Signal synthesis device, signal synthesis method, and program

Abstract

Provided is a signal compositing device including a waveform compositing unit that: obtains an R wave composite waveform, represented by a function obtained in a manner such that a function, which is obtained by multiplying a function specified by the parameter value of a parameter specifying a second unimodal distribution by the parameter value of a second weighting parameter, is subtracted from a function, which is obtained by multiplying a function specified by the parameter value of a parameter specifying a first unimodal distribution by the parameter value of a first weight parameter, and the parameter values of first level parameters are added thereto; obtains a T wave composite waveform with the time axis of the waveform being reversed, represented by a function obtained in a manner such that a function, which is obtained by multiplying a function specified by the parameter value of a parameter specifying a fourth unimodal distribution by the parameter value of a fourth weighting parameter, is subtracted from a function, which is obtained by multiplying a function specified by the parameter value of a parameter specifying a third unimodal distribution by the parameter value of a third weight parameter, and the parameter values of second level parameters are added thereto; and connects the R wave composite waveform and the T wave composite waveform.

Description

[Technical Field]

[0001] The present invention relates to a signal synthesis device, a signal synthesis 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 acquired from a living body may not always be 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 acquired from the living body. For example, in the case of heart failure, the onset can be suppressed by observing 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 techniques, 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 essentially based solely 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 first target time waveform is a waveform of a time interval of an R wave included in a waveform for one cycle indicating a cardiac cycle of the heart, a second target time waveform is a waveform of a time interval of a T wave included in the first target time waveform, a waveform obtained by reversing the time axis of the second target time waveform is a second target inverted time waveform, and a first approximated time waveform is a time waveform obtained by subtracting a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter, and adding the result to the first approximated time waveform, and the first approximated time waveform is a time waveform obtained by approximating the first target time waveform, the value of a parameter specifying the first unimodal distribution or the value of the first cumulative distribution function, the value of a parameter specifying the second unimodal distribution or the value of the second cumulative distribution function, the value of the first weighting parameter, and the second weighting parameter. When the second target inverse-time waveform is approximated by a second approximated inverse-time waveform, which is a waveform obtained by subtracting a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weighting parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a third weighting parameter, and adding the result to the second approximated inverse-time waveform, a set of a parameter value specifying the third unimodal distribution or a parameter value specifying the third cumulative distribution function, a parameter value specifying the fourth unimodal distribution or a parameter value specifying the fourth cumulative distribution function, a value of the third weighting parameter, a value of the fourth weighting parameter, and a value of the second-level parameter is defined as a myocardial activity parameter set, and each element value included in the myocardial activity parameter set is defined as a myocardial activity parameter value, and the myocardial activity parameter set is used to calculate the myocardial activity parameter value of a target heart (hereinafter referred to as the "target heart").) using each myocardial activity parameter value included in the myocardial activity parameter set of the first parameter, subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the second weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, to obtain a time waveform as a composite waveform for the time interval of an R wave, and a waveform synthesis unit that obtains a waveform obtained by reversing the time axis of a waveform obtained by subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter from a function obtained by multiplying the cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter, and adding the myocardial activity parameter value of the second level parameter to the function, and then connects the composite waveform of the R wave time period and the composite waveform of the T wave time period to obtain a composite signal for a waveform indicating the cardiac cycle of the target heart.

[0008] In one aspect of the present invention, a first target time waveform is a waveform of a time interval of an R wave included in a waveform for one cycle indicating a cardiac cycle of the heart, and a second target time waveform is a waveform of a time interval of a T wave included in the first target time waveform, and the first approximated time waveform is a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a second weighting parameter from a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter, and adding the result to the function, and the first approximated time waveform is a time waveform obtained by approximating the first target time waveform, the value of a parameter specifying the first unimodal distribution or the value of the first cumulative distribution function, the value of a parameter specifying the second unimodal distribution or the value of the second cumulative distribution function, the value of the first weighting parameter, the value of the second weighting parameter, the value of the first level parameter, and a third unimodal distribution. a second approximated time waveform, which is a time waveform obtained by subtracting from 1 a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weighting parameter from a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weighting parameter from a function obtained by subtracting from 1 a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weighting parameter, and adding the result to the second approximated time waveform; a myocardial activity parameter set is a set of a parameter value specifying the third unimodal distribution or a parameter value specifying the third cumulative distribution function, a parameter value specifying the fourth unimodal distribution or a parameter value specifying the fourth cumulative distribution function, the value of the third weighting parameter, the value of the fourth weighting parameter, and the value of the second level parameter; and each element value included in the myocardial activity parameter set is used as a myocardial activity parameter value to calculate a myocardial activity parameter value for a target heart (hereinafter referred to as the "target heart").) using each myocardial activity parameter value included in the myocardial activity parameter set of the first parameter, subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the second weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, to obtain a time waveform as a composite waveform for the time interval of an R wave, and a waveform synthesis unit that obtains a time waveform as a composite waveform for a T-wave time interval by a function obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter, from a function obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the third weighting parameter by the myocardial activity parameter value of the fourth weighting parameter, and adding the myocardial activity parameter value of the second level parameter to the time waveform obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the fourth weighting parameter by the myocardial activity parameter value of the fourth weighting parameter, and connecting the composite waveform for the R-wave time interval and the composite waveform for the T-wave time interval to obtain a composite signal for a waveform indicating the cardiac cycle of the target heart.

[0009] One aspect of the present invention is a signal synthesis method executed by a signal synthesis device, the method comprising: a first target time waveform being a waveform of a time interval of an R wave included in a waveform for one cycle representing a cardiac cycle of the heart; a second target time waveform being a waveform obtained by reversing the time axis of the second target time waveform; a second target inverted time waveform being a waveform obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter; a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter; and a first approximate time waveform being a time waveform obtained by subtracting a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a first weighting parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter, and adding the result to the first approximate time waveform; and a first approximation time waveform being a time waveform obtained by approximating the first target time waveform; a value of a parameter specifying the first unimodal distribution or a parameter specifying the first cumulative distribution function; a value of a parameter specifying the second unimodal distribution or a parameter specifying the second cumulative distribution function; and a value of the first weighting parameter. and a second approximated inverse-time waveform, which is a waveform obtained by subtracting a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of the fourth weight parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of the third weight parameter, and adding the result to the second-level parameter. When the second target inverse-time waveform is approximated by a second approximated inverse-time waveform, which is a waveform obtained by subtracting a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of the fourth weight parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of the third weight parameter, and adding the result to the second-level parameter, a set of a parameter value specifying the third unimodal distribution or a parameter value specifying the third cumulative distribution function, a parameter value specifying the fourth unimodal distribution or a parameter value specifying the fourth cumulative distribution function, a value of the third weight parameter, a value of the fourth weight parameter, and a value of the second-level parameter is defined as a myocardial activity parameter set, and each element value included in the myocardial activity parameter set is defined as a myocardial activity parameter value, and a target heart (referred to as the "target heart").), a time waveform obtained by subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the second weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function is obtained as a composite waveform for the time interval of an R wave, and a waveform synthesis step of obtaining a waveform obtained by reversing the time axis of a waveform obtained by subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter from a function obtained by multiplying the cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the fourth cumulative distribution function by the myocardial activity parameter value of the third weighting parameter, and adding the result to the myocardial activity parameter value of the second level parameter, and connecting the composite waveform of the R wave time period and the composite waveform of the T wave time period to obtain a composite signal for a waveform indicating the cardiac cycle of the target heart.

[0010] One aspect of the present invention is a signal synthesis method executed by a signal synthesis device, in which a waveform in a time interval of an R wave included in one cycle of a waveform indicating a cardiac cycle of the heart is defined as a first target time waveform, a waveform in a time interval of a T wave included in the first target time waveform is defined as a second target time waveform, and the first approximated time waveform is a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a second weighting parameter from a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter, and adding the result to the first approximated time waveform. When the first target time waveform is approximated, the value of a parameter specifying the first unimodal distribution or the value of the first cumulative distribution function, the value of the parameter specifying the second unimodal distribution or the value of the second cumulative distribution function, the value of the first weighting parameter, the value of the second weighting parameter, and the value of the first level parameter are included. and a second approximated time waveform, which is a time waveform obtained by subtracting from 1 a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a third weighting parameter, and then adding the value of a second-level parameter to the resultant function, the second target time waveform is approximated by a second approximated time waveform, which is a time waveform obtained by subtracting from 1 a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weighting parameter, from the resultant function, the resultant function being obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a fourth weighting parameter, and then adding the result to the second approximated time waveform. A myocardial activity parameter set is a set of a parameter value specifying the third unimodal distribution or a parameter value specifying the third cumulative distribution function, a parameter value specifying the fourth unimodal distribution or a parameter value specifying the fourth cumulative distribution function, the value of the third weighting parameter, the value of the fourth weighting parameter, and the value of the second-level parameter. Each element value included in the myocardial activity parameter set is used as a myocardial activity parameter value to calculate the myocardial activity of a target heart (hereinafter referred to as the "target heart").) using each myocardial activity parameter value included in the myocardial activity parameter set of the first parameter, subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of the parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the second weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, to obtain a time waveform according to a function obtained by adding the myocardial activity parameter value of the first level parameter as a composite waveform for the time interval of an R wave; a waveform synthesis step of obtaining a time waveform as a composite waveform for a T-wave time interval by a function obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by a myocardial activity parameter value of a parameter specifying the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter, from a function obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by a myocardial activity parameter value by the myocardial activity parameter value of the third weighting parameter, and adding the result to the myocardial activity parameter value of the second-level parameter; and connecting the composite waveform for the R-wave time interval and the composite waveform for the T-wave time interval to obtain a composite signal for a waveform indicating the cardiac cycle of the target heart.

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

[0012] 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]

[0013] [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 a signal conversion device 2 and a signal synthesis device 3 in the second embodiment and its first and fifth modifications. [Figure 61] 10 is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in the second embodiment and its modified examples 1 and 5. [Figure 62] FIG. 10 is a diagram showing an example of the functional configuration of a signal conversion device 2 and a signal synthesis device 3 in Modifications 2, 3, and 5 of the second embodiment. [Figure 63] 10 is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in Modifications 2, 3, and 5 of the second embodiment. [Figure 64] FIG. 10 is a diagram showing an example of the functional configuration of a signal conversion device 2 and a signal synthesis device 3 in a fourth modified example of the second embodiment. [Figure 65] 10 is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in the fourth modification of the second embodiment. [Figure 66] FIG. 13 is a diagram showing an example of the functional configuration of a signal conversion device 2 and a signal synthesis device 3 in a sixth modified example of the second embodiment. [Figure 67] 13 is a flowchart showing an example of the flow of processing executed by the signal conversion device 2 and the signal synthesis device 3 in the sixth modification of the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0014] 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.

[0015] 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.

[0016] 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").

[0017] 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.

[0018] Reference 1: Yoshifumi Tanaka, "Understanding the Electrocardiogram Waveform from Its Origin: Deciphering the Myocardial Action Potential," Gakken Medical Shujunsha (2012)

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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).

[0031]

number

[0032]

number

[0033]

number

[0034]

number

[0035] 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.

[0036] 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.

[0037]

number

[0038] 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.

[0039] The weighted difference between the first cumulative distribution function and the second cumulative distribution function is expressed, for example, by the following formula (6), where the weight of the first cumulative distribution function is 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.

[0040]

number

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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).

