting results. The results in FIGS. 21 to 59 are obtained by sampling at 300 Hz. Therefore, the origin of the horizontal axis in each of FIGS. 21 to 59 represents 0 seconds, and a value of 1 represents 3.33 milliseconds.

[0171] The signal analyzing device 1 configured as described above uses a waveform of either an R wave or a T wave in a time interval included in one cycle of a waveform representing the cardiac cycle of a target heart as a target time waveform. The target time waveform is approximated by an approximated time waveform, which is a time waveform based on the difference or weighted difference between a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, and a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution. The signal analyzing device 1 acquires, as parameters representing the characteristics of the target time waveform, parameters specifying the first unimodal distribution or the first cumulative distribution function, and parameters specifying the second unimodal distribution or the second cumulative distribution function. The first cumulative distribution function is information indicating activity in the endomyocardium of the target heart, and the second cumulative distribution function is information indicating activity in the epimyocardium of the target heart. Therefore, the parameters representing the shape of the first cumulative distribution function are parameters indicating activity in the endomyocardium of the target heart (endomyocardium parameters), and the parameters representing the shape of the second cumulative distribution function are parameters indicating activity in the epimyocardium of the target heart (epimyocardium parameters). The information clearly indicating the characteristics of the activity of the endocardial layer of the target heart and the information clearly indicating the characteristics of the activity of the epicardial layer of the target heart, such as these parameters, cannot be obtained by conventional analysis of the electrocardiogram waveform. Therefore, the signal analysis device 1 can obtain information useful for understanding the state of the heart from the electrocardiogram waveform.

[0172] [Only some parameters are acquired] When it is desired to grasp only the characteristics of activity of the endomyocardium of the target heart, only the endomyocardium parameters may be acquired by the signal analyzing device 1, and when it is desired to grasp only the characteristics of activity of the epimyocardium of the target heart, only the epimyocardium parameters may be acquired by the signal analyzing device 1. Furthermore, only some of the parameters representing the shape of the cumulative distribution function may be acquired by the signal analyzing device 1 as the endomyocardium parameters or the epimyocardium parameters.

[0173] For example, the myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 acquires at least one of the following as myocardial activity parameters indicating the activity of the myocardium of the target heart when a first target time waveform, which is the waveform of the time interval of the R wave of the target heart, is approximated by a first approximated time waveform, which is a time waveform obtained by approximating the first cumulative distribution function, which is the cumulative distribution function of a first unimodal distribution, and the second cumulative distribution function, which is the cumulative distribution function of a second unimodal distribution, or by a first approximated time waveform, which is a time waveform obtained by adding a 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 a second unimodal distribution.

[0174] For example, if both the first unimodal distribution and the second unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire, as the myocardial activity parameter, at least one of the mean value of the first unimodal distribution, the standard deviation or variance of the first unimodal distribution, the mean value of the second unimodal distribution, and the standard deviation or variance of the second unimodal distribution. The mean value of a Gaussian distribution is the time when the frequency value in the unimodal distribution is at its maximum and the time when the slope of the cumulative distribution function of the unimodal distribution is at its maximum. Therefore, for example, regardless of whether the first unimodal distribution and the second unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire, as the myocardial activity parameter, at least one of the time corresponding to the maximum value of the first unimodal distribution, the time corresponding to the maximum slope of the first cumulative distribution function, the time corresponding to the maximum value of the second unimodal distribution, and the time corresponding to the maximum slope of the second cumulative distribution function.

[0175] For example, the myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 acquires at least one of the following as myocardial activity parameters indicating myocardial activity of the target heart: at least a portion of the parameters specifying the third unimodal distribution, at least a portion of the parameters specifying the third cumulative distribution function, at least a portion of the parameters specifying the fourth unimodal distribution, or at least a portion of the parameters specifying the fourth cumulative distribution function, when the second target inverse time waveform, which is a waveform obtained by reversing the time axis of the second target time waveform, which is a waveform of the time interval of the T wave of the target heart, is approximated by the second approximated inverse time waveform, which is a waveform obtained by the difference or weighted difference between the third cumulative distribution function, which is a cumulative distribution function of the third unimodal distribution, and the fourth cumulative distribution function, which is a cumulative distribution function of the fourth unimodal distribution, or by the second approximated inverse time waveform, which is a waveform obtained by adding a level value to the difference or weighted difference between the third cumulative distribution function, which is the cumulative distribution function of the third unimodal distribution, and the fourth cumulative distribution function, which is the cumulative distribution function of the fourth unimodal distribution.

[0176] For example, if the third unimodal distribution and the fourth unimodal distribution are both Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire at least 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. Furthermore, regardless of whether the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire at least one of the time corresponding to the maximum value of the third unimodal distribution, the time corresponding to the maximum slope of the third cumulative distribution function, the time corresponding to the maximum value of the fourth unimodal distribution, and the time corresponding to the maximum slope of the fourth cumulative distribution function as the myocardial activity parameter. When acquiring time as the myocardial activity parameter, the myocardial activity information parameter acquiring unit 132 acquires time (the value of x in the above example) instead of information representing time in the reverse direction (the value of x' in the above example) even when approximating a waveform with a reversed time axis.

[0177] For example, the myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 may set 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 may calculate a second target time waveform, which is a waveform of a time interval of a T wave of the target heart, as a second approximate time waveform, which is a waveform obtained by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or as the third inverse cumulative distribution function. When the third unimodal distribution is approximated by a second approximate time waveform, which is a waveform obtained by adding a level value to the difference or weighted difference between the third unimodal distribution function and the fourth inverse cumulative distribution function, at least one of the parameters specifying the third cumulative distribution function, at least one of the parameters specifying the fourth unimodal distribution, and at least one of the parameters specifying the fourth cumulative distribution function is obtained as a myocardial activity parameter, which is a parameter indicating the activity of the myocardium of the target heart.

[0178] For example, if the third unimodal distribution and the fourth unimodal distribution are both Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire at least 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. Furthermore, for example, regardless of whether the third unimodal distribution and the fourth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire at least one of the time corresponding to the maximum value of the third unimodal distribution, the time corresponding to the maximum slope of the third cumulative distribution function, the time corresponding to the maximum value of the fourth unimodal distribution, and the time corresponding to the maximum slope of the fourth cumulative distribution function as the myocardial activity parameter.

[0179] Similarly, for example, when the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 further approximates a residual time waveform, which is the time waveform of the difference between the first target time waveform and the first approximated time waveform, or a residual time waveform, which is the time waveform of the difference between the second target time waveform and the second approximated time waveform, or a residual time waveform, which is the time waveform of the difference between the second target time waveform and the second approximated time waveform, which is a waveform obtained by inverting the time axis of the second approximated inverse time waveform, with a fifth cumulative distribution function, which is the cumulative distribution function of a fifth unimodal distribution, or an approximated residual time waveform, which is a time waveform obtained by multiplying the fifth cumulative distribution function by a weight, at least some of the parameters specifying the fifth unimodal distribution and at least some of the parameters specifying the fifth cumulative distribution function are also acquired as myocardial activity parameters, which are parameters indicating the activity of the myocardium of the target heart.

[0180] For example, if the fifth unimodal distribution is a Gaussian distribution, the myocardial activity information parameter acquiring unit 132 may acquire at least one of the mean value of the fifth unimodal distribution, the standard deviation or the variance of the fifth unimodal distribution as the myocardial activity parameter. Furthermore, for example, regardless of whether the fifth unimodal distribution is a Gaussian distribution, the myocardial activity information parameter acquiring 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.

[0181] Furthermore, for example, when the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 further approximates the residual time waveform with an approximated residual time waveform, which is a time waveform based on the difference or weighted difference between a fifth cumulative distribution function, which is the cumulative distribution function of a fifth unimodal distribution, and a sixth cumulative distribution function, which is the cumulative distribution function of a sixth unimodal distribution, it acquires at least any of the parameters specifying the fifth unimodal distribution, at least any of the parameters specifying the fifth cumulative distribution function, at least any of the parameters specifying the sixth unimodal distribution, and at least any of the parameters specifying the sixth cumulative distribution function as myocardial activity parameters, which are parameters indicating the activity of the myocardium of the target heart.

[0182] For example, if the fifth unimodal distribution and the sixth unimodal distribution are both Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire at least one of the mean value of the fifth unimodal distribution, the standard deviation or variance of the fifth unimodal distribution, the mean value of the sixth unimodal distribution, and the standard deviation or variance of the sixth unimodal distribution as the myocardial activity parameter. Furthermore, for example, regardless of whether the fifth unimodal distribution and the sixth unimodal distribution are Gaussian distributions, the myocardial activity information parameter acquiring unit 132 may acquire at least one of the time corresponding to the maximum value of the fifth unimodal distribution, the time corresponding to the maximum slope of the fifth cumulative distribution function, the time corresponding to the maximum value of the sixth unimodal distribution, and the time corresponding to the maximum slope of the sixth cumulative distribution function as the myocardial activity parameter.