[0045]

number

[0046]

number

[0047]

number

[0048] 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).

[0049]

number

[0050] 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.

[0051] 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.

[0052] 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).

[0053]

number

[0054]

number

[0055]

number

[0056] For example, when the second target time waveform (i.e., T wave) is approximated by the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function using the first method, the 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).

[0057]

number

[0058] 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.

[0059] 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).

[0060]

number

[0061]

number

[0062]

number

[0063] 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).

[0064]

number

[0065] 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.

[0066] 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 (CDF) is used to calculate the T wave amplitude.

[0067] 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).

[0068] 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 the dashed dotted 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 two-dot 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).

[0069] 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.

[0070] 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.

[0071] The "first fitting result" and "second fitting result" in Fig. 4 are the results of fitting for the R wave. The "third fitting result" and "fourth fitting result" in Fig. 4 are the results of fitting for the T wave.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] [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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089]

number

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094]

number

[0095] 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.

[0096] 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).

[0097]

number

[0098] 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.

[0099] For example, when the second target time waveform (i.e., T wave) is approximated by a function obtained by adding a level value to 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).

[0100]

number

[0101] 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.

[0102] 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).

[0103] 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).

[0104] 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.

[0105] [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.

[0106] 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.

[0107] 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").

[0108] 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.

[0109]

number

[0110]

number

[0111] 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.

[0112] 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.

[0113]

number

[0114]

number

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The input unit 12 includes input devices such as a mouse, a keyboard, and a touch panel. The input unit 12 may be configured as an interface 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

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

[0133] 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.

[0134] 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.

[0135] 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.

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

[0137] 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).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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.

[0168] 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 the execution of the analysis of the cardiac potential by the signal analysis device 1, and the fitting results.

[0169] 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.

[0170] 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.

[0171] [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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] [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.

[0183] 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.

[0184] 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.

[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 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.

[0186] 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.

[0187] (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.

[0188] (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.

[0189] (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.

[0190] (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.

[0191] (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.

[0192] (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.

[0193] (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 have occurred 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, when 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.

[0194] (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.

[0195] (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.

[0196] 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.

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

[0198] Second Embodiment The signal analysis device 1 of the first embodiment can obtain myocardial activity parameters, which are information useful for understanding the state of the heart, from one channel of time-series biological information related to the heartbeat. However, even if the myocardial activity parameters obtained by the signal analysis device 1 of the first embodiment are presented as they are to users such as doctors who are accustomed to looking at electrocardiogram waveforms, it is not necessarily easy for the users such as doctors to understand the state of the heart. Therefore, the signal conversion device of the second embodiment is capable of generating corrected waveforms that make it easier for users such as doctors to understand the state of the heart.

[0199] 60 is a diagram showing an example of the functional configuration of the signal conversion device 2 of the second embodiment. The signal conversion device 2 of the second embodiment includes a signal analysis unit 210, a converter acquisition unit 220, a conversion information setting unit 230, a myocardial activity parameter conversion unit 240, and a waveform synthesis unit 250. As with the signal analysis device 1 of the first embodiment, any time-series biological information related to the beating of a target heart may be input to the signal conversion device 2 of the second embodiment. However, the following description will be given of an example in which an electrocardiogram waveform of the target heart is input to the signal conversion device 2 of the second embodiment. The electrocardiogram waveform of the target heart input to the signal conversion device 2 is an electrocardiogram waveform acquired from the target heart using a known device or the like, and is the waveform to be processed by the signal conversion device 2, i.e., the waveform to be corrected by the signal conversion device 2. The electrocardiogram waveform of the target heart input to the signal conversion device 2 is usually a continuous waveform spanning multiple beats (multiple cycles) (i.e., a waveform in which multiple cycles of waveforms are arranged in time series), but it is sufficient if it is a waveform spanning at least one beat (one cycle).

[0200] In the second and subsequent embodiments, the same parts as those of the signal analyzing device 1 of the first embodiment will be described using the same words and symbols as those in the first embodiment, thereby omitting the same explanation as in the first embodiment and avoiding redundant explanation, and the description will focus on the differences from the first embodiment. Note that, since the second and subsequent embodiments deal with both information on the type of myocardial activity parameter and information on the value of the myocardial activity parameter, in the second and subsequent embodiments, the value will be expressed as "value of XX parameter" and the type will be expressed simply as "XX parameter", thereby distinguishing between the type and the value.

[0201] The signal conversion device 2 obtains and outputs a composite signal from the input electrocardiogram waveform of the target heart. The composite signal obtained by the signal conversion device 2 is a signal having a waveform obtained by correcting the electrocardiogram waveform of the target heart input to the signal conversion device 2. The signal conversion device 2 of the second embodiment performs steps S210, S220, S230A, S240A, and S250 illustrated in FIG.

[0202] The composite signal output by the signal conversion device 2 may be input to a display device having a screen for displaying a waveform, and the display device may present the waveform of the composite signal. If the signal conversion device 2 has a display unit that is a display screen, the signal conversion device 2 may display the waveform of the composite signal on the display unit provided in the device.

[0203] In addition, if a set of myocardial activity parameter values ​​of the target heart ("myocardial activity parameter set" to be described later) has already been obtained, the set of myocardial activity parameter values ​​of the target heart may be input as shown by the dashed line in FIG. 60 instead of inputting the electrocardiogram waveform of the target heart. In this case, the set of myocardial activity parameter values ​​of the target heart input to the signal conversion device 2 is a set of myocardial activity parameter values ​​obtained from a waveform indicating the cardiac cycle of the target heart and to be corrected. In this case, the signal conversion device 2 does not need to include the signal analysis unit 210, does not need to perform step S210, and can also be said to be a signal synthesis device 3 that obtains and outputs a synthesis signal from the set of myocardial activity parameter values. The hardware configuration of the signal conversion device 2 or the signal synthesis device 3 is, for example, the same as the hardware configuration of the signal analysis device 1 shown in FIG. 1.

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

[0205] The first analysis processing performed by the signal analysis unit 210 is a first analysis processing for approximating a first target time waveform, which is a waveform in a time interval of an R wave in each cycle of an input electrocardiogram of the target heart, with a first approximate time waveform, which is a time waveform obtained by subtracting a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter, and adding the result to the first approximate time waveform. The first approximate time waveform is a time waveform obtained by subtracting a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter, from the result of multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first level parameter. The second target inverse-time waveform, which is a waveform obtained by inverting the time axis of the waveform in the time interval of the T wave of the cycle, is approximated by a second approximated inverse-time waveform, which is a waveform obtained by subtracting a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of the fourth weighting parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of the third weighting parameter, and adding the result to the second level parameter. The process obtains, as myocardial activity parameters, the values ​​of the parameters specifying the third unimodal distribution or the third cumulative distribution function, the values ​​of the parameters specifying the fourth unimodal distribution or the fourth cumulative distribution function, the values ​​of the third weighting parameter, the fourth weighting parameter, and the second level parameter. That is, the first weighting parameter is the weight k of the first cumulative distribution function described in the first embodiment. a and the second weight parameter is the weight k of the second cumulative distribution function described in the first embodiment. b and the third weight parameter is the weight k of the third cumulative distribution function described in the first embodiment. e and the fourth weight parameter is the weight k of the fourth cumulative distribution function described in the first embodiment. g The first level parameter is the R wave level value β R and the second level parameter is the T wave level value β T is.

[0206] When each unimodal distribution is a Gaussian distribution, the parameters specifying each unimodal distribution or the parameters specifying each cumulative distribution function are the mean and standard deviation or variance of each Gaussian distribution. Therefore, when each of the first to fourth unimodal distributions is a Gaussian distribution, an example of the value of the parameter specifying the first unimodal distribution or the value of the parameter specifying the first cumulative distribution function obtained by the first analysis process is the mean value μ of the first unimodal distribution (i.e., the first Gaussian distribution). a and the standard deviation value σ a An example of the value of the parameter specifying the second unimodal distribution or the value of the parameter specifying the second cumulative distribution function obtained by the first analysis process is the mean value μ of the second unimodal distribution (i.e., the second Gaussian distribution). b and the standard deviation value σ b An example of the parameter value specifying the third unimodal distribution or the parameter value specifying the third cumulative distribution function obtained by the first analysis process is the mean value μ of the third unimodal distribution (i.e., the third Gaussian distribution). e and the standard deviation value σ e An example of the value of the parameter specifying the fourth unimodal distribution or the value of the parameter specifying the fourth cumulative distribution function obtained by the first analysis process is the mean value μ of the fourth unimodal distribution (i.e., the fourth Gaussian distribution). g and the standard deviation value σ g is.

[0207] The second analysis processing performed by the signal analysis unit 210 is a process of approximating, for each waveform of an input electrocardiogram of the target heart, a first target time waveform, which is a waveform in a time interval of an R wave in the relevant cycle, with a first approximated time waveform, which is a time waveform obtained by subtracting a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter, and adding the result to the first approximated time waveform. The second approximated time waveform is a time waveform obtained by adding a value of a first level parameter to the first approximated time waveform. The first approximated time waveform is a time waveform obtained by subtracting a function obtained by multiplying a second cumulative distribution function, which is a cumulative distribution function of a second unimodal distribution, by the value of a second weighting parameter from the function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter. The second target time waveform, which is a waveform of the section, is approximated by a second approximated time waveform, which is a time waveform obtained by subtracting a function obtained by multiplying a fourth inverse cumulative distribution function, which is a function obtained by subtracting a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, from a function obtained by multiplying a fourth weight parameter by a third inverse cumulative distribution function, which is a function obtained by subtracting a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, from 1, and adding the result to the second level parameter. The process obtains, as myocardial activity parameters, the values ​​of the parameters specifying the third unimodal distribution or the third cumulative distribution function, the values ​​of the parameters specifying the fourth unimodal distribution or the fourth cumulative distribution function, the third weight parameter, the fourth weight parameter, and the second level parameter. That is, the first weight parameter is the weight k of the first cumulative distribution function described in the first embodiment. a and the second weight parameter is the weight k of the second cumulative distribution function described in the first embodiment. b and the third weight parameter is the weight k of the third cumulative distribution function described in the first embodiment. c and the fourth weight parameter is the weight k of the fourth cumulative distribution function described in the first embodiment. d The first level parameter is the R wave level value β R and the second level parameter is the T wave level value β T is.