[0183] [Obtaining myocardial activity parameters by calculating parameters obtained by fitting] The characteristics of the myocardial activity of the target heart may not only be reflected in the parameters obtained by the above-described fitting, but may also be directly reflected in the value obtained by calculating the parameters obtained by fitting. Therefore, the value obtained by calculating the parameters obtained by fitting may be acquired as the myocardial activity parameter by the signal analysis device 1. The parameters obtained by fitting are at least one of a parameter specifying a unimodal distribution or a parameter specifying a cumulative distribution function, a weight, and a level value. If the unimodal distribution is a Gaussian distribution, the parameter specifying the unimodal distribution or the parameter specifying the cumulative distribution function is at least one of the mean and the standard deviation (or variance). Regardless of whether the unimodal distribution is a Gaussian distribution or not, the time corresponding to the maximum value of the unimodal distribution is an example of a parameter specifying the unimodal distribution, and the time corresponding to the maximum slope of the cumulative distribution function of the unimodal distribution is an example of a parameter specifying the cumulative distribution function.

[0184] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may acquire a parameter indicating the myocardial activity of the target heart (a parameter different from each of the above-mentioned parameters) by calculating a myocardial activity parameter (i.e., a parameter indicating the myocardial activity of the target heart in the time interval of the R wave) obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in one cycle of the waveform indicating the cardiac cycle of the target heart, and a myocardial activity parameter (i.e., a parameter indicating the myocardial activity of the target heart in the time interval of the T wave) obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in the waveform of the same one cycle.

[0185] Furthermore, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may acquire a parameter indicating the activity of the myocardium of the target heart (a parameter different from each of the above-mentioned parameters) by calculating a parameter indicating the activity of the inner layer of the myocardium of the target heart obtained by the above-mentioned fitting of the waveform of the time interval of the R wave included in one cycle of the waveform indicating the cardiac cycle of the target heart, and a parameter indicating the activity of the outer layer of the myocardium of the target heart obtained by the fitting.

[0186] Furthermore, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may acquire a parameter indicating the activity of the myocardium of the target heart (a parameter different from each of the above-mentioned parameters) by calculating a parameter indicating the activity of the inner layer of the myocardium of the target heart obtained by the above-mentioned fitting of the waveform of the time interval of the T wave included in one cycle of the waveform indicating the cardiac cycle of the target heart, and a parameter indicating the activity of the outer layer of the myocardium of the target heart obtained by the fitting.

[0187] Below, an example will be described in which the myocardial activity parameter is a value obtained by calculating the means obtained by fitting when the first to fourth unimodal distributions are all Gaussian distributions. If the first to fourth unimodal distributions are not Gaussian distributions, the "mean" in the following example can be interpreted as "the time when the value in the unimodal distribution is maximum" i.e., "the time corresponding to the maximum value of the unimodal distribution," "the time when the slope in the cumulative distribution function is maximum" i.e., "the time corresponding to the maximum slope of the cumulative distribution function," etc.

[0188] (1) A parameter that represents the time it takes for the depolarization to switch to repolarization. It is known that an extremely short or long time from depolarization of the inner or outer layer of the myocardium to repolarization of the inner or outer layer of the myocardium may lead to sudden death due to arrhythmia. In other words, a shortened or prolonged time from depolarization to repolarization of the myocardium may indicate the occurrence of some pathological condition in the myocardium. Therefore, the signal analysis device 1 may acquire 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, it may acquire at least one of the following four parameters (1A) to (1D) as the myocardial activity parameter.

[0189] (1A) A parameter that represents the time it takes for the myocardial inner layer to switch from depolarization to repolarization. The myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 may acquire, as the myocardial activity parameter, the difference between the average of a first unimodal distribution obtained by the above-described fitting of a first target time waveform, which is a waveform of the time interval of an R wave included in one cycle of the waveform representing the cardiac cycle of the target heart, and the average of a third unimodal distribution obtained by the above-described fitting of a second target time waveform, which is a waveform of the time interval of a T wave included in the waveform of one cycle. For example, as in the above-described example, if the average of the first unimodal distribution is μ a and the mean of the third unimodal distribution is μ c Then, the myocardial activity information parameter acquiring unit 132 calculates |μa -μ c | may be acquired as a myocardial activity parameter. c μ is better a Since the time is later than μ c -μ a may be acquired as a myocardial activity parameter. This myocardial activity parameter is a parameter that indicates the time it takes for the depolarization of the inner layer of the myocardium to switch to repolarization of the inner layer of the myocardium.

[0190] (1B) A parameter that represents the time from depolarization of the outer layer of the myocardium to repolarization of the outer layer of the myocardium. The myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 may acquire, as the myocardial activity parameter, the difference between the average of the second unimodal distribution obtained by the above-described fitting of the first target time waveform, which is the waveform of the time interval of the R wave included in one cycle of the waveform indicating the cardiac cycle of the target heart, and the average of the fourth unimodal distribution obtained by the above-described fitting of the second target time waveform, which is the waveform of the time interval of the T wave included in the waveform of one cycle. For example, as in the above-described example, if the average of the second unimodal distribution is μ b and the mean of the fourth unimodal distribution is μ d Then, the myocardial activity information parameter acquiring unit 132 calculates |μ b -μ d | may be acquired as a myocardial activity parameter. d μ is better b Since the time is later than μ d -μ b may be acquired as the myocardial activity parameter. This myocardial activity parameter is a parameter that indicates the time it takes for the outer layer of the myocardium to switch from depolarization to repolarization of the outer layer of the myocardium.

[0191] (1C) A parameter that represents the time it takes for the depolarization of the outer layer of the myocardium to switch to repolarization of the inner layer of the myocardium. The myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 may acquire, as the myocardial activity parameter, the difference between the average of the second unimodal distribution obtained by the above-described fitting of the first target time waveform, which is the waveform of the time interval of the R wave included in one cycle of the waveform indicating the cardiac cycle of the target heart, and the average of the third unimodal distribution obtained by the above-described fitting of the second target time waveform, which is the waveform of the time interval of the T wave included in the waveform of one cycle. For example, as in the above-described example, if the average of the second unimodal distribution is μ b and the mean of the third unimodal distribution is μ c Then, the myocardial activity information parameter acquiring unit 132 calculates |μ b -μ c | may be acquired as a myocardial activity parameter. c μ is better b Since the time is later than μ c -μ b may be acquired as a myocardial activity parameter, which represents the time it takes for depolarization of the outer layer of the myocardium to switch to repolarization of the inner layer of the myocardium.

[0192] (1D) A parameter that represents the time it takes for the depolarization of the inner layer of the myocardium to switch to the repolarization of the outer layer of the myocardium. The myocardial activity information parameter acquiring unit 132 of the analyzing unit 130 of the signal analyzing device 1 may acquire, as the myocardial activity parameter, the difference between the average of a first unimodal distribution obtained by the above-described fitting of a first target time waveform, which is a waveform of the time interval of an R wave included in one cycle of the waveform representing the cardiac cycle of the target heart, and the average of a fourth unimodal distribution obtained by the above-described fitting of a second target time waveform, which is a waveform of the time interval of a T wave included in the waveform of one cycle. For example, if the average of the first unimodal distribution is μ a and the mean of the fourth unimodal distribution is μ d Then, the myocardial activity information parameter acquiring unit 132 calculates |μ a -μ d | may be acquired as a myocardial activity parameter. d μ is bettera Since the time is later than μ d -μ a may be acquired as a myocardial activity parameter. This myocardial activity parameter is a parameter that indicates the time it takes for depolarization of the inner layer of the myocardium to switch to repolarization of the outer layer of the myocardium.

[0193] (2) Parameters that represent the time difference and sequence of activity between the inner and outer layers during depolarization If the time difference between the activity of the inner layer and the activity of the outer layer during depolarization is longer than normal, it may indicate a delay or block in the conduction of myocardial excitation, or the location where excitation begins or the order in which excitation propagates may differ from the normal pattern, particularly suggesting a disorder in the myocardial conduction system, an ischemic state of the myocardium, or the presence of premature contractions. Therefore, the signal analysis device 1 may acquire, as myocardial activity parameters, parameters representing the time difference and order between the activity of the inner layer and the activity of the outer layer during depolarization. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may acquire, as myocardial activity parameters, the difference between the mean of the first unimodal distribution and the mean of the second unimodal distribution obtained by the above-described fitting of a first target time waveform, which is a waveform for the time interval of an R wave included in one waveform representing the cardiac cycle of the target heart. For example, if the mean of the first unimodal distribution is μ as in the above-described example, a and the mean of the second unimodal distribution is μ b Then, the myocardial activity information parameter acquisition unit 132 calculates μ a -μ b Or μ b -μ a may be obtained as a myocardial activity parameter.