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

[0209] Whether the first analysis process or the second analysis process is performed, the signal analysis unit 210 obtains, for each cycle of the input electrocardiogram waveform of the target heart, a set of the following: a parameter value specifying a first unimodal distribution or a parameter value specifying a first cumulative distribution function; a parameter value specifying a second unimodal distribution or a parameter value specifying a second cumulative distribution function; a first weighting parameter value; a second weighting parameter value; a first level parameter value; a parameter value specifying a third unimodal distribution or a parameter value specifying a third cumulative distribution function; a parameter value specifying a fourth unimodal distribution or a parameter value specifying a fourth cumulative distribution function; a third weighting parameter value; a fourth weighting parameter value; and a second level parameter value. Hereinafter, a set of myocardial activity parameter values ​​obtained from the electrocardiogram waveform of each cycle is referred to as a myocardial activity parameter set. When the first to fourth unimodal distributions are Gaussian distributions, the myocardial activity parameter set is a set of values ​​(element values) of 14 types of myocardial activity parameters, including the mean value of the first unimodal distribution, the standard deviation or variance value of the first unimodal distribution, the mean value of the second unimodal distribution, the standard deviation or variance value of the second unimodal distribution, the value of the first weighting parameter, the value of the second weighting parameter, the value of the first level parameter, the mean value of the third unimodal distribution, the standard deviation or variance value of the third unimodal distribution, the mean value of the fourth unimodal distribution, the standard deviation or variance value of the fourth unimodal distribution, the value of the third weighting parameter, the value of the fourth weighting parameter, and the value of the second level parameter. For example, when each of the first to fourth unimodal distributions is a Gaussian distribution and the signal analysis unit 210 performs the first analysis process, the myocardial activity parameter set is (μ a , σ a、 μ b , σ b , k a , k b , β R , μ e , σ e、 μ g , σ g , k e , k g , β T), and the myocardial activity parameter set when each of the first to fourth unimodal distributions is a Gaussian distribution and the signal analysis unit 210 performs the second analysis process is (μ a , σ a、 μ b , σ b , k a , k b , β R , μ c , σ c、 μ d , σ d , k c , k d , β T )

[0210] As described in the first embodiment, the unit of the information x representing time in each analysis process can be any. However, in the second and subsequent embodiments, in order to handle the information representing time in a consistent manner in each processing unit, the start time and the end time of a cycle may each be set to a predetermined sample number, or the start time and the end time of a cycle may each be set to a predetermined relative time, and each processing unit of the signal conversion device 2 may be operated with that information. For this purpose, for example, the electrocardiogram waveform of the target heart may be subjected to a well-known adjustment process, such as companding in the time direction, to obtain a heart rate of 60 beats per minute, and then input to the signal conversion device 2. Alternatively, the signal analysis unit 210 may perform the above-described adjustment process on the electrocardiogram waveform of the target heart input to the signal conversion device 2 before processing to obtain a myocardial activity parameter. Alternatively, the signal analysis unit 210 may perform a process to obtain a myocardial activity parameter on the electrocardiogram waveform of the target heart input to the signal conversion device 2, and then perform a process corresponding to the above-described adjustment process on the myocardial activity parameter value and output the processed myocardial activity parameter value.

[0211] [Converter acquisition unit 220] The converter acquisition unit 220 receives a set of myocardial activity parameters for each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210. When the signal conversion device 2 receives a set of myocardial activity parameters for each cycle of the electrocardiogram of the target heart, the converter acquisition unit 220 receives the set of myocardial activity parameters for each cycle of the electrocardiogram of the target heart input to the signal conversion device 2. A storage unit (not shown) in the converter acquisition unit 220 pre-stores a set of myocardial activity parameters for a standard electrocardiogram (hereinafter referred to as a "standard electrocardiogram"). The standard electrocardiogram is, for example, an electrocardiogram of a healthy subject whose heart exhibits normal sinus rhythm excitation propagation and whose heart is of average size, shape, position, orientation, and physique, and is recorded in accordance with the international standard. The myocardial activity parameter set for the standard electrocardiogram is a set of myocardial activity parameters obtained from one cycle of the waveform of the standard electrocardiogram by the processing of the signal analysis unit 210 described above. The converter acquisition unit 220 obtains a converter that bijectively converts the myocardial activity parameter set of the target heart so that the myocardial activity parameter set for each cycle of the electrocardiogram of the target heart approaches the myocardial activity parameter set of the standard electrocardiogram (step S220). More specifically, the converter acquisition unit 220 obtains a converter that bijectively converts a set of myocardial activity parameters of the target heart so that the set of myocardial activity parameter values ​​for each cycle of the electrocardiogram of the target heart approaches the set of myocardial activity parameter values ​​for one cycle of the standard electrocardiogram. The converter obtained by the converter acquisition unit 220 is output from the converter acquisition unit 220 and input to the myocardial activity parameter conversion unit 240.

[0212] When the signal analysis unit 210 of the signal conversion device 2 performs the first analysis processing, or when the myocardial activity parameter set input to the signal conversion device 2 is obtained by the first analysis processing, a storage unit (not shown) in the converter acquisition unit 220 stores, for one waveform period of a standard electrocardiogram, the values ​​of the parameters specifying the first unimodal distribution or the first cumulative distribution function, and the values ​​of the parameters specifying the second unimodal distribution or the second cumulative distribution function, when a first target time waveform, which is a waveform in a time interval of an R wave in the waveform of the one waveform period, is approximated by a first approximated time waveform, which is a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a second weighting parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weighting parameter, and adding the result to the first level parameter. The myocardial activity parameter set is prestored, the myocardial activity parameter set being determined by the value of a parameter specifying the third unimodal distribution or the value of a parameter specifying the third cumulative distribution function, the value of a parameter specifying the fourth unimodal distribution or the value of a parameter specifying the fourth cumulative distribution function, the value of the third weighting parameter, the value of the fourth weighting parameter, and the value of the second level parameter, when a second target inverse-time waveform, which is a waveform obtained by inverting the time axis of a waveform in a time section of a T wave within one cycle of the waveform, is approximated by a second approximate inverse-time waveform, which is a waveform obtained by subtracting a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of the fourth weighting parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of the fourth weighting parameter, and adding the result to the second level parameter. a0 and the standard deviation of the first unimodal distribution is σ a0 and the mean of the second unimodal distribution is μ b0 and the standard deviation of the second unimodal distribution is σ b0 and the mean of the third unimodal distribution is μ e0 and the standard deviation of the third unimodal distribution is σ e0and the mean of the fourth unimodal distribution is μ g0 and the standard deviation of the fourth unimodal distribution is σ g0 If so, the myocardial activity parameter set of the standard electrocardiogram stored in advance in a storage unit (not shown) in the converter acquisition unit 220 is (μ a0 , σ a0 , μ b0 , σ b0 , k a0 , k b0 , β R0 , μ e0 , σ e0 , μ g0 , σ g0 , k e0 , k g0 , β T0 )

[0213] When the signal analysis unit 210 of the signal conversion device 2 performs the second analysis process, or when the myocardial activity parameter set input to the signal conversion device 2 is obtained by the second analysis process, a storage unit (not shown) in the converter acquisition unit 220 stores, for one waveform period of a standard electrocardiogram, the values ​​of the parameters specifying the first unimodal distribution or the first cumulative distribution function, the values ​​of the parameters specifying the second unimodal distribution or the second cumulative distribution function, and the first weighting parameter, when a first target time waveform, which is a waveform in a time interval of an R wave in the waveform of one cycle of the standard electrocardiogram, is approximated by a first approximated time waveform, which is a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a second weighting parameter, from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a second cumulative distribution function, and adding the value of a first level parameter to the first approximated time waveform. The data value, the value of the second weighting parameter, the value of the first level parameter, and a second target time waveform, which is a waveform in a time section of a T wave in one cycle of the waveform, are approximated by a second approximated time waveform, which is a waveform obtained by subtracting a function obtained by multiplying a fourth inverse cumulative distribution function, which is a function obtained by subtracting a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, from a function obtained by multiplying a fourth weighting parameter by a third inverse cumulative distribution function, which is a function obtained by subtracting a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, from 1, and adding the result to the second level parameter. A myocardial activity parameter set is pre-stored, which includes the value of the parameter specifying the third unimodal distribution or the value of the parameter specifying the third cumulative distribution function, the value of the third weighting parameter, the value of the fourth weighting parameter, and the value of the second level parameter. Each of the first to fourth unimodal distributions is a Gaussian distribution, and the mean value of the first unimodal distribution is μ a0 and the standard deviation of the first unimodal distribution is σ a0 and the mean of the second unimodal distribution is μ b0 and the standard deviation of the second unimodal distribution is σ b0 and the mean of the third unimodal distribution is μ c0and the standard deviation of the third unimodal distribution is σ c0 and the mean of the fourth unimodal distribution is μ d0 and the standard deviation of the fourth unimodal distribution is σ d0 If so, the myocardial activity parameter set (μ a0 , σ a0 , μ b0 , σ b0 , k a0 , k b0 , β R0 , μ c0 , σ c0 , μ d0 , σ d0 , k c0 , k d0 , β T0 )

[0214] The converter acquisition unit 220 generates a converter that bijectively converts the myocardial activity parameter set of the target heart so that the myocardial activity parameter set of the electrocardiogram of the target heart approaches the myocardial activity parameter set of the standard electrocardiogram. For example, the converter acquisition unit 220 generates a converter so that the total error between each of the converted myocardial activity parameter sets obtained by converting each of the myocardial activity parameter sets of multiple cycles of the electrocardiogram of the target heart with the converter and the myocardial activity parameter set of the standard electrocardiogram is minimized. The converter acquisition unit 220 may generate one converter from the myocardial activity parameter set of all the cycles of the input electrocardiogram of the target heart, or may generate one converter for each of the myocardial activity parameter sets of multiple cycles included in the myocardial activity parameter set of all the cycles of the input electrocardiogram of the target heart.

[0215] The converter generated by the converter acquisition unit 220 may be a conversion matrix, a function, a neural network, or any other type of converter. Regardless of the type of converter, the converter acquisition unit 220 may generate a converter that bijectively converts the myocardial activity parameter sets of the target heart using a well-known technique, for example, so that the total error between each converted myocardial activity parameter set obtained by converting each of the myocardial activity parameter sets of multiple cycles of the electrocardiogram of the target heart with the converter and the myocardial activity parameter set of the standard electrocardiogram is minimized.

[0216] [Modification of the converter acquisition unit 220] A converter generated from a myocardial activity parameter set obtained from an electrocardiogram waveform previously acquired from the target heart and a myocardial activity parameter set of a standard electrocardiogram may be stored in advance in a storage unit (not shown) within the converter acquisition unit 220, and the converter acquisition unit 220 may acquire the pre-stored converter. This configuration will be described as a modified example of the converter acquisition unit 220. In this modified example as well, the converter acquired by the converter acquisition unit 220 is output from the converter acquisition unit 220 and input to the myocardial activity parameter conversion unit 240.

[0217] In this modification, for example, as shown by the dashed line in FIG. 60 , in addition to the electrocardiogram waveform of the target heart, identification information (hereinafter referred to as the “target heart ID”) for identifying the target heart is input to the signal conversion device 2, and the target heart ID input to the signal conversion device 2 is input to the converter acquisition unit 220. The target heart ID is designated by a user such as a doctor. For example, the signal conversion device 2 may include a display unit which is a display screen that displays target heart IDs as options, and an input unit which is an input interface such as a keyboard or mouse that accepts a user's selection, such that the display unit displays the target heart IDs as options, the input unit accepts the user's selection of any of the target heart IDs, and the target heart ID selected by the input unit is input to the converter acquisition unit 220. Note that in this modification, the myocardial activity parameter sets for each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 do not need to be input to the converter acquisition unit 220. In this modified example, the converter acquisition section 220 does not need to store the myocardial activity parameter set of the standard electrocardiogram.

[0218] Specific processing in this modified example is the following substeps S220-A1 to S220-A3. However, it is the following substep S220-A3 that the converter acquisition unit 220 performs when the signal conversion device 2 is operated, and the following substeps S220-A1 and S220-A2 are performed in advance before the signal conversion device 2 is used.

[0219] Substep S220-A1: For each of the plurality of target hearts, obtain a converter that bijectively converts the myocardial activity parameter sets of the plurality of previously acquired electrocardiogram cycles so that each of the myocardial activity parameter sets approaches the myocardial activity parameter set of the standard electrocardiogram. The converter may be a transformation matrix, a function, a neural network, or any other type, and may be obtained by the well-known technique as described above. Sub-step S220-A2: Each converter obtained in sub-step S220-A1 is associated with the target heart ID and stored in advance in a storage unit (not shown) in the converter acquisition unit 220. Substep S220-A3: The converter acquisition unit 220 acquires a converter associated with the input target heart ID from a storage unit (not shown) in the converter acquisition unit 220.

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

[0221] For example, the conversion information setting unit 230 includes a display unit, which is a display screen that displays identification information of optional myocardial activity parameters, and an input unit, which is an input interface such as a keyboard or a mouse that accepts selections by a user, where the display unit displays the identification information of optional myocardial activity parameters, the input unit accepts the user's selection of one or more optional myocardial activity parameters, and outputs conversion information that identifies one or more myocardial activity parameters selected by the input unit. The optional myocardial activity parameters are, for example, the 14 types of myocardial activity parameters described above.

[0222] The conversion information may be any information that can identify which of the myocardial activity parameter options the designated parameter is, and may be an identification number or a group of identification numbers that represent the type of designated parameter, an identification number or a group of identification numbers that represent the type of myocardial activity parameter that is not a designated parameter (hereinafter also referred to as a "non-designated parameter"), or a group of information that represents whether each type of myocardial activity parameter that is an option is a designated parameter or a non-designated parameter.

[0223] [Myocardial activity parameter conversion unit 240] The myocardial activity parameter conversion unit 240 receives the myocardial activity parameter set for each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210, the converter output from the converter acquisition unit 220, and the conversion information output from the conversion information setting unit 230. When the myocardial activity parameter set for each cycle of the electrocardiogram of the target heart is input to the signal conversion device 2, the myocardial activity parameter conversion unit 240 receives the myocardial activity parameter set for each cycle of the electrocardiogram of the target heart input to the signal conversion device 2 instead of the myocardial activity parameter set for each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210. The myocardial activity parameter converter 240 obtains a converted myocardial activity parameter set for each electrocardiogram cycle of the target heart by using the converted myocardial activity parameter values ​​included in the input myocardial activity parameter set for designated parameters specified by the conversion information as they are, and converting the converted myocardial activity parameter values ​​included in the input myocardial activity parameter set for non-designated parameters (i.e., non-designated parameters) using a converter to obtain the converted myocardial activity parameter values ​​(step S240A). For example, the myocardial activity parameter converter 240 obtains a converted myocardial activity parameter set for each electrocardiogram cycle of the target heart by performing the following substeps S240A-1 and S240A-2. The converted myocardial activity parameter set for each electrocardiogram cycle of the target heart obtained by the myocardial activity parameter converter 240 is input to the waveform synthesizer 250.

[0224] Substep S240A-1 The myocardial activity parameter conversion unit 240 first converts the myocardial activity parameter set for each period of the electrocardiogram of the target heart using a converter to obtain a converted myocardial activity parameter set (hereinafter referred to as a "provisional converted myocardial activity parameter set"). Hereinafter, each parameter included in the provisional converted myocardial activity parameter set is referred to as a provisional converted myocardial activity parameter.

[0225] Substep S240A-2 The myocardial activity parameter conversion unit 240 then obtains a converted myocardial activity parameter set by taking the value of the myocardial activity parameter included in the input myocardial activity parameter set as the value of the converted myocardial activity parameter for the designated parameters identified by the conversion information, and taking the value of the provisional converted myocardial activity parameter included in the provisional converted myocardial activity parameter set as the value of the converted myocardial activity parameter for the myocardial activity parameters that are not designated parameters identified by the conversion information (i.e., non-designated parameters).

[0226] Some converted myocardial activity parameter values ​​included in the converted myocardial activity parameter set are the values ​​of the myocardial activity parameters that were included in the myocardial activity parameter set, and the remaining converted myocardial activity parameter values ​​included in the converted myocardial activity parameter set are the values ​​of the provisional converted myocardial activity parameters that were included in the provisional converted myocardial activity parameter set. However, hereinafter, the values ​​of each converted myocardial activity parameter will be represented by the following symbols, regardless of whether they are myocardial activity parameter values ​​or provisional converted myocardial activity parameter values. Note that the symbols for the mean and standard deviation below are those for the case where the first to fourth unimodal distributions are Gaussian distributions.

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

[0228] For the values ​​of the myocardial activity parameters obtained by the myocardial activity parameter conversion unit 240 from the values ​​of the myocardial activity parameters obtained by the first analysis process from the waveform of the T wave time interval, the mean value of the third unimodal distribution is μ' e and the standard deviation of the third unimodal distribution is denoted as σ' e and the mean value of the fourth unimodal distribution is μ' g and the standard deviation of the fourth unimodal distribution is denoted as σ' g and the value of the third weight parameter is k' e and the value of the fourth weight parameter is k' g and the value of the second-level parameter is denoted as β' T It is written as follows.

[0229] For the values ​​of the myocardial activity parameters obtained by the myocardial activity parameter conversion unit 240 from the waveform of the T wave in the second analysis process, the mean value of the third unimodal distribution is μ' c and the standard deviation of the third unimodal distribution is denoted as σ' c and the mean value of the fourth unimodal distribution is μ' d and the value of the standard deviation of the fourth unimodal distribution is σ' d and the value of the third weight parameter is k' c and the value of the fourth weight parameter is k' d and the value of the second-level parameter is denoted as β' T It is written as follows.

[0230] [Waveform synthesis section 250] The converted myocardial activity parameter set for each period of the electrocardiogram of the target heart obtained by the myocardial activity parameter conversion unit 240 is input to the waveform synthesis unit 250. The waveform synthesis unit 250 performs the following first or second generation process for each period of the electrocardiogram of the target heart to obtain a synthesized signal for each period from the converted myocardial activity parameter set (step S250). Note that the waveform synthesis unit 250 may further obtain synthesized signals for multiple periods by concatenating synthesized signals for multiple periods in the order of the periods.

[0231] The first generation process (step S250A) performed by the waveform synthesis unit 250 for each cycle is performed when the signal analysis unit 210 performs the first analysis process or when the myocardial activity parameter set input to the signal conversion device 2 is obtained by the first analysis process, and includes a process of obtaining a composite waveform for the R wave time interval of the cycle (substep S250A-1), a process of obtaining a composite waveform for the T wave time interval of the cycle (substep S250A-2), and a process of connecting the composite waveform for the R wave time interval of the cycle and the composite waveform for the T wave time interval of the cycle to obtain a composite signal for the cycle (substep S250A-3).

[0232] The process of sub-step S250A-1 performed by the waveform synthesis unit 250 is to calculate a cumulative distribution function F specified by the values ​​of the myocardial activity parameters after transformation of the parameters specifying the first unimodal distribution or the parameters specifying the first cumulative distribution function. a (x) is the value of the myocardial activity parameter k' after the transformation of the first weighting parameter a Function k' multiplied by a F a The cumulative distribution function F is determined by the value of the myocardial activity parameter after transformation of the parameter that determines the second unimodal distribution or the parameter that determines the second cumulative distribution function from (x). b (x) is the value of the myocardial activity parameter k' after the transformation of the second weighting parameter b Function k' multiplied by b F b (x) is subtracted to obtain the value β' of the myocardial activity parameter after transformation of the first level parameter. R Function k' with a F a (x)-k' b F b (x)+β' R This is a process of obtaining the time waveform of the R wave as a composite waveform in the time interval of the R wave.

[0233] If the first unimodal distribution is Gaussian, then the cumulative distribution function F a (x) is the average value μ' a and the standard deviation value σ' aIn addition, if the second unimodal distribution is a Gaussian distribution, the cumulative distribution function F b (x) is the average value μ' b and the standard deviation value σ' b Therefore, in sub-step S250A-1, the waveform synthesis section 250 can obtain a synthesized waveform for the time interval of the R wave using equation (29).

[0234]

number

[0235]

number

[0236]

number

[0237] Note that the function k' a F a (x)-k' b F b (x)+β' R is the value of the function when it reaches its minimum value β' R The time range in which the value of the function is close to the maximum value k' a -k' b +β' R In the time range where the value is close to , the change in the value of the function over time is smaller than the change in the amplitude over time due to noise and fluctuations in the actually acquired electrocardiogram. In other words, in order to obtain a corrected waveform that makes it easier for users such as doctors to understand the state of the heart, the function k' a F a (x)-k' b F b (x)+β' RA time range in which the time change in the value of k' is less than the time change in amplitude due to noise or fluctuations does not need to be included in the time interval of the R wave. Also, if each unimodal distribution is a Gaussian distribution, for example, 99.7% of the information is contained in the time range in which the time difference from the mean value is up to six times the standard deviation value. Therefore, for the purpose of obtaining a corrected waveform that makes it easy for users such as doctors to understand the state of the heart, there is no need to include other time ranges in the time interval of the R wave. Therefore, in sub-step S250A-1 performed by the waveform synthesis unit 250, the function k' a F a (x)-k' b F b (x)+β' R The waveform of the time interval required for a user such as a doctor to understand the state of the heart may be obtained as a composite waveform of the time interval of the R wave. a F a (x)-k' b F b (x)+β' R In the time waveform of the function k', a time interval determined by a predetermined method from the standard deviation of the Gaussian distribution included in the function, or a time interval in which the value of the function falls within a range determined by a predetermined method from the minimum and maximum values ​​of the function, may be obtained as a composite waveform of the time interval of the R wave. a F a (x)-k' b F b (x)+β' R The waveform of the time interval where the time difference from the mean value of the Gaussian distribution included in the function is up to six times the standard deviation value of the function k' can be obtained as the composite waveform of the time interval of the R wave. a F a (x)-k' b F b (x)+β' RSince there are two Gaussian distributions included in the above, there are four values ​​where the time difference from the mean value of the Gaussian distribution is six times the standard deviation value. For example, the value where the time difference from the mean value of the Gaussian distribution is six times the standard deviation value and is closest to the start of the cycle can be set as the start time of the R-wave time interval, and the value where the time difference from the mean value of the Gaussian distribution is six times the standard deviation value and is closest to the end of the cycle can be set as the end time of the R-wave time interval, thereby obtaining a composite waveform for the R-wave time interval.

[0238] The process of sub-step S250A-2 performed by the waveform synthesis unit 250 is to calculate a cumulative distribution function F specified by the value of the myocardial activity parameter after transformation of the parameter specifying the third unimodal distribution or the parameter specifying the third cumulative distribution function. e (x') is the value of the myocardial activity parameter k' after transformation of the third weighting parameter e Function k' multiplied by e F e The cumulative distribution function F is determined by the value of the myocardial activity parameter after transformation of the parameter specifying the fourth unimodal distribution or the parameter specifying the fourth cumulative distribution function from (x'). g (x') is the value of the myocardial activity parameter k' after transformation of the fourth weighting parameter g Function k' multiplied by g F g (x') is subtracted to obtain the β' value of the myocardial activity parameter after transformation of the second level parameter. T Function k' with e F e (x')-k' g F g (x')+β' T This is a process in which the time axis of the waveform obtained by reversing the time axis of the waveform is reversed to obtain a time waveform as a composite waveform for the time interval of the T wave.