[0194] (3) Parameters that represent the time difference and sequence of activity between the inner and outer layers during repolarization When the time difference between the activity of the inner layer and the activity of the outer layer during repolarization is extended or shortened, some abnormality may occur in the myocardium. Therefore, the signal analysis device 1 may acquire, as the myocardial activity parameter, a parameter that indicates the time difference and order between the activity of the inner layer and the activity of the outer layer during repolarization. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 may acquire, as the myocardial activity parameter, the difference between the mean of the third unimodal distribution and the mean of the fourth unimodal distribution obtained by the above-mentioned fitting of the second target time waveform, which is the waveform of the time interval of the T wave included in one waveform period indicating the cardiac cycle of the target heart. For example, as in the above-mentioned example, if the mean of the third unimodal distribution is μ c and the mean of the fourth unimodal distribution is μ d Then, the myocardial activity information parameter acquisition unit 132 calculates μ c -μ d Or μ d -μ c may be obtained as a myocardial activity parameter.

[0195] (Correction of myocardial activity parameters) Among the above-described myocardial activity parameters acquired by the myocardial activity information parameter acquiring unit 132, the myocardial activity parameters related to the width in the time direction have the characteristic that, like parameters related to the width in the time direction (e.g., QT interval) among conventional parameters representing cardiac activity, they are affected by fluctuations due to heart rate and individual differences such as age and gender. It is known that conventional parameters related to the width in the time direction are corrected to reduce the influence of fluctuations due to heart rate and individual differences, and the corrected values ​​are used as parameters for evaluating cardiac activity. Therefore, the myocardial activity parameters acquired by the myocardial activity information parameter acquiring unit 132 may also be corrected to reduce the influence of fluctuations due to heart rate and individual differences, and the corrected values ​​may be used as parameters for evaluating myocardial activity. For example, to address fluctuations due to heart rate, the myocardial activity parameters acquired by the above-described fitting may be corrected by linear or nonlinear calculation based on the time interval between adjacent R spikes (hereinafter referred to as "RR interval"), and the corrected values ​​may be used as myocardial activity parameters. To address individual differences, the myocardial activity parameters obtained by the fitting described above may be corrected by determining the relationship between the RR intervals and the myocardial activity parameters from each individual's electrocardiogram data, and the corrected values ​​may be used as the myocardial activity parameters. This correction may be performed by a separate device after the signal analyzing device 1 outputs the myocardial activity parameters obtained by the fitting, or by the signal analyzing device 1 itself. When the signal analyzing device 1 corrects the myocardial activity parameters, for example, the myocardial activity information parameter acquiring unit 132 may correct the myocardial activity parameters obtained by the fitting described above and output the corrected values ​​as the myocardial activity parameters. Note that when the correction is performed within the signal analyzing device 1, the myocardial activity parameters obtained by the fitting described above are ultimately intermediate parameters obtained within the device, but they are still parameters representing myocardial activity, just like when they are output to an external device.

[0196] (Variation) It is preferable that the electrocardiogram be an electrocardiogram with leads close to the cardiac electromotive force vector. An electrocardiogram with leads close to the cardiac electromotive force vector is preferably an electrocardiogram that can acquire three-dimensional information of cardiac potential, such as lead II, lead V4, or lead V5. Since the amount of information increases as the number of channels in an electrocardiogram increases, the more channels an electrocardiogram has, the more desirable it is. Specifically, the signal analysis device 1 may analyze the waveforms of each channel of a multi-channel electrocardiogram to obtain myocardial activity parameters as the analysis results for each channel.

[0197] The signal analyzing device 1 may be implemented using a plurality of information processing devices communicably connected via a network, in which case the respective functional units of the signal analyzing device 1 may be distributed and implemented in the plurality of information processing devices.

[0198] The electrocardiogram acquisition unit 110 is an example of a biological information acquisition unit.

[0199] Second Embodiment In the first embodiment, myocardial activity parameters were obtained from time-series biometric information regarding the heart beating as information useful for understanding the state of the heart, but it is also possible to obtain from time-series biometric information regarding the heart beating whether the heart is normal or not, and if the heart is not normal, what kind of disease it corresponds to, as information useful for understanding the state of the heart, and this form will be described as the second embodiment.

[0200] As explained in the Background Art section, even if electrocardiogram waveforms are similar, the onset of heart-related diseases may differ. In other words, even if an estimation model that uses time-series biological information related to heartbeats as input to obtain estimation results such as whether the heart is normal or what kind of disease corresponds to an abnormal heart, is trained and an estimation result is obtained by estimation using the trained estimation model, the estimation accuracy may not be high.

[0201] On the other hand, the signal analyzing device 1 of the first embodiment can obtain, from one channel of time-series biometric information related to cardiac pulsation, information clearly indicating the characteristics of cardiac activity as myocardial activity parameters. Furthermore, as described in the first embodiment, the myocardial activity parameters obtained by the signal analyzing device 1 of the first embodiment are strongly related to whether the heart is normal and what kind of disease the abnormal heart corresponds to. Given these facts, if myocardial activity parameters are obtained from time-series biometric information related to cardiac pulsation by processing similar to that performed by the signal analyzing device 1 of the first embodiment, and an estimation model is trained using the acquired myocardial activity parameters to obtain estimation results such as whether the heart is normal and what kind of disease the abnormal heart corresponds to, and if the estimation results are obtained by estimation using the trained estimation model, highly accurate estimation results can be expected. The information providing system of the second embodiment performs this learning and estimation. It is expected that doctors can use the estimation results of the information providing system of the second embodiment as a reference during diagnosis, thereby more accurately diagnosing whether the target heart is normal and what kind of disease the abnormal heart corresponds to.

[0202] 60, the information providing system 200 of the second embodiment includes a learning device 300 and an information providing device 400. The hardware configurations of the information providing system 200, the learning device 300, and the information providing device 400 are similar to, for example, the hardware configuration of the signal analyzing device 1 shown in FIG.

[0203] First, the learning device 300 will be described. Learning device 300 60, the learning device 300 includes a signal analysis unit 310 and a learning unit 320. The learning device 300 performs the processes of steps S310 and S320 shown in FIG.

[0204] [Source dataset, source data] A learning source data set is input to the learning device 300. The learning source data set includes a plurality of pieces of learning source data. Each piece of learning source data includes at least time-series biological information related to the pulsation of the heart from which the learning source data was acquired (hereinafter referred to as the "target heart") and cardiac condition information representing the condition of the target heart. Each piece of learning source data may include information for identifying the learning source data, such as an identification number uniquely assigned to the learning source data.

[0205] An example of time-series biological information related to the heartbeat of the target heart is an electrocardiogram waveform of the target heart. Cardiac condition information of the target heart is information that uniquely identifies a diagnosis result of the heart condition made by a doctor who has viewed the time-series biological information related to the heartbeat of the target heart (e.g., an electrocardiogram waveform). The doctor's diagnosis result of the heart condition specifically includes whether the heart is in a normal state or not, and if not, which disease the heart corresponds to. That is, for example, the cardiac condition information may be a specific numerical value assigned in advance to indicate whether the heart is in a normal state or which disease the heart is suffering from, such as "0" if the doctor who has viewed the electrocardiogram waveform determines that the heart is in a normal state; "1" if the doctor who has viewed the electrocardiogram waveform determines that the heart is suffering from Brugada syndrome; "2" if the doctor who has viewed the electrocardiogram waveform determines that the heart is suffering from ischemic heart disease; "3" if the doctor who has viewed the electrocardiogram waveform determines that the heart is suffering from early repolarization syndrome; etc.

[0206] Alternatively, for example, the cardiac status information may be a specific numerical value assigned in advance to indicate whether the cardiac rhythm is normal or what type of arrhythmia is present, such as "0" if a doctor who looks at the electrocardiogram waveform determines that the cardiac rhythm is normal, "1" if a doctor who looks at the electrocardiogram waveform determines that the cardiac rhythm is a supraventricular extrasystole, "2" if a doctor who looks at the electrocardiogram waveform determines that the cardiac rhythm is atrial fibrillation, "3" if a doctor who looks at the electrocardiogram waveform determines that the cardiac rhythm is a ventricular extrasystole, "4" if a doctor who looks at the electrocardiogram waveform determines that the cardiac rhythm is ventricular fibrillation, etc.

[0207] Alternatively, for example, the cardiac status information may be a specific numerical value assigned in advance to indicate whether the cardiac blood flow is normal, whether it is ischemic, and where the ischemic area is, such as "0" if a doctor who looks at the electrocardiogram waveform determines that the coronary artery blood flow is normal; "1" if a doctor who looks at the electrocardiogram waveform determines that the endocardial myocardium is ischemic; "2" if a doctor who looks at the electrocardiogram waveform determines that the endocardial and outer myocardium are ischemic (transmural ischemic); "3" if a doctor who looks at the electrocardiogram waveform determines that the anterior wall myocardial infarction is present; "4" if a doctor who looks at the electrocardiogram waveform determines that the inferior wall myocardial infarction is present; etc.

[0208] Let Y be the number of pieces of original data included in the original data set, and y be an integer between 1 and Y inclusive. The y-th piece of original data includes at least time-series biological information related to the pulsation of the target heart from which the y-th piece of original data is acquired and cardiac status information of the target heart from which the y-th piece of original data is acquired. The Y pieces of original data included in the original data set may be acquired from the target hearts of Y people, or may be acquired from the target hearts of less than Y people. When the Y pieces of original data acquired from the target hearts of less than Y people are used as the original data, multiple pieces of original data acquired from the target hearts of the same person will be included in the original data set. In this case, the multiple pieces of original data acquired from the target hearts of the same person may be original data acquired at different times and may be original data with different cardiac status information.

[0209] The time-series biological information related to heartbeats included in each learning source data may be a waveform for one cycle or multiple cycles. For example, when a doctor diagnoses the condition of the heart by looking at any one cycle of the waveform of an electrocardiogram, the waveform for that one cycle may be included in the learning source data as time-series biological information related to heartbeats, and information specifying a diagnosis result based on the waveform for that one cycle may be included in the learning source data as cardiac condition information. Also, for example, when a doctor diagnoses the condition of the heart by looking at multiple cycles of the waveform of an electrocardiogram, the waveforms for those multiple cycles may be included in the learning source data as time-series biological information related to heartbeats, and information specifying a diagnosis result based on the waveforms for those multiple cycles may be included in the learning source data as cardiac condition information.

[0210] [Signal analysis section 310] The signal analysis unit 310 receives time-series biological information related to cardiac pulsation, which is included in each learning source data set included in the learning source data set input to the learning device 300. The signal analysis unit 310 acquires a set of myocardial activity parameters from the time-series biological information related to cardiac pulsation by performing the same process as the signal analysis device 1 of the first embodiment (step S310). The myocardial activity parameters acquired by the signal analysis unit 310 are one or more of the myocardial activity parameters described in the first embodiment, and are myocardial activity parameters of a type preselected as the myocardial activity parameters to be used by the learning unit 320 (described later). Hereinafter, the set of myocardial activity parameters acquired by the signal analysis unit 310 will be referred to as a "myocardial activity parameter set." In some cases, the signal analysis unit 310 acquires only one type of myocardial activity parameter for one cycle. However, the "set of myocardial activity parameters," the "myocardial activity parameter set" and "each myocardial activity parameter included in the myocardial activity parameter set" may be appropriately interpreted as a "myocardial activity parameter." The myocardial activity parameter set acquired by the signal analysis section 310 is output from the signal analysis section 310 and input to the learning section 320 .

[0211] When the time-series biological information related to heartbeats included in each learning source data is a waveform for one cycle, the signal analysis unit 310 acquires a set of one or more of the above-mentioned myocardial activity parameters from the waveform for one cycle as a myocardial activity parameter set.When the time-series biological information related to heartbeats included in each learning source data is a waveform for multiple cycles, the signal analysis unit 310 acquires a set of one or more of the above-mentioned myocardial activity parameters from the waveform for each cycle, and acquires the set of myocardial activity parameters for multiple cycles as a myocardial activity parameter set.

[0212] [Learning Section 320] The learning unit 320 receives, as input, myocardial activity parameter sets obtained by the signal analysis unit 310 from time-series biological information relating to the pulsation of a target heart, which is included in each of the source data included in the source data sets input to the learning device 300, and cardiac state information included in each of the source data included in the source data sets input to the learning device 300. The learning unit 320 uses, as learning data, pairs of myocardial activity parameter sets and cardiac state information based on the same source data. That is, in each of the learning data, the myocardial activity parameter sets and cardiac state information are based on the same time-series biological information relating to the pulsation of a target heart. In other words, each myocardial activity parameter set corresponds to a respective piece of cardiac state information, and each piece of cardiac state information corresponds to a respective piece of myocardial activity parameter set.

[0213] If a set of all the learning data used by the learning unit 320 is referred to as a learning data set, the learning data set used by the learning unit 320 is made up of a plurality (Y) of learning data. The y-th learning data includes at least a myocardial activity parameter set obtained by the signal analysis unit 310 from time-series biological information related to the beating of the target heart included in the y-th learning source data, and cardiac state information which is information representing the state of the target heart included in the y-th learning source data.

[0214] The learning unit 320 uses the training dataset, more specifically, a set of myocardial activity parameter sets included in each training data set included in the training dataset and cardiac state information corresponding to the myocardial activity parameter sets, to learn an estimation model that uses the myocardial activity parameter sets as input to obtain cardiac state information corresponding to the myocardial activity parameter sets, thereby obtaining a trained estimation model (step S320). The trained estimation model obtained by the learning unit 320 is output from the learning unit 320 as the output of the learning device 300. A well-known learning technique may be used to train the estimation model, and the number of training data may be sufficient to train the estimation model. In other words, the number of original learning data may be the same as the number of training data.

[0215] [First example of learning device 300] An overview of each data and operation of the learning device 300 will be described using, as a first example, a case where time-series biological information relating to heartbeats included in each learning source data is a waveform for one cycle.

[0216] If the time-series biological information related to heartbeats included in the y-th piece of learning source data is Wave(y) and the cardiac status information is Info(y), the y-th piece of learning source data is [Wave(y), Info(y)]. That is, the learning source data set is [[Wave(1), Info(1)], [Wave(2), Info(2)], ..., [Wave(Y), Info(Y)]].

[0217] The signal analysis unit 310 obtains a myocardial activity parameter set Para(y) from time-series biological information Wave(y) relating to the heartbeat for each y of 1, ..., Y. Each myocardial activity parameter set Para(y) is a set of one or more myocardial activity parameters obtained from time-series biological information Wave(y) relating to the heartbeat by the same processing as that performed by the signal analysis device 1 of the first embodiment, and therefore has one or more elements.

[0218] Since the learning unit 320 uses pairs of myocardial activity parameter sets and cardiac state information based on the same learning source data as learning data, the y-th learning data is [Para(y), Info(y)]. That is, the learning data set is [[Para(1), Info(1)], [Para(2), Info(2)], ..., [Para(Y), Info(Y)]]. The learning unit 320 uses [Para(1), Info(1)], [Para(2), Info(2)], ..., [Para(Y), Info(Y)] to learn an estimation model that receives a myocardial activity parameter set as input and obtains cardiac state information corresponding to the myocardial activity parameter set, thereby obtaining a trained estimation model.

[0219] [Second Example of Learning Device 300] As a second example, a case where time-series biological information related to heartbeats included in each learning source data is a waveform covering multiple periods will be used to explain an overview of each data and operation of the learning device 300. In the second example, it is assumed that the time-series biological information related to heartbeats included in each learning source data is a waveform covering Z periods, and each of the period indices 1, ..., Z is z. That is, Z is an integer equal to or greater than 2, and z is an integer equal to or greater than 1 and equal to or less than Z.

[0220] If the waveform of each period contained in the time-series biological information regarding heartbeats contained in the y-th piece of learning source data is Wave(y, z) and the cardiac status information contained in the y-th piece of learning source data is Info(y), then the y-th piece of learning source data is [Wave(y, 1), Wave(y, 2), ..., Wave(y, Z), Info(y)].

[0221] The signal analysis unit 310 obtains one or more myocardial activity parameters Para(y,z) from each Wave(y,z) for each y of 1, ..., Y and each z of 1, ..., Z, thereby obtaining a myocardial activity parameter set [Para(y,1), Para(y,2), ..., Para(y,Z)] for each y of 1, ..., Y. Each myocardial activity parameter Para(y,z) is one or more myocardial activity parameters obtained by processing similar to that performed by the signal analysis device 1 of the first embodiment from the waveform Wave(y,z) of each cycle included in the time-series biological information related to the heartbeat, and therefore has one or more elements.

[0222] Since the learning unit 320 uses a set of myocardial activity parameter sets and cardiac state information based on the same learning source data as learning data, an example of the y-th learning data is [Para(y, 1), Para(y, 2), ..., Para(y, Z), Info(y)]. That is, the learning data set is [[Para(1, 1), Para(1, 2), ..., Para(1, Z), Info(1)], [Para(2, 1), Para(2, 2), ..., Para(2, Z), Info(2)], ..., [Para(Y, 1), Para(Y, 2), ..., Para(Y, Z), Info(Y)]. The learning unit 320 uses [Para(1, 1), Para(1, 2), ..., Para(1, Z), Info(1)], [Para(2, 1), Para(2, 2), ..., Para(2, Z), Info(2)], ..., [Para(Y, 1), Para(Y, 2), ..., Para(Y, Z), Info(Y)] to learn an estimation model that receives a myocardial activity parameter set as input and obtains cardiac state information corresponding to the myocardial activity parameter set, and outputs the learned estimation model.