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

[0240]

number

[0241]

number

[0242]

number

[0243] For the same reason as explained in sub-step S250A-2, in sub-step S250A-2 performed by the waveform synthesis unit 250, the function k' e F e (x')-k' g F g (x')+β' T The waveform of the time interval necessary for a user such as a doctor to understand the state of the heart can be obtained as a composite waveform of the time interval of the T wave from the time waveform obtained by reversing the time axis of the waveform obtained by e F e (x')-k' g F g (x')+β' T Among the time waveforms obtained by reversing the time axis of the waveform obtained by the function k', a time interval determined by a predetermined method from the standard deviation of the Gaussian distribution included in the function, or a time interval in which the value of the function falls within a range determined by a predetermined method from the minimum and maximum values ​​of the function, may be obtained as a composite waveform of the time interval of the T wave. e F e (x')-k' g F g (x')+β' TThe waveform obtained by reversing the time axis of the waveform obtained by the function k' is the waveform for the time interval in which the time difference from the mean value of the Gaussian distribution included in the function is up to six times the standard deviation value, and is obtained as the composite waveform for the time interval of the T wave. e F e (x')-k' g F g (x')+β' T There are two Gaussian distributions contained in the time waveform obtained by reversing the time axis of the waveform obtained by the method described above, and therefore there are four values ​​where the time difference from the mean value of the Gaussian distribution is six times the standard deviation. For example, the value where the time difference from the mean value of the Gaussian distribution is six times the standard deviation and is closest to the start of the cycle can be set as the start time of the T-wave time interval, and the value where the time difference from the mean value of the Gaussian distribution is six times the standard deviation and is closest to the end of the cycle can be set as the end time of the T-wave time interval, thereby obtaining a composite waveform for the T-wave time interval.

[0244] The processing of substep S250A-3 performed by waveform synthesis unit 250 is processing in which the start of the period and the start of the composite waveform for the R-wave time interval obtained in the processing of substep S250A-1 are connected in a predetermined manner, the end of the composite waveform for the R-wave time interval obtained in the processing of substep S250A-1 is connected in a predetermined manner to the start of the composite waveform for the T-wave time interval obtained in the processing of substep S250A-2, and the end of the composite waveform for the T-wave time interval obtained in the processing of substep S250A-2 is connected to the end of the period in a predetermined manner, thereby obtaining a composite signal for one period that connects the composite waveform for the R-wave time interval and the composite waveform for the T-wave time interval. The predetermined method is, for example, a method of connecting the beginning of a cycle and the beginning of the composite waveform for the R-wave time interval, the end of the composite waveform for the R-wave time interval and the beginning of the composite waveform for the T-wave time interval, and the end of the composite waveform for the T-wave time interval and the end of the cycle with a straight line or a predetermined function. The predetermined function is, for example, a function that smoothly connects the beginning of the composite waveform for the R-wave time interval, the end of the composite waveform for the R-wave time interval, the beginning of the composite waveform for the T-wave time interval, and the end of the composite waveform for the T-wave time interval. Note that when connecting the beginning of a cycle among multiple cycles and the beginning of the composite waveform for the R-wave time interval, the waveform synthesis unit 250 may use a straight line or a predetermined function that connects the end of the composite waveform for the T-wave time interval of the cycle immediately preceding the cycle to the beginning of the composite waveform for the R-wave time interval of the cycle. Similarly, when connecting the end of the composite waveform for the time period of the T wave in a certain cycle among multiple cycles to the end of the cycle, the waveform synthesis unit 250 may use a straight line or a predetermined function that connects the end of the composite waveform for the time period of the T wave in the certain cycle to the start of the composite waveform for the time period of the R wave in the cycle immediately following the certain cycle.

[0245] The second generation process (step S250B) performed by the waveform synthesis unit 250 for each period is performed when the signal analysis unit 210 performs the second analysis process or when the myocardial activity parameter set input to the signal conversion device 2 is obtained by the second analysis process, and includes a process of obtaining a composite waveform for the R-wave time interval of the period (substep S250B-1), a process of obtaining a composite waveform for the T-wave time interval of the period (substep S250B-2), and a process of combining the composite waveform for the R-wave time interval of the period and the composite waveform for the T-wave time interval of the period to obtain a composite signal for the period (substep S250B-3). Here, the process of substep S250B-1 is the same as the process of substep S250A-1, and the process of substep S250B-3 is the same as the process of substep S250A-3. Step S250B differs from step S250A in that it includes the following sub-step S250B-2 instead of sub-step S250A-2.

[0246] The process of sub-step S250B-2 performed by the waveform synthesis unit 250 is to calculate a cumulative distribution function F specified by the value of the myocardial activity parameter after transformation of the parameter specifying the third unimodal distribution or the parameter specifying the third cumulative distribution function. c The function 1-F is the function obtained by subtracting (x) from 1. c (x) is the value of the myocardial activity parameter k' after the transformation of the third weighting parameter c Function k' multiplied by c (1-F c (x)) to determine the cumulative distribution function F determined by the value of the myocardial activity parameter after transformation of the parameter determining the fourth unimodal distribution or the parameter determining the fourth cumulative distribution function. d The function 1-F is the function obtained by subtracting (x) from 1. d (x) is the value of the myocardial activity parameter k' after transformation of the fourth weighting parameter d Function k' multiplied by d (1-F d (x)) to obtain the converted myocardial activity parameter value β' of the second level parameter. T Function k' with c (1-F c (x))-k' d (1-Fd (x))+β' T This is a process of obtaining the time waveform of the T wave as a composite waveform in the time interval of the T wave.

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

[0248]

number

[0249]

number

[0250]

number

[0251] For the same reason as explained in sub-step S250A-2, in sub-step S250B-2 performed by the waveform synthesis unit 250, the function k' c (1-F c (x))-k' d (1-F d (x))+β' T The waveform of the time interval required for a user such as a doctor to understand the state of the heart from the time waveform of the function k' may be obtained as a composite waveform of the time interval of the T wave. c (1-F c (x))-k' d (1-F d (x))+β'T A time interval determined by a predetermined method from the standard deviation of the Gaussian distribution included in the function, or a time interval in which the value of the function falls within a range determined by a predetermined method from the minimum and maximum values ​​of the function, may be obtained as a composite waveform of the time interval of the T wave. c (1-F c (x))-k' d (1-F d (x))+β' T The waveform of the time interval where the time difference from the mean value of the Gaussian distribution included in the function is up to six times the standard deviation value of the function k' can be obtained as the composite waveform of the time interval of the T wave. c (1-F c (x))-k' d (1-F d (x))+β' T Since there are two Gaussian distributions included in the time waveform by

[0048] , there are four values ​​where the time difference from the mean value of the Gaussian distribution is six times the standard deviation value. For example, the value where the time difference from the mean value of the Gaussian distribution is six times the standard deviation value and is closest to the start of the cycle can be set as the start time of the T-wave time interval, and the value where the time difference from the mean value of the Gaussian distribution is six times the standard deviation value and is closest to the end of the cycle can be set as the end time of the T-wave time interval, thereby obtaining a composite waveform for the T-wave time interval.

[0252] [Method of Using the Signal Conversion Device 2 of the Second Embodiment] For example, if a doctor wants to check for abnormalities in the epimyocardium during repolarization of the myocardial activity of the target heart, the signal conversion device 2 of the second embodiment can be operated using, as specified parameters, a parameter specifying a fourth unimodal distribution or a parameter specifying a fourth cumulative distribution function, which is an epimyocardium parameter during repolarization, and a fourth weighting parameter. In this manner, the signal conversion device 2 of the second embodiment outputs a composite signal in which features other than the features of the epimyocardium activity during repolarization of the myocardium of the target heart are significantly reduced, i.e., a composite signal in which the features of the epimyocardium activity during repolarization of the myocardium of the target heart are prominently retained. Therefore, by using the signal conversion device 2 of the second embodiment, a doctor can check a corrected waveform in which the features of the epimyocardium activity during repolarization of the myocardium of the target heart are prominently retained.

[0253] Alternatively, for example, if a doctor wants to check for abnormalities related to ischemia in the target heart, the signal conversion device 2 of the second embodiment can be operated by setting level parameters and weighting parameters related to ischemia as designated parameters. As a result, the signal conversion device 2 of the second embodiment outputs a synthesized signal in which the features related to ischemia in the target heart are prominently retained, and the doctor can check the corrected waveform in which the features related to ischemia in the target heart are prominently retained.

[0254] That is, according to the second embodiment, a user such as a doctor can confirm a corrected waveform in which features other than those related to the desired activity or the desired abnormality among the myocardial activity of the target heart have been significantly reduced, i.e., a corrected waveform in which the features related to the desired activity or the desired abnormality among the myocardial activity of the target heart remain prominently. Note that the features reduced by the signal conversion device 2 of the second embodiment also include individual differences among the target hearts.

[0255] <Modification 1 of the Second Embodiment> For each myocardial activity parameter, a value between the value of the myocardial activity parameter obtained from the electrocardiogram waveform of the target heart and the value obtained by converting the value of the myocardial activity parameter using a converter may be set as the value of the converted myocardial activity parameter. This embodiment will be referred to as Modification 1 of the second embodiment, and differences from the second embodiment will be mainly described below.

[0256] The signal converter 2 of the first modification of the second embodiment includes a signal analyzer 210, a converter acquirer 220, a conversion information setting unit 230, a myocardial activity parameter converter 240, and a waveform synthesizer 250, similar to the signal converter 2 of the second embodiment. However, the operation of the conversion information setting unit 230 and the operation of the myocardial activity parameter converter 240 are different from those of the signal converter 2 of the second embodiment. The signal converter 2 of the first modification of the second embodiment performs steps S210, S220, S230B, S240B, and S250 illustrated in Fig. 61. Step S210 performed by the signal analyzer 210, step S220 performed by the converter acquirer 220, and step S250 performed by the waveform synthesizer 250 of the signal converter 2 of the first modification of the second embodiment are the same as those of the signal converter 2 of the second embodiment.

[0257] As in the second embodiment, if the myocardial activity parameter set of the target heart has already been obtained, the myocardial activity parameter set of the target heart may be input instead of the electrocardiogram waveform of the target heart. In this case, the signal conversion device 2 does not need to include the signal analysis unit 210, does not need to perform step S210, and can be said to be a signal synthesis device 3 that obtains and outputs a synthesis signal from the myocardial activity parameter set.

[0258] [Conversion information setting unit 230] The conversion information setting unit 230 receives the user's designation of the reflection weight value for each myocardial activity parameter and outputs conversion information specifying the designated reflection weight value for each myocardial activity parameter to the myocardial activity parameter conversion unit 240 (step S230B). The reflection weight value for each myocardial activity parameter is designated by a user, such as a doctor, for the purpose of detecting or confirming abnormalities in the myocardial activity of the target heart. For example, the conversion information setting unit 230 may display on the display unit a field or a slide bar for setting the reflection weight value for each myocardial activity parameter, and output conversion information including the values ​​entered by the user into each field or slide bar by the input unit as the reflection weight value for each myocardial activity parameter. The myocardial activity parameters for which reflection weight values ​​are designated are, for example, the 14 types of myocardial activity parameters described above.

[0259] [Myocardial activity parameter conversion unit 240] The myocardial activity parameter converter 240 calculates a weighted average of the values ​​of the myocardial activity parameters included in the input myocardial activity parameter set and the values ​​obtained by converting the values ​​of the myocardial activity parameters included in the input myocardial activity parameter set using the reflection weights, and determines the converted myocardial activity parameter value for each myocardial activity parameter in each period as a converted myocardial activity parameter value (step S240B). For example, the myocardial activity parameter converter 240 may use the reflection weights as weights to be assigned to the values ​​of the myocardial activity parameters included in the input myocardial activity parameter set for each myocardial activity parameter in each period to obtain the weighted average value, and then use the obtained weighted average value as the converted myocardial activity parameter value.