[0223] Next, the information providing device 400 will be described. [[Information providing device 400]] The information providing device 400 receives input of time-series biological information relating to the pulsation of a heart to be provided with information (hereinafter referred to as the "information providing target heart"). The information providing device 400 acquires myocardial activity parameters from the time-series biological information relating to the pulsation of the information providing target heart, and uses a trained estimation model to input the acquired myocardial activity parameters and obtain and output cardiac status information, which is information representing the status of the heart corresponding to the myocardial activity parameters. As illustrated in FIG. 60, the information providing device 400 includes a signal analysis unit 410 and a status information generation unit 420. The information providing device 400 performs the processes of steps S410 and S420 illustrated in FIG. 62.

[0224] [Signal analysis section 410] The signal analysis unit 410 receives time-series biological information relating to the pulsation of the information-providing target heart, which has been input to the information providing device 400. The signal analysis unit 410 performs the same processing as the signal analysis unit 310 of the learning device 300, i.e., the same processing as the signal analysis device 1 of the first embodiment, to acquire a myocardial activity parameter set from the time-series biological information relating to the pulsation of the information-providing target heart (step S410). The myocardial activity parameter set acquired by the signal analysis unit 410 is output from the signal analysis unit 410 and input to the state information generation unit 420. The myocardial activity parameter set acquired by the signal analysis unit 410 is a set of one or more types of myocardial activity parameters described in the first embodiment. The one or more types of myocardial activity parameters are myocardial activity parameters of a type preselected as myocardial activity parameters to be used by the state information generation unit 420 (described later), and are myocardial activity parameters of a type used by the learning unit 320 of the second embodiment to train an estimation model.

[0225] When the input time-series biological information regarding the pulsation of the information-provided heart is a waveform for one cycle, the signal analysis unit 410 acquires a set of one or more of the above-mentioned myocardial activity parameters from the waveform for one cycle as a myocardial activity parameter set. When the input time-series biological information regarding the pulsation of the information-provided heart is a waveform for multiple cycles, the signal analysis unit 410 acquires a set of one or more of the above-mentioned myocardial activity parameters from the waveform for each cycle, and acquires a set of one or more of the myocardial activity parameters for multiple cycles as a myocardial activity parameter set.

[0226] [Status Information Generator 420] As shown in Fig. 60, the state information generating unit 420 includes a model storage unit 425. The model storage unit 425 stores in advance a trained estimation model output by the learning device 300 of the second embodiment. The trained estimation model stored in advance in the model storage unit 425 is an estimation model that receives a myocardial activity parameter set as input and obtains cardiac state information, which is information representing the state of the heart corresponding to the myocardial activity parameter set.

[0227] The myocardial activity parameter set of the information provision target heart output by the signal analysis unit 410 is input to the state information generation unit 420. Using a trained estimation model pre-stored in the model storage unit 425, the state information generation unit 420 receives the myocardial activity parameter set of the information provision target heart as input and obtains cardiac state information representing the state of the information provision target heart (step S420). The cardiac state information obtained by the state information generation unit 420 is output from the state information generation unit 420 and becomes the output of the information provision device 400.

[0228] [First Example of Information Providing Device 400] The data and operations of the information providing device 400 will be outlined below, taking as a first example a case where time-series biological information relating to the heartbeat of the information providing target input to the information providing device 400 is a waveform for one cycle.

[0229] If the time-series biological information relating to the pulsation of the information-provided heart input to the information providing device 400 is WAVE, the signal analyzing unit 410 obtains a myocardial activity parameter set PARA from the time-series biological information WAVE relating to the pulsation of the information-provided heart. The myocardial activity parameter set PARA is a set of one or more myocardial activity parameters obtained from the time-series biological information WAVE relating to the pulsation of the information-provided heart by the same processing as that performed by the signal analyzing device 1 of the first embodiment, and therefore has one or more elements.

[0230] The state information generating unit 420 uses a trained estimation model stored in advance in the model storage unit 425 to obtain, from the myocardial activity parameter set PARA, one piece of cardiac state information INFO corresponding to the myocardial activity parameter set PARA.

[0231] [Second Example of Information Providing Device 400] An overview of each data and operation of the information providing device 400 will be described using as a second example a case where time-series biological information relating to the pulsation of the information providing object heart input to the information providing device 400 is a waveform for multiple cycles. In the second example, it is assumed that the time-series biological information relating to the pulsation of the information providing object heart is a waveform for Z cycles, and each of the cycle indices 1, ..., Z is z. That is, Z is an integer equal to or greater than 2, and z is an integer equal to or greater than 1 and equal to or less than Z.

[0232] If the waveform of each period included in the time-series biological information on the pulsation of the information-provided heart input to the information providing device 400 is WAVE(z), the signal analysis unit 410 obtains one or more myocardial activity parameters PARA(z) from each WAVE(z) for each z of 1, ..., Z, thereby obtaining a myocardial activity parameter set [Para(1), Para(2), ..., Para(Z)]. The myocardial activity parameters PARA(z) are one or more myocardial activity parameters obtained by processing similar to that performed by the signal analysis device 1 of the first embodiment from the waveform WAVE(z) of each period included in the time-series biological information on the pulsation of the information-provided heart, and therefore have one or more elements.

[0233] The status information generating unit 420 obtains one piece of cardiac status information INFO corresponding to the myocardial activity parameter set [Para(1), Para(2), ..., Para(Z)] from the myocardial activity parameter set [Para(1), Para(2), ..., Para(Z)] using a trained estimation model pre-stored in the model storage unit 425.

[0234] The cardiac status information obtained by the status information generating unit 420 is an estimation result of the status of the information provision target heart by the information providing device 400. That is, for example, the cardiac status information obtained by the status information generating unit 420 represents an estimation result as to whether the information provision target heart is in a normal state or has some disease using a specific numerical value assigned in advance, such as "0" corresponding to the information provision target heart being in a normal state, "1" corresponding to Brugada syndrome, "2" corresponding to ischemic heart disease, "3" corresponding to early repolarization syndrome, and so on.

[0235] Alternatively, for example, the cardiac status information obtained by the status information generating unit 420 represents the estimated result of whether the rhythm of the heart to be provided with information is normal or what kind of arrhythmia it is in using a specific numerical value assigned in advance, such as "0" corresponding to the heart to be provided with information being in normal sinus rhythm, "1" corresponding to supraventricular extrasystole, "2" corresponding to atrial fibrillation, "3" corresponding to ventricular extrasystole, "4" corresponding to ventricular fibrillation, etc.

[0236] Alternatively, for example, the cardiac status information obtained by the status information generating unit 420 represents the estimated results of whether the blood flow status of the heart to be provided with information is normal or ischemic, and where the ischemic area is, using pre-assigned specific numerical values, such as "0" corresponding to the heart to be provided with information being in a normal coronary artery blood flow state, "1" corresponding to the heart being in an ischemic state in the endomyosarcoma, "2" corresponding to the heart being in an ischemic state in the endomyosarcoma and outer myocardium (transmural ischemic state), "3" corresponding to myocardial infarction of the anterior wall, "4" corresponding to myocardial infarction of the inferior wall, etc.

[0237] Third Embodiment In the second embodiment, an estimation model is trained and cardiac state information is estimated after a myocardial activity parameter set is obtained from time-series biological information related to the beating of the heart, but an estimation model may be trained and cardiac state information may be estimated using a myocardial activity parameter set obtained in advance. This embodiment will be referred to as the third embodiment, and the differences from the second embodiment will be mainly described below.

[0238] 63, the information providing system 201 of the third embodiment includes a learning device 301 and an information providing device 401. The hardware configurations of the information providing system 201, the learning device 301, and the information providing device 401 are similar to, for example, the hardware configuration of the signal analyzing device 1 shown in FIG.

[0239] First, the learning device 301 will be described. [[Learning device 301]] As shown in Fig. 63, the learning device 301 includes a learning unit 320. The learning device 301 performs the process of step S320 shown in Fig. 64.

[0240] [Training dataset, training data] A training data set is input to the learning device 300. The training data set includes a plurality of pieces of training data. Each piece of training data includes at least a myocardial activity parameter set of a target heart, which is the heart from which the training data was acquired, and cardiac state information, which is information representing the state of the target heart. Each piece of training data may include information for identifying the training data, such as an identification number uniquely assigned to the training data.

[0241] If the number of training data included in the training data set is Y and y is an integer between 1 and Y, the Y-th training data includes at least a myocardial activity parameter set of the target heart from which the y-th training data is obtained and cardiac status information which is information representing the status of the target heart from which the y-th training data is obtained.

[0242] The myocardial activity parameter set of the target heart is a myocardial activity parameter set obtained from time-series biological information regarding the pulsation of the target heart by the same processing as that performed by the signal analysis unit 310 of the learning device 300 of the second embodiment, that is, by the same processing as that performed by the signal analysis device 1 of the first embodiment. The cardiac state information of the target heart is the same as the cardiac state information of the target heart in the second embodiment.

[0243] [Learning Section 320] The learning unit 320 receives the learning data set input to the learning device 301. That is, the learning unit 320 receives the myocardial activity parameter sets included in each piece of learning data included in the learning data set input to the learning device 301 and the cardiac state information included in each piece of learning data included in the learning data set input to the learning device 301. The learning unit 320 uses a pair of myocardial activity parameter sets and cardiac state information included in the same learning data as learning data. That is, in each piece of learning data, the myocardial activity parameter set and the cardiac state information are based on the same time-series biological information related to cardiac beats. In other words, the myocardial activity parameter set is a myocardial activity parameter set corresponding to the cardiac state information, and the cardiac state information is cardiac state information corresponding to the myocardial activity parameter set.

[0244] Similar to the learning unit 320 of the second embodiment, the learning unit 320 uses a learning dataset, more specifically, a set of myocardial activity parameter sets included in each learning data set included in the learning dataset and cardiac state information corresponding to the myocardial activity parameter sets, to learn an estimation model that uses the myocardial activity parameter sets as input to obtain cardiac state information corresponding to the myocardial activity parameter sets, thereby obtaining a trained estimation model (step S320). The trained estimation model obtained by the learning unit 320 is output from the learning unit 320 and becomes the output of the learning device 300. Well-known learning techniques may be used to train the estimation model, and the number of training data may be sufficient to train the estimation model. A specific example of the learning unit 320 is as described in the second embodiment.

[0245] Next, the information providing device 401 will be described. [[Information providing device 401]] The information providing device 401 receives an input of a myocardial activity parameter set of an information providing target heart, which is a heart that is a target of information provision. The myocardial activity parameter set of the information providing target heart is a myocardial activity parameter set obtained from time-series biological information related to the pulsation of the information providing target heart by the same processing as that performed by the signal analysis unit 410 of the information providing device 400 of the second embodiment, i.e., the same processing as that performed by the signal analysis device 1 of the first embodiment. The information providing device 401 obtains and outputs cardiac status information, which is information representing the status of the heart corresponding to the myocardial activity parameter set, from the input myocardial activity parameter set using a trained estimation model. The information providing device 401 includes a status information generating unit 420, as illustrated in FIG. 63. The information providing device 401 performs the processing of step S420 illustrated in FIG. 65.

[0246] [Status Information Generator 420] As shown in Fig. 63, the state information generating unit 420 includes a model storage unit 425. The model storage unit 425 stores in advance a trained estimation model output by the learning device 301 of the third embodiment. The trained estimation model stored in advance in the model storage unit 425 is an estimation model that receives a myocardial activity parameter set as input and obtains cardiac state information, which is information representing the state of the heart corresponding to the myocardial activity parameter set.

[0247] The state information generating unit 420 receives the myocardial activity parameter set of the information target heart input to the information providing device 401. Using a trained estimation model pre-stored in the model storage unit 425, the state information generating unit 420 receives the myocardial activity parameter set of the information target heart as input and obtains cardiac state information representing the state of the information target heart (step S420). The cardiac state information obtained by the state information generating unit 420 is output from the state information generating unit 420 and becomes the output of the information providing device 401. A specific example of the state information generating unit 420 is as described in the second embodiment. The cardiac state information obtained by the state information generating unit 420 is the same as the cardiac state information obtained by the state information generating unit 420 in the second embodiment and is an estimation result by the information providing device 401 regarding a state representing the state of the information target heart, a specific example of which is as described in the second embodiment.

[0248] <Modifications of the second and third embodiments> As can be seen from the description of the third embodiment, the estimation model obtained by the learning device 300 of the second embodiment and the estimation model obtained by the learning device 301 of the third embodiment are equivalent. Therefore, the trained estimation model output by the learning device 301 of the third embodiment may be stored in advance in the model storage unit 425 of the information providing device 400 of the second embodiment, or the trained estimation model output by the learning device 300 of the learning device of the second embodiment may be stored in advance in the model storage unit 425 of the information providing device 401 of the third embodiment. That is, the information providing system may be configured like the information providing system 202 including the learning device 301 of the third embodiment and the information providing device 400 of the second embodiment, as exemplified in FIG. 66, or may be configured like the information providing system 203 including the learning device 300 of the second embodiment and the information providing device 401 of the third embodiment, as exemplified in FIG. 67. The hardware configurations of the information providing system 202, the information providing system 203, the learning device 300, the learning device 301, the information providing device 400, and the information providing device 401 are similar to the hardware configuration of the signal analyzing device 1 shown in FIG. 1, for example.

[0249] <Summary of the Second and Third Embodiments and Their Modifications> [Learning device] The learning device 300 of the second embodiment and the learning device 301 of the third embodiment both include a learning unit 320 that uses a training data set consisting of Y (Y is plural) pieces of training data, each of which includes a myocardial activity parameter set of the heart that is the subject of the y-th training data and cardiac state information that represents the state of the heart that is the subject of the y-th training data, where y is an integer between 1 and Y, to train an estimation model that receives the myocardial activity parameter set as input and obtains cardiac state information that represents the state of the heart corresponding to the myocardial activity parameter set.The learning device 300 of the second embodiment further includes a signal analysis unit 310 that receives time-series biological information related to the heart beating that is the subject of each y-th training data and obtains a myocardial activity parameter set. However, the myocardial activity parameter set of the heart that is the subject of the y-th learning data in the learning device 300 of the second embodiment and the learning device 301 of the third embodiment includes one or more predetermined types of myocardial activity parameters from among multiple types of myocardial activity parameters included in the first group described below, multiple types of myocardial activity parameters included in the second group described below, multiple types of myocardial activity parameters included in the third group described below, and one or more types of myocardial activity parameters included in the fourth group described below, for each period of the time-series biological information related to the heart beating that is the subject of each y-th learning data.

[0250] [Information providing device] Both the information providing device 400 of the second embodiment and the information providing device 401 of the third embodiment include a state information generating unit 420 that stores in advance an estimation model for receiving a myocardial activity parameter set as input and obtaining cardiac state information representing the state of the heart corresponding to the myocardial activity parameter set, and that uses the estimation model to receive a myocardial activity parameter set of an information providing target heart as input and obtains cardiac state information representing the state of the information providing target heart.The information providing device 400 of the second embodiment further includes a signal analyzing unit 410 that receives time-series biological information related to the beating of the information providing target heart as input and obtains a myocardial activity parameter set. However, the myocardial activity parameter set of the information-receiving heart in the information providing device 400 of the second embodiment and the information providing device 401 of the third embodiment includes one or more predetermined types of myocardial activity parameters from among multiple types of myocardial activity parameters included in the first group described below, multiple types of myocardial activity parameters included in the second group described below, multiple types of myocardial activity parameters included in the third group described below, and one or more types of myocardial activity parameters included in the fourth group described below, for each cycle of the time-series biological information regarding the beating of the information-receiving heart.

[0251] [Multiple myocardial activity parameters included in Group 1] The multiple types of myocardial activity parameters included in the first group refer to a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and a first level value when the first target time waveform is approximated by a first approximated time waveform which is a time waveform resulting from the difference or weighted difference between a first cumulative distribution function which is a cumulative distribution function of a first unimodal distribution and a second cumulative distribution function which is a cumulative distribution function of a second unimodal distribution, when the waveform of the time interval of an R wave included in one cycle of the waveform of time-series biological information related to cardiac beating is used as the first target time waveform.

[0252] [Multiple myocardial activity parameters included in Group 2] The multiple types of myocardial activity parameters included in the second group are a second approximate inverse time waveform that is a waveform resulting from the difference or weighted difference between a third cumulative distribution function that is a cumulative distribution function of a third unimodal distribution and a fourth cumulative distribution function that is a cumulative distribution function of a fourth unimodal distribution, when a waveform in the time interval of a T wave included in one cycle of the waveform of time-series biological information related to cardiac pulsation is defined as a second target time waveform and a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverse time waveform, or a third cumulative distribution function that is a cumulative distribution function of a third unimodal distribution and When the second target inverse-time waveform is approximated by the second approximate inverse-time waveform, which is a waveform obtained by adding a second level value to the difference or weighted difference with the fourth cumulative distribution function, which is the cumulative distribution function of the fourth unimodal distribution, the parameters specifying the third unimodal distribution, the parameters specifying the third cumulative distribution function, the parameters specifying the fourth unimodal distribution, the parameters specifying the fourth cumulative distribution function, the weight of the third cumulative distribution function, the weight of the fourth cumulative distribution function, the ratio of the weight of the third cumulative distribution function to the weight of the fourth cumulative distribution function, and the second level value.