[0260] For example, if the lower limit of the range that the reflection weight value can take is 0 and the upper limit is 1, the myocardial activity parameter conversion unit 240 obtains, for each myocardial activity parameter in each cycle, the value of the converted myocardial activity parameter by adding the product of the value of the myocardial activity parameter included in the input myocardial activity parameter set and the reflection weight value, and the product of the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter set using a converter and the value obtained by subtracting the reflection weight value from 1. That is, the myocardial activity parameter converter 240 determines the converted myocardial activity parameter value so that, for each myocardial activity parameter in each period, the value of the myocardial activity parameter included in the input myocardial activity parameter set is the first end of the range of possible converted myocardial activity parameters, and the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter set using the converter is the second end of the range of possible converted myocardial activity parameters. The larger the reflection weight value specified by the conversion information, the closer the converted myocardial activity parameter value to the first end of the range of possible converted myocardial activity parameters, and the smaller the reflection weight value specified by the conversion information, the closer the converted myocardial activity parameter value to the second end of the range of possible converted myocardial activity parameters. Note that, for a myocardial activity parameter with a reflection weight value of 1, the myocardial activity parameter converter 240 simply uses the value of the myocardial activity parameter included in the input myocardial activity parameter set as the converted myocardial activity parameter value, and for a myocardial activity parameter with a reflection weight value of 0, the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter set using the converter is used as the converted myocardial activity parameter value.

[0261] For example, the myocardial activity parameter conversion unit 240 may set the lower limit of the range that the reflection weight value can take to 0 and the upper limit to a value greater than 1, and for each myocardial activity parameter in each cycle, obtain the converted myocardial activity parameter value by adding the product of the value of the myocardial activity parameter included in the input myocardial activity parameter set and the reflection weight value, and the product of the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter set using a converter and the value obtained by subtracting the reflection weight value from 1. That is, the myocardial activity parameter conversion unit 240 may obtain the value of the converted myocardial activity parameter such that, for each myocardial activity parameter in each cycle, a predetermined value in the opposite direction to the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter set using a converter is the first end value of the range of possible values ​​of the converted myocardial activity parameter, and the value obtained by converting the value of the myocardial activity parameter included in the input myocardial activity parameter set using a converter is the second end value of the range of possible values ​​of the converted myocardial activity parameter, and the larger the reflection weight value specified by the conversion information, the closer the value of the converted myocardial activity parameter is to the first end of the range of possible values ​​of the converted myocardial activity parameter, and the smaller the reflection weight value specified by the conversion information, the closer the value of the converted myocardial activity parameter is to the second end of the range of possible values ​​of the converted myocardial activity parameter. In this case, in addition to values ​​between the value of the myocardial activity parameter obtained from the electrocardiogram waveform of the target heart and the value obtained by converting the value of the myocardial activity parameter using a converter, values ​​in the opposite direction to the value obtained by converting the value of the myocardial activity parameter obtained from the electrocardiogram waveform of the target heart using a converter are also allowed to be used as the value of the converted myocardial activity parameter.

[0262] [Method of Using the Signal Conversion Device 2 of Modification 1 of the Second Embodiment] For example, if a physician wants to check for abnormalities in the epimyocardium during repolarization of the myocardial activity of the target heart, the physician can operate the signal conversion device 2 of the first modified example of the second embodiment by specifying the reflection weights of the parameter specifying the fourth unimodal distribution or the parameter specifying the fourth cumulative distribution function, which is an epimyocardium parameter of repolarization, and the fourth weighting parameter so that the reflection weights are greater than the reflection weights of the other myocardial activity parameters. In this way, the signal conversion device 2 of the first modified example of the second embodiment outputs a composite signal in which features other than the features of the epimyocardium activity during repolarization of the myocardium of the target heart are reduced, i.e., a composite signal in which the features of the epimyocardium activity during repolarization of the myocardium of the target heart are more significantly reflected. Therefore, by using the signal conversion device 2 of the second embodiment, the physician can check a corrected waveform in which the features of the epimyocardium activity during repolarization of the myocardium of the target heart are more significantly reflected.

[0263] Furthermore, for example, if a doctor wants to check for abnormalities related to ischemia in the target heart, the doctor can operate the signal conversion device 2 of Modification 1 of the second embodiment by specifying reflection weight values ​​such that the reflection weight values ​​of the level parameters and weight parameters related to ischemia are greater than the reflection weight values ​​of the other myocardial activity parameters. This allows the signal conversion device 2 of Modification 1 of the second embodiment to output a composite signal that more significantly reflects features related to ischemia in the target heart, allowing the doctor to check a corrected waveform that more significantly reflects features related to ischemia in the target heart.

[0264] That is, according to the first modification of the second embodiment, a user such as a doctor can confirm a corrected waveform in which features other than those related to the desired activity or the desired abnormality among the myocardial activity of the target heart are further reduced, i.e., a corrected waveform in which the features related to the desired activity or the desired abnormality among the myocardial activity of the target heart are more significantly reflected. Note that the features reduced by the signal conversion device 2 of the first modification of the second embodiment also include individual differences in the target heart.

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

[0266] FIG. 62 is a diagram showing an example of the functional configuration of a signal converter 2 according to Modification 2 of the second embodiment. The signal converter 2 according to Modification 2 of the second embodiment includes a signal analyzer 210, a conversion information setting unit 230, a myocardial activity parameter converter 240, and a waveform synthesizer 250. The signal converter 2 according to Modification 2 of the second embodiment does not include a converter acquirer 220, and the operation and memory contents of the myocardial activity parameter converter 240 differ from those of the signal converter 2 according to the second embodiment. The signal converter 2 according to Modification 2 of the second embodiment performs steps S210, S230A, S240C, and S250 illustrated in FIG. 63. Step S210 performed by the signal analyzer 210, step S230A performed by the conversion information setting unit 230, and step S250 performed by the waveform synthesizer 250 of the signal converter 2 according to Modification 2 of the second embodiment are the same as those of the signal converter 2 according to the second embodiment. The method of using the signal conversion device 2 of the second modification of the second embodiment is similar to the method of using the signal conversion device 2 of the second embodiment.

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

[0268] [Myocardial activity parameter conversion unit 240] The myocardial activity parameter conversion unit 240 receives the myocardial activity parameter sets for each period of the electrocardiogram of the target heart output from the signal analysis unit 210 and the conversion information output from the conversion information setting unit 230. A memory unit (not shown) in the myocardial activity parameter conversion unit 240 pre-stores myocardial activity parameter sets for standard electrocardiograms. The myocardial activity parameter conversion unit 240 directly sets the values ​​of the myocardial activity parameters included in the input myocardial activity parameter sets as the values ​​of converted myocardial activity parameters for designated parameters specified by the conversion information, and sets the values ​​of the myocardial activity parameters included in the myocardial activity parameter set for the standard electrocardiogram as the values ​​of converted myocardial activity parameters for myocardial activity parameters other than the designated parameters specified by the conversion information (i.e., non-designated parameters), thereby obtaining converted myocardial activity parameter sets for each period of the electrocardiogram of the target heart (step S240C).

[0269] <Modification 3 of the Second Embodiment> Instead of the values ​​obtained by converting the values ​​of myocardial activity parameters obtained from the waveform of the electrocardiogram of the target heart in Modification 1 of the second embodiment using a converter, values ​​of myocardial activity parameters obtained from the waveform of a standard electrocardiogram may be used. That is, for each myocardial activity parameter, a value between the value of the myocardial activity parameter obtained from the waveform of the electrocardiogram of the target heart and the value of the myocardial activity parameter obtained from the waveform of the standard electrocardiogram may be used as the value of the converted myocardial activity parameter. This form will be referred to as Modification 3 of the second embodiment, and differences from Modification 1 of the second embodiment will be mainly described below.

[0270] An example of the functional configuration of the signal converting device 2 of Modification 3 of the second embodiment is shown in Fig. 62, similar to the functional configuration of the signal converting device 2 of Modification 2 of the second embodiment. That is, the signal converting device 2 of Modification 3 of the second embodiment includes a signal analyzing unit 210, a conversion information setting unit 230, a myocardial activity parameter converting unit 240, and a waveform synthesizing unit 250. The signal converting device 2 of Modification 3 of the second embodiment differs from the signal converting device 2 of Modification 1 of the second embodiment in that it does not include the converter acquiring unit 220 and in the operation and stored contents of the myocardial activity parameter converting unit 240. The signal converting device 2 of Modification 3 of the second embodiment performs steps S210, S230B, S240D, and S250 illustrated in Fig. 63. Step S210 performed by the signal analysis unit 210, step S230B performed by the conversion information setting unit 230, and step S250 performed by the waveform synthesis unit 250 of the signal conversion device 2 of Modification 3 of the second embodiment are the same as those of the signal conversion device 2 of Modification 1 of the second embodiment. The method of using the signal conversion device 2 of Modification 3 of the second embodiment is the same as the method of using the signal conversion device 2 of Modification 1 of the second embodiment.

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

[0272] [Myocardial activity parameter conversion unit 240] The myocardial activity parameter conversion unit 240 receives the myocardial activity parameter sets for each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210 and the conversion information output from the conversion information setting unit 230. When the myocardial activity parameter sets for each cycle of the electrocardiogram of the target heart are input to the signal conversion device 2, the myocardial activity parameter sets for each cycle of the electrocardiogram of the target heart input to the signal conversion device 2 are input to the myocardial activity parameter conversion unit 240 instead of the myocardial activity parameter sets for each cycle of the electrocardiogram of the target heart output from the signal analysis unit 210. A storage unit (not shown) in the myocardial activity parameter conversion unit 240 pre-stores myocardial activity parameter sets for a standard electrocardiogram. The myocardial activity parameter conversion unit 240 obtains a converted myocardial activity parameter set for each cycle of the electrocardiogram of the target heart by taking the weighted average, using the reflection weight value, of the value of the myocardial activity parameter included in the input myocardial activity parameter set and the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram as the value of the converted myocardial activity parameter (step S240D).

[0273] For example, if the lower limit of the range that the reflection weight value can take is 0 and the upper limit is 1, the myocardial activity parameter conversion unit 240 obtains, for each myocardial activity parameter in each cycle, the value of the converted myocardial activity parameter by adding the value obtained by multiplying the value of the myocardial activity parameter included in the input myocardial activity parameter set by the reflection weight value to the value obtained by multiplying the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram by the value obtained by subtracting the reflection weight value from 1. That is, the myocardial activity parameter converter 240 determines the converted myocardial activity parameter value so that, for each myocardial activity parameter in each period, the value of the myocardial activity parameter included in the input myocardial activity parameter set is the first end of the range of possible converted myocardial activity parameters, and the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram is the second end of the range of possible converted myocardial activity parameters. The larger the reflection weight value specified by the conversion information, the closer the converted myocardial activity parameter value to the first end of the range of possible converted myocardial activity parameters, and the smaller the reflection weight value specified by the conversion information, the closer the converted myocardial activity parameter value to the second end of the range of possible converted myocardial activity parameters. Note that, for a myocardial activity parameter with a reflection weight value of 1, the myocardial activity parameter converter 240 simply uses the value of the myocardial activity parameter included in the input myocardial activity parameter set as the converted myocardial activity parameter value, and for a myocardial activity parameter with a reflection weight value of 0, the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram as the converted myocardial activity parameter value.