[0253] Alternatively, the multiple types of myocardial activity parameters included in the second group are a time waveform resulting from the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, where the waveform of the time interval of the T wave included in one cycle of the waveform of time-series biological information related to the beating of the heart is defined as the second target time waveform, the cumulative distribution function of the third unimodal distribution is defined as the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is defined as the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is defined as the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is defined as the fourth inverse cumulative distribution function. The parameters specifying the third unimodal distribution, the parameter specifying the third cumulative distribution function, the parameter specifying the fourth unimodal distribution, the parameter specifying the fourth cumulative distribution function, the weight of the third inverse cumulative distribution function, the weight of the fourth inverse cumulative distribution function, the ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value when the second target time waveform is approximated by the second approximate time waveform, or the second approximate time waveform which is a time waveform obtained by adding the second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function.

[0254] [Multiple myocardial activity parameters included in Group 3] The multiple types of myocardial activity parameters included in the third group are the parameters that specify the fifth unimodal distribution, the parameters that specify the fifth cumulative distribution function, and the weight of the fifth cumulative distribution function when the residual time waveform is approximated by a fifth cumulative distribution function, which is the cumulative distribution function of a fifth unimodal distribution, or an approximated residual time waveform, which is a time waveform obtained by multiplying the fifth cumulative distribution function by a weight, using any of the following as the residual time waveform: the time waveform of the difference between the first target time waveform and the first approximated time waveform, the time waveform of the difference between the second target time waveform and the second approximated inverse time waveform, or the time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximated inverse time waveform.

[0255] Alternatively, the multiple types of myocardial activity parameters included in the third group are, when at least one of the time waveform of the difference between the first target time waveform and the first approximated time waveform, the time waveform of the difference between the second target time waveform and the second approximated time waveform, and the time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximated inverse time waveform is used as a residual time waveform, and the residual time waveform is approximated by an approximated residual time waveform, which is a time waveform resulting from the difference or weighted difference between a fifth cumulative distribution function, which is the cumulative distribution function of a fifth unimodal distribution, and a sixth cumulative distribution function, which is the cumulative distribution function of a sixth unimodal distribution, the following are obtained: a parameter specifying the fifth unimodal distribution, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a weight of the fifth cumulative distribution function, a weight of the sixth cumulative distribution function, and a ratio of the weight of the fifth cumulative distribution function to the weight of the sixth cumulative distribution function.

[0256] [One or more myocardial activity parameters included in Group 4] The one or more myocardial activity parameters included in the fourth group are myocardial activity parameters obtained by calculating multiple types of myocardial activity parameters from among the multiple types of myocardial activity parameters included in the first group, the multiple types of myocardial activity parameters included in the second group, and the multiple types of myocardial activity parameters included in the third group.

[0257] All or part of the functions of the signal analysis device 1 and / or the information provision systems 200, 201, 202, 203 and / or the learning devices 300, 301 and / or the information provision devices 400, 401 may be realized using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The program may be recorded on a computer-readable recording medium. Examples of computer-readable recording media include portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, and storage devices such as hard disks built into computer systems. The program may be transmitted via a telecommunications line.

[0258] Although an embodiment of the present invention has been described above in detail with reference to the drawings, the specific configuration is not limited to this embodiment, and includes designs within the scope of the gist of the present invention. [Explanation of symbols]

[0259] 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, 200, 201, 202, 203...information provision system, 300, 301...learning device, 310...signal analysis unit, 320...learning unit, 400, 401...information provision device, 410...signal analysis unit, 420...state information generation unit, 425...model storage unit

Claims

1. a waveform of a time interval of an R wave included in one waveform representing a cardiac cycle of the heart is defined as a first target time waveform; a waveform of a time interval of a T wave included in the waveform is set as a second target time waveform; a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverted time waveform; The cumulative distribution function of the first unimodal distribution is the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution is the second cumulative distribution function, a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value when the first target time waveform is approximated by a first approximate time waveform which is a time waveform obtained by adding a first level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, The cumulative distribution function of the third unimodal distribution is the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is the fourth inverse cumulative distribution function, a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio of the weight of the third cumulative distribution function to the weight of the fourth cumulative distribution function, and the second level value, when the second target inverse-time waveform is approximated by a second approximate inverse-time waveform which is a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, or a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value, when the second target time waveform is approximated by a second approximate time waveform which is a time waveform resulting from the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a second approximate time waveform which is a time waveform resulting from adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function; are multiple types of myocardial activity parameters included in the second group, a residual time waveform is any one of a time waveform of the difference between the first target time waveform and the first approximate time waveform, a time waveform of the difference between the second target time waveform and the second approximate time waveform, and a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximate inverse time waveform; a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function; and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function; an approximated residual time waveform, which is a time waveform obtained by approximating the fifth cumulative distribution function or the fifth cumulative distribution function multiplied by a weight, a parameter specifying the fifth unimodal distribution when the residual time waveform is approximated, a parameter specifying the fifth cumulative distribution function, and a weight of the fifth cumulative distribution function; or a parameter specifying the fifth unimodal distribution, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, a weight of the fifth cumulative distribution function, a weight of the sixth cumulative distribution function, and a ratio of the weight of the fifth cumulative distribution function to the weight of the sixth cumulative distribution function, when the residual time waveform is approximated by an approximate residual time waveform which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function; are multiple types of myocardial activity parameters included in the third group, a parameter obtained by calculating a plurality of myocardial activity parameters among the plurality of myocardial activity parameters included in the first group, the plurality of myocardial activity parameters included in the second group, and the plurality of myocardial activity parameters included in the third group is set as one or more myocardial activity parameters included in a fourth group; a set of one or more predetermined myocardial activity parameters from among a plurality of types of myocardial activity parameters included in the first group, a plurality of types of myocardial activity parameters included in the second group, a plurality of types of myocardial activity parameters included in the third group, and one or more types of myocardial activity parameters included in the fourth group, which are obtained from one or more waveforms indicating a cardiac cycle of the heart, is defined as a myocardial activity parameter set; The training dataset consists of Y pieces of training data (Y is multiple), Each of the Y pieces of learning data includes a myocardial activity parameter set of the heart that is the subject of the y-th piece of learning data, and cardiac state information that is information representing the state of the heart that is the subject of the y-th piece of learning data, where y is an integer that is equal to or greater than 1 and equal to or less than Y, a learning unit that uses the learning data set to learn an estimation model that receives a myocardial activity parameter set as an input and obtains cardiac state information that is information representing a cardiac state corresponding to the myocardial activity parameter set; Learning device.

2. a signal analysis unit that acquires the myocardial activity parameter set from each of waveforms indicating one or more cardiac cycles of the heart that are the subject of the Y pieces of learning data, The learning device according to claim 1 .

3. each said unimodal distribution is a Gaussian distribution; The learning device according to claim 1 or 2.

4. a waveform of a time interval of an R wave included in one waveform representing a cardiac cycle of the heart is defined as a first target time waveform; a waveform of a time interval of a T wave included in the waveform is set as a second target time waveform; a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverted time waveform; The cumulative distribution function of the first unimodal distribution is the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution is the second cumulative distribution function, a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value when the first target time waveform is approximated by a first approximate time waveform which is a time waveform obtained by adding a first level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, The cumulative distribution function of the third unimodal distribution is the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is the fourth inverse cumulative distribution function, a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio of the weight of the third cumulative distribution function to the weight of the fourth cumulative distribution function, and the second level value, when the second target inverse-time waveform is approximated by a second approximate inverse-time waveform which is a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, or a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value, when the second target time waveform is approximated by a second approximate time waveform which is a time waveform resulting from the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a second approximate time waveform which is a time waveform resulting from adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function; are multiple types of myocardial activity parameters included in the second group, a residual time waveform is any one of a time waveform of the difference between the first target time waveform and the first approximate time waveform, a time waveform of the difference between the second target time waveform and the second approximate time waveform, and a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximate inverse time waveform; a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function; and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function; an approximated residual time waveform, which is a time waveform obtained by approximating the fifth cumulative distribution function or the fifth cumulative distribution function multiplied by a weight, a parameter specifying the fifth unimodal distribution when the residual time waveform is approximated, a parameter specifying the fifth cumulative distribution function, and a weight of the fifth cumulative distribution function; or a parameter specifying the fifth unimodal distribution, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, a weight of the fifth cumulative distribution function, a weight of the sixth cumulative distribution function, and a ratio of the weight of the fifth cumulative distribution function to the weight of the sixth cumulative distribution function, when the residual time waveform is approximated by an approximate residual time waveform which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function; are multiple types of myocardial activity parameters included in the third group, a parameter obtained by calculating a plurality of myocardial activity parameters among the plurality of myocardial activity parameters included in the first group, the plurality of myocardial activity parameters included in the second group, and the plurality of myocardial activity parameters included in the third group is set as one or more myocardial activity parameters included in a fourth group; a set of one or more predetermined myocardial activity parameters from among a plurality of types of myocardial activity parameters included in the first group, a plurality of types of myocardial activity parameters included in the second group, a plurality of types of myocardial activity parameters included in the third group, and one or more types of myocardial activity parameters included in the fourth group, which are obtained from one or more waveforms indicating a cardiac cycle of the heart, is defined as a myocardial activity parameter set; an estimation model is stored in advance, which receives a myocardial activity parameter set as an input and obtains cardiac state information, which is information representing a cardiac state corresponding to the myocardial activity parameter set; a state information generating unit that uses the estimation model to input a myocardial activity parameter set of an information provision target heart, which is a heart that is a target of information provision, and obtains cardiac state information of the information provision target heart, Information provision device.

5. a signal analysis unit that acquires the myocardial activity parameter set from a waveform that indicates one or more cardiac cycles of the information-provided heart; 5. The information providing device according to claim 4.

6. each said unimodal distribution is a Gaussian distribution; 6. The information providing device according to claim 4 or 5.

7. A learning method executed by a learning device, a waveform of a time interval of an R wave included in one waveform representing a cardiac cycle of the heart is defined as a first target time waveform; a waveform of a time interval of a T wave included in the waveform is set as a second target time waveform; a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverted time waveform; The cumulative distribution function of the first unimodal distribution is the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution is the second cumulative distribution function, a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value when the first target time waveform is approximated by a first approximate time waveform which is a time waveform obtained by adding a first level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, The cumulative distribution function of the third unimodal distribution is the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is the fourth inverse cumulative distribution function, a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio of the weight of the third cumulative distribution function to the weight of the fourth cumulative distribution function, and the second level value, when the second target inverse-time waveform is approximated by a second approximate inverse-time waveform which is a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, or a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value, when the second target time waveform is approximated by a second approximate time waveform which is a time waveform resulting from the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a second approximate time waveform which is a time waveform resulting from adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function; are multiple types of myocardial activity parameters included in the second group, a residual time waveform is any one of a time waveform of the difference between the first target time waveform and the first approximate time waveform, a time waveform of the difference between the second target time waveform and the second approximate time waveform, and a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximate inverse time waveform; a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function; and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function; an approximated residual time waveform, which is a time waveform obtained by approximating the fifth cumulative distribution function or the fifth cumulative distribution function multiplied by a weight, a parameter specifying the fifth unimodal distribution when the residual time waveform is approximated, a parameter specifying the fifth cumulative distribution function, and a weight of the fifth cumulative distribution function; or a parameter specifying the fifth unimodal distribution, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, a weight of the fifth cumulative distribution function, a weight of the sixth cumulative distribution function, and a ratio of the weight of the fifth cumulative distribution function to the weight of the sixth cumulative distribution function, when the residual time waveform is approximated by an approximate residual time waveform which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function; are multiple types of myocardial activity parameters included in the third group, a parameter obtained by calculating a plurality of myocardial activity parameters among the plurality of myocardial activity parameters included in the first group, the plurality of myocardial activity parameters included in the second group, and the plurality of myocardial activity parameters included in the third group is set as one or more myocardial activity parameters included in a fourth group; a set of one or more predetermined myocardial activity parameters from among a plurality of types of myocardial activity parameters included in the first group, a plurality of types of myocardial activity parameters included in the second group, a plurality of types of myocardial activity parameters included in the third group, and one or more types of myocardial activity parameters included in the fourth group, which are obtained from one or more waveforms indicating a cardiac cycle of the heart, is defined as a myocardial activity parameter set; The training dataset consists of Y pieces of training data (Y is multiple), Each of the Y pieces of learning data includes a myocardial activity parameter set of the heart that is the subject of the y-th piece of learning data, and cardiac state information that is information representing the state of the heart that is the subject of the y-th piece of learning data, where y is an integer that is equal to or greater than 1 and equal to or less than Y, a learning step of learning an estimation model that uses the learning data set to input a myocardial activity parameter set and obtains cardiac state information that is information representing a cardiac state corresponding to the myocardial activity parameter set; How to learn.

8. An information providing method executed by an information providing device, a waveform of a time interval of an R wave included in one waveform representing a cardiac cycle of the heart is defined as a first target time waveform; a waveform of a time interval of a T wave included in the waveform is set as a second target time waveform; a waveform obtained by reversing the time axis of the second target time waveform is defined as a second target inverted time waveform; The cumulative distribution function of the first unimodal distribution is the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution is the second cumulative distribution function, a parameter specifying the first unimodal distribution, a parameter specifying the first cumulative distribution function, a parameter specifying the second unimodal distribution, a parameter specifying the second cumulative distribution function, a weight of the first cumulative distribution function, a weight of the second cumulative distribution function, a ratio of the weight of the first cumulative distribution function to the weight of the second cumulative distribution function, and the first level value when the first target time waveform is approximated by a first approximate time waveform which is a time waveform obtained by adding a first level value to the difference or weighted difference between the first cumulative distribution function and the second cumulative distribution function, The cumulative distribution function of the third unimodal distribution is the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution is the fourth cumulative distribution function, the function obtained by subtracting the third cumulative distribution function from 1 is the third inverse cumulative distribution function, and the function obtained by subtracting the fourth cumulative distribution function from 1 is the fourth inverse cumulative distribution function, a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third cumulative distribution function, a weight of the fourth cumulative distribution function, a ratio of the weight of the third cumulative distribution function to the weight of the fourth cumulative distribution function, and the second level value, when the second target inverse-time waveform is approximated by a second approximate inverse-time waveform which is a waveform obtained by adding a second level value to the difference or weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, or a parameter specifying the third unimodal distribution, a parameter specifying the third cumulative distribution function, a parameter specifying the fourth unimodal distribution, a parameter specifying the fourth cumulative distribution function, a weight of the third inverse cumulative distribution function, a weight of the fourth inverse cumulative distribution function, a ratio of the weight of the third inverse cumulative distribution function to the weight of the fourth inverse cumulative distribution function, and the second level value, when the second target time waveform is approximated by a second approximate time waveform which is a time waveform resulting from the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a second approximate time waveform which is a time waveform resulting from adding a second level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function; are multiple types of myocardial activity parameters included in the second group, a residual time waveform is any one of a time waveform of the difference between the first target time waveform and the first approximate time waveform, a time waveform of the difference between the second target time waveform and the second approximate time waveform, and a time waveform obtained by reversing the time axis of the waveform of the difference between the second target inverse time waveform and the second approximate inverse time waveform; a cumulative distribution function of a fifth unimodal distribution is a fifth cumulative distribution function; and a cumulative distribution function of a sixth unimodal distribution is a sixth cumulative distribution function; an approximated residual time waveform, which is a time waveform obtained by approximating the fifth cumulative distribution function or the fifth cumulative distribution function multiplied by a weight, a parameter specifying the fifth unimodal distribution when the residual time waveform is approximated, a parameter specifying the fifth cumulative distribution function, and a weight of the fifth cumulative distribution function; or a parameter specifying the fifth unimodal distribution, a parameter specifying the fifth cumulative distribution function, a parameter specifying the sixth unimodal distribution, a parameter specifying the sixth cumulative distribution function, a weight of the fifth cumulative distribution function, a weight of the sixth cumulative distribution function, and a ratio of the weight of the fifth cumulative distribution function to the weight of the sixth cumulative distribution function, when the residual time waveform is approximated by an approximate residual time waveform which is a time waveform based on the difference or weighted difference between the fifth cumulative distribution function and the sixth cumulative distribution function; are multiple types of myocardial activity parameters included in the third group, a parameter obtained by calculating a plurality of myocardial activity parameters among the plurality of myocardial activity parameters included in the first group, the plurality of myocardial activity parameters included in the second group, and the plurality of myocardial activity parameters included in the third group is set as one or more myocardial activity parameters included in a fourth group; a set of one or more predetermined myocardial activity parameters from among a plurality of types of myocardial activity parameters included in the first group, a plurality of types of myocardial activity parameters included in the second group, a plurality of types of myocardial activity parameters included in the third group, and one or more types of myocardial activity parameters included in the fourth group, which are obtained from one or more waveforms indicating a cardiac cycle of the heart, is defined as a myocardial activity parameter set; an estimation model is stored in advance, which receives a myocardial activity parameter set as an input and obtains cardiac state information, which is information representing a cardiac state corresponding to the myocardial activity parameter set; a state information generating step of obtaining cardiac state information of an information provision target heart by using the estimation model and inputting a myocardial activity parameter set of the information provision target heart, the heart being a target of information provision; Information provision method.

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

10. 6. A program for causing a computer to function as the information providing device according to claim 4 or 5.

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