[0274] For example, the myocardial activity parameter conversion unit 240 may set the lower limit of the range that the reflection weight value can take to 0 and the upper limit to a value greater than 1, and for each myocardial activity parameter in each cycle, obtain the converted myocardial activity parameter value by adding the product of the value of the myocardial activity parameter included in the input myocardial activity parameter set and the reflection weight value to the product of the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram and the value obtained by subtracting the reflection weight value from 1. That is, the myocardial activity parameter conversion unit 240 may obtain the value of the converted myocardial activity parameter such that, for each myocardial activity parameter in each cycle, a predetermined value that is in the opposite direction from the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram with respect to the value of the myocardial activity parameter included in the input myocardial activity parameter set is the first end value of the range of possible values ​​of the converted myocardial activity parameter, and the value of the myocardial activity parameter included in the myocardial activity parameter set of the standard electrocardiogram is the second end value of the range of possible values ​​of the converted myocardial activity parameter, and the larger the reflection weight value specified by the conversion information, the closer the value of the converted myocardial activity parameter will be to the first end of the range of possible values ​​of the converted myocardial activity parameter, and the smaller the reflection weight value specified by the conversion information, the closer the value of the converted myocardial activity parameter will be to the second end of the range of possible values ​​of the converted myocardial activity parameter. In this case, in addition to values ​​between the values ​​of myocardial activity parameters obtained from the electrocardiogram waveform of the target heart and the values ​​of myocardial activity parameters included in the myocardial activity parameter set of the standard electrocardiogram, values ​​that are in the opposite direction from the values ​​of myocardial activity parameters obtained from the electrocardiogram waveform of the target heart and the values ​​of myocardial activity parameters included in the myocardial activity parameter set of the standard electrocardiogram are also allowed to be used as the values ​​of the converted myocardial activity parameters.

[0275] As described above, the myocardial activity parameter conversion unit 240 of the second embodiment and its variants 1 to 3 obtains converted myocardial activity parameter values ​​by converting one or more types of myocardial activity parameters so that the myocardial activity parameter values ​​included in the myocardial activity parameter set of the target heart approach or move away from the myocardial activity parameter values ​​included in the myocardial activity parameter set of the standard electrocardiogram.

[0276] <Fourth Modification of the Second Embodiment> For each myocardial activity parameter, a representative value of the myocardial activity parameter obtained from multiple cycles of the electrocardiogram waveform of the target heart may be used as the converted myocardial activity parameter value. This embodiment will be referred to as Modification 4 of the second embodiment, and differences from the second embodiment will be mainly described.

[0277] Fig. 64 is a diagram showing an example of the functional configuration of the signal converter 2 of Modification 4 of the second embodiment. The signal converter 2 of Modification 4 of the second embodiment includes a signal analyzer 210, a myocardial activity parameter converter 240, and a waveform synthesizer 250. The signal converter 2 of Modification 4 of the second embodiment does not include a converter acquirer 220 and a conversion information setter 230, and the operation of the myocardial activity parameter converter 240 differs from that of the signal converter 2 of the second embodiment. The signal converter 2 of Modification 4 of the second embodiment performs steps S210, S240E, and S250 illustrated in Fig. 64. Step S210 performed by the signal analyzer 210 and step S250 performed by the waveform synthesizer 250 of the signal converter 2 of Modification 4 of the second embodiment are similar to those of the signal converter 2 of the second embodiment.

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

[0279] [Myocardial activity parameter conversion unit 240] The myocardial activity parameter conversion unit 240 receives the myocardial activity parameter set for each period of the electrocardiogram of the target heart output from the signal analysis unit 210 or the myocardial activity parameter set for each period of the electrocardiogram of the target heart input to the signal conversion device 2. For each myocardial activity parameter in each period, the myocardial activity parameter conversion unit 240 determines the representative value of the myocardial activity parameter values ​​for multiple periods including the period (hereinafter referred to as the "target period") as the converted myocardial activity parameter value, thereby obtaining a converted myocardial activity parameter set for each period of the electrocardiogram of the target heart (step S240E). The representative value may be, for example, the median, average, minimum, maximum, or moving average value.

[0280] The multiple cycles including the target cycle may be multiple consecutive cycles including the target cycle, or multiple non-consecutive cycles including the target cycle. Furthermore, the multiple cycles including the target cycle may be multiple cycles consisting of a cycle earlier than the target cycle and the target cycle, multiple cycles consisting of a cycle earlier than the target cycle, a cycle later than the target cycle, and the target cycle, or multiple cycles consisting of a cycle later than the target cycle and the target cycle. The multiple cycles to be used as the target cycle may be preset or may be specified by a user such as a doctor. When specified by a user such as a doctor, the signal conversion device 2 may include a display unit that is a display screen that displays options for the multiple cycles including the target cycle, and an input unit that is an input interface such as a keyboard or mouse that accepts the user's selection. The display unit displays the options, the input unit accepts the user's selection of one of the options, and the selection result accepted by the input unit is input to the myocardial activity parameter conversion unit 240.

[0281] [Method of Using the Signal Conversion Device 2 of Modification 4 of the Second Embodiment] For example, if a doctor wants to check myocardial activity of the target heart other than respiratory variation, the doctor can operate the signal conversion device 2 of the fourth modification of the second embodiment by using the number of cardiac cycles corresponding to one or more breathing cycles due to inspiration and expiration, and treating consecutive cycles equal to or greater than this number of cardiac cycles as "multiple cycles including the target cycle." In this way, the signal conversion device 2 obtains a composite signal by using, for each myocardial activity parameter of each cycle, a representative value of the myocardial activity parameter value of the cardiac cycle corresponding to one or more breathing cycles including the target cycle as the value of the converted myocardial activity parameter. As a result, the signal conversion device 2 of the fourth modification of the second embodiment outputs a composite signal in which the influence of respiratory variation has been reduced, allowing the doctor to check a corrected waveform in which the influence of respiratory variation has been reduced in the electrocardiogram of the target heart.

[0282] Furthermore, for example, if a doctor wants to check myocardial activity other than the desired physiological fluctuation of the target heart, the signal conversion device 2 of the fourth modification of the second embodiment can be operated so that both an electrocardiogram cycle of the target heart obtained with a predetermined load that induces the desired physiological fluctuation applied and an electrocardiogram cycle of the target heart obtained without applying the load are included in the "plurality of cycles including the target cycle." For example, electrocardiograms of the target heart can be acquired consecutively with and without applying a predetermined load that induces the desired physiological fluctuation applied, and the signal conversion device 2 of the fourth modification of the second embodiment can acquire a composite signal using a representative value of all the acquired electrocardiogram cycles as the value of the converted myocardial activity parameter.

[0283] Loads that induce desired physiological changes include respiratory loads (e.g., deep breathing and ventilation using a ventilator), postural loads (e.g., body movements such as lying down, standing, sitting, lying on one's side, changes between these positions, head up, head down, raising the legs, and standing up), acceleration and pressure loads (applying acceleration to the body, pressurizing or depressurizing the body such as the legs or abdomen), exercise loads, drug administration loads, and loads due to changes in circulatory dynamics (e.g., infusion, diuresis, dialysis).

[0284] When a doctor wants to check myocardial activity other than the desired physiological fluctuation of the target heart, the signal conversion device 2 of the fourth modification of the second embodiment may be operated so that both an electrocardiogram cycle of the target heart obtained by applying a large predetermined load that induces the desired physiological fluctuation and an electrocardiogram cycle of the target heart obtained by applying a small predetermined load are included in the "plural periods including the target period." For example, electrocardiograms of the target heart may be acquired consecutively under conditions of applying a large predetermined load and a small predetermined load, and the signal conversion device 2 of the fourth modification of the second embodiment may acquire a composite signal by using a representative value of all the acquired electrocardiogram cycles as the value of the converted myocardial activity parameter.

[0285] When a doctor wants to check myocardial activity other than the desired physiological fluctuation of the target heart, the signal conversion device 2 of the fourth modification of the second embodiment may be operated so that both an electrocardiogram cycle of the target heart obtained by applying a predetermined first load that induces the desired physiological fluctuation and an electrocardiogram cycle of the target heart obtained by applying a predetermined second load that induces the physiological fluctuation are included in the "plural periods including the target period." For example, electrocardiograms of the target heart may be acquired consecutively under the conditions of applying the predetermined first load and the predetermined second load, and the signal conversion device 2 of the fourth modification of the second embodiment may acquire a composite signal by using a representative value of all the acquired electrocardiogram cycles as the value of the converted myocardial activity parameter.

[0286] In this manner, the signal conversion device 2 obtains a composite signal by using, for each myocardial activity parameter, a representative value of the myocardial activity parameter values ​​in cardiac cycles under different loads as the value of the converted myocardial activity parameter. As a result, the signal conversion device 2 of the fourth modification of the second embodiment outputs a composite signal in which the influence of desired physiological fluctuations has been reduced, and a doctor can confirm the corrected waveform in which the influence of desired physiological fluctuations has been reduced in the electrocardiogram of the target heart.

[0287] <Fifth Modification of the Second Embodiment> In the myocardial activity parameter conversion unit 240 of the second embodiment and its modifications 1 to 3, a representative value of the myocardial activity parameter obtained from a plurality of cycles of the electrocardiogram waveform of the target heart may be used instead of the value of the myocardial activity parameter obtained from the electrocardiogram waveform of the target heart. This form will be referred to as Modification 5 of the second embodiment, and the differences from the second embodiment and its modifications 1 to 3 will be described. Modification 5 of the second embodiment differs from the second embodiment and its modifications 1 to 3 in the operation of the myocardial activity parameter conversion unit 240.

[0288] [Myocardial activity parameter conversion unit 240] As indicated by the two-dot chain lines in Figures 61 and 63, the myocardial activity parameter conversion unit 240 first obtains, for each myocardial activity parameter in each period, a representative value of the myocardial activity parameter values ​​for multiple periods including the period in question (step S240α). The method by which the myocardial activity parameter conversion unit 240 obtains the representative value is the same as in the second embodiment and its modification 4. Then, for each myocardial activity parameter in each period, the myocardial activity parameter conversion unit 240 performs step S240A of the second embodiment, step S240B of modification 1 of the second embodiment, step S240C of modification 2 of the second embodiment, or step S240D of modification 3 of the second embodiment using the representative value obtained in step S240α instead of the value of the myocardial activity parameter included in the input myocardial activity parameter set.

[0289] By using the signal conversion device 2 of the fifth modification of the second embodiment, it is possible to obtain a synthetic signal in which the influence of the desired physiological fluctuations is reduced and the features related to the desired activity and abnormality remain to a greater extent. In other words, by using the signal conversion device 2 of the fifth modification of the second embodiment, a user such as a doctor can confirm a corrected waveform in which the influence of the desired physiological fluctuations in the myocardial activity of the target heart is reduced and the features related to the desired activity and abnormality in the myocardial activity of the target heart remain to a greater extent.

[0290] <Sixth Modification of the Second Embodiment> A composite signal may be obtained by directly using the values ​​of the myocardial activity parameters obtained from the electrocardiogram waveform of the target heart without converting any of the myocardial activity parameters as performed by the myocardial activity parameter converter 240 in the second embodiment and its modifications 1 to 5. This modification will be referred to as modification 6 of the second embodiment, and the differences from the second embodiment will be mainly described.

[0291] Fig. 66 is a diagram showing an example of the functional configuration of the signal conversion device 2 of Modification 6 of the second embodiment. The signal conversion device 2 of Modification 6 of the second embodiment includes a signal analysis unit 210 and a waveform synthesis unit 250. The signal conversion device 2 of Modification 6 of the second embodiment differs from the signal conversion device 2 in that it does not include the converter acquisition unit 220, the conversion information setting unit 230, and the myocardial activity parameter conversion unit 240. The signal conversion device 2 of Modification 6 of the second embodiment performs steps S210 and S250 exemplified in Fig. 67. Step S210 performed by the signal analysis unit 210 of the signal conversion device 2 of Modification 6 of the second embodiment is similar to that of the signal conversion device 2 of the second embodiment.

[0292] As in the second embodiment, if the myocardial activity parameter set of the target heart has already been obtained, the myocardial activity parameter set of the target heart may be input instead of the electrocardiogram waveform of the target heart. In this case, the signal conversion device 2 does not need to include the signal analysis unit 210, and step S210 does not need to be performed. It can also be said that the signal conversion device 2 is a signal synthesis device 3 that obtains and outputs a synthesis signal from the myocardial activity parameter set.

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

[0294] The composite signal output by the signal conversion device 2 of the sixth modification of the second embodiment is not a waveform in which features other than those related to the desired activity or the desired abnormality have been significantly reduced, nor is it a waveform in which the influence of the desired physiological fluctuations has been reduced, but is a waveform generated by myocardial activity parameters that represent the main features of the myocardial activity of the target heart. That is, the composite signal output by the signal conversion device 2 of the sixth modification of the second embodiment is a waveform in which noise that is not the main feature of the myocardial activity and is included in the electrocardiogram waveform of the target heart has been reduced. That is, the signal conversion device 2 of the sixth modification of the second embodiment allows a user such as a doctor to check the corrected waveform in which the noise included in the electrocardiogram waveform of the target heart has been reduced.

[0295] All or part of the functions of the signal analyzing device 1 and / or the signal converting device 2 and / or the signal synthesizing device 3 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 the computer-readable recording medium 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.

[0296] 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]

[0297] 1...signal analysis device, 2...signal conversion device, 3...signal synthesis 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, 210...signal analysis unit, 220...converter acquisition unit, 230...conversion information setting unit, 240...myocardial activity parameter conversion unit, 250...waveform synthesis 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; a value of a parameter specifying the first unimodal distribution or a value of the first cumulative distribution function, a value of a parameter specifying the second unimodal distribution or a value of the second cumulative distribution function, a value of the first weight parameter, and a value of the first level parameter, when the first target time waveform is approximated by a first approximate time waveform, the first target time waveform being a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by a value of a second weight parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weight parameter, and adding the result to the first level parameter; a second approximated inverse-time waveform that is a waveform obtained by subtracting a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weight parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a third weight parameter, and adding the result to the function, the result being a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weight parameter, and the result being an addition of a second-level parameter; and The set of parameters is defined as a myocardial activity parameter set. Each element value included in the myocardial activity parameter set is defined as a myocardial activity parameter value, Using each myocardial activity parameter value included in the myocardial activity parameter set of a target heart (hereinafter referred to as "target heart"), a time waveform according to a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, is obtained as a composite waveform for the time interval of an R wave; obtaining a waveform obtained by reversing the time axis of a waveform obtained by subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weighting parameter, and adding the myocardial activity parameter value of the second level parameter to the subtracted function, as a composite waveform for the time interval of the T wave; a composite signal representing a waveform indicating a cardiac cycle of the target heart is obtained by connecting the composite waveform of the R wave time interval and the composite waveform of the T wave time interval; Waveform synthesis section, A signal synthesizer including:

2. 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 value of a parameter specifying the first unimodal distribution or a value of the first cumulative distribution function, a value of a parameter specifying the second unimodal distribution or a value of the second cumulative distribution function, a value of the first weight parameter, and a value of the first level parameter, when the first target time waveform is approximated by a first approximate time waveform, the first target time waveform being a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by a value of a second weight parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weight parameter, and adding the result to the first level parameter; a value of a parameter specifying the third unimodal distribution or a value of a parameter specifying the third cumulative distribution function, a value of the parameter specifying the fourth unimodal distribution or a value of a parameter specifying the fourth cumulative distribution function, a value of the third weight parameter, and a value of the second-level parameter, when the second target time waveform is approximated by a second approximate time waveform which is a time waveform obtained by subtracting from 1 a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a third weight parameter, and then adding the result to the subtracted function; The set of parameters is defined as a myocardial activity parameter set. Each element value included in the myocardial activity parameter set is defined as a myocardial activity parameter value, Using each myocardial activity parameter value included in the myocardial activity parameter set of a target heart (hereinafter referred to as "target heart"), a time waveform according to a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, is obtained as a composite waveform for the time interval of an R wave; obtaining a time waveform according to a function obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weighting parameter, and then adding the myocardial activity parameter value of the second level parameter to the resultant function, as a composite waveform for the time interval of the T wave; a composite signal representing a waveform indicating a cardiac cycle of the target heart is obtained by connecting the composite waveform of the R wave time interval and the composite waveform of the T wave time interval; Waveform synthesis section, A signal synthesizer including:

3. a myocardial activity parameter conversion unit that converts one or more myocardial activity parameters so that the myocardial activity parameter values ​​included in the myocardial activity parameter set of the target heart are closer to the myocardial activity parameter values ​​included in the myocardial activity parameter set of a standard electrocardiogram, thereby obtaining converted myocardial activity parameter values; a converter acquisition unit that acquires a converter that bijectively converts the myocardial activity parameter set of the target heart so that the myocardial activity parameter set of the target heart approaches the myocardial activity parameter set of the standard electrocardiogram; a conversion information setting unit for obtaining conversion information for identifying one or more types of myocardial activity parameters; further comprising The myocardial activity parameter conversion unit For the myocardial activity parameters identified by the conversion information, the myocardial activity parameter values ​​included in the myocardial activity parameter set of the target heart are directly obtained as the converted myocardial activity parameter values; For myocardial activity parameters that are not specified by the conversion information, the converter converts the myocardial activity parameter values ​​included in the myocardial activity parameter set of the target heart to obtain the converted myocardial activity parameter values; the waveform synthesis unit obtains a synthesis signal by using the converted myocardial activity parameter value instead of the myocardial activity parameter value included in the myocardial activity parameter set of the target heart.

3. A signal synthesizing device according to claim 1 or 2.

4. A myocardial activity parameter conversion unit that obtains converted myocardial activity parameter values ​​by converting one or more types of myocardial activity parameters so that the myocardial activity parameter values ​​included in the myocardial activity parameter set of the target heart are closer to the myocardial activity parameter values ​​included in the myocardial activity parameter set of a standard electrocardiogram; and a converter acquisition unit that acquires a converter that bijectively converts the myocardial activity parameter set of the target heart so that the myocardial activity parameter set of the target heart approaches the myocardial activity parameter set of the standard electrocardiogram; a conversion information setting unit for obtaining conversion information specifying a reflection weight value for each myocardial activity parameter; further comprising The myocardial activity parameter conversion unit For each myocardial activity parameter, a weighted average value of the myocardial activity parameter value included in the myocardial activity parameter set of the target heart and the value obtained by converting the myocardial activity parameter value included in the myocardial activity parameter set of the target heart using the reflection weight value as a weight to be assigned to the myocardial activity parameter value included in the myocardial activity parameter set of the target heart, is obtained as the converted myocardial activity parameter value; the waveform synthesis unit obtains a synthesis signal by using the converted myocardial activity parameter value instead of the myocardial activity parameter value included in the myocardial activity parameter set of the target heart.

3. A signal synthesizing device according to claim 1 or 2.

5. a myocardial activity parameter conversion unit that obtains, as a converted myocardial activity parameter value, a representative value of the myocardial activity parameter values ​​included in the myocardial activity parameter set for a plurality of cycles of the target heart for one or more types of myocardial activity parameters; further comprising The waveform synthesis unit obtaining a composite signal by using the converted myocardial activity parameter values ​​instead of the myocardial activity parameter values ​​included in the myocardial activity parameter set of the target heart; 3. A signal synthesizing device according to claim 1 or 2.

6. A signal synthesis method executed by a signal synthesis device, comprising: 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; a value of a parameter specifying the first unimodal distribution or a value of the first cumulative distribution function, a value of a parameter specifying the second unimodal distribution or a value of the second cumulative distribution function, a value of the first weight parameter, and a value of the first level parameter, when the first target time waveform is approximated by a first approximate time waveform, the first target time waveform being a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by a value of a second weight parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weight parameter, and adding the result to the first level parameter; a second approximated inverse-time waveform that is a waveform obtained by subtracting a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weight parameter from a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a third weight parameter, and adding the result to the function, the result being a function obtained by multiplying a fourth cumulative distribution function, which is a cumulative distribution function of a fourth unimodal distribution, by the value of a fourth weight parameter, and the result being an addition of a second-level parameter; and The set of parameters is defined as a myocardial activity parameter set. Each element value included in the myocardial activity parameter set is defined as a myocardial activity parameter value, Using each myocardial activity parameter value included in the myocardial activity parameter set of a target heart (hereinafter referred to as "target heart"), a time waveform according to a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, is obtained as a composite waveform for the time interval of an R wave; obtaining a waveform obtained by reversing the time axis of a waveform obtained by subtracting a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the fourth unimodal distribution or the fourth cumulative distribution function by the myocardial activity parameter value of the fourth weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weighting parameter, and adding the myocardial activity parameter value of the second level parameter to the subtracted function, as a composite waveform for the time interval of the T wave; a composite signal representing a waveform indicating a cardiac cycle of the target heart is obtained by connecting the composite waveform of the R wave time interval and the composite waveform of the T wave time interval; a waveform synthesis step; A signal synthesis method comprising:

7. A signal synthesis method executed by a signal synthesis device, comprising: 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 value of a parameter specifying the first unimodal distribution or a value of the first cumulative distribution function, a value of a parameter specifying the second unimodal distribution or a value of the second cumulative distribution function, a value of the first weight parameter, and a value of the first level parameter, when the first target time waveform is approximated by a first approximate time waveform, the first target time waveform being a time waveform obtained by subtracting a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by a value of a second weight parameter from a function obtained by multiplying a first cumulative distribution function, which is a cumulative distribution function of a first unimodal distribution, by the value of a first weight parameter, and adding the result to the first level parameter; a value of a parameter specifying the third unimodal distribution or a value of a parameter specifying the third cumulative distribution function, a value of the parameter specifying the fourth unimodal distribution or a value of a parameter specifying the fourth cumulative distribution function, a value of the third weight parameter, and a value of the second-level parameter, when the second target time waveform is approximated by a second approximate time waveform which is a time waveform obtained by subtracting from 1 a function obtained by multiplying a third cumulative distribution function, which is a cumulative distribution function of a third unimodal distribution, by the value of a third weight parameter, and then adding the result to the subtracted function; The set of parameters is defined as a myocardial activity parameter set. Each element value included in the myocardial activity parameter set is defined as a myocardial activity parameter value, Using each myocardial activity parameter value included in the myocardial activity parameter set of a target heart (hereinafter referred to as "target heart"), a time waveform according to a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the second unimodal distribution or the second cumulative distribution function by the myocardial activity parameter value of the second weighting parameter from a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the first unimodal distribution or the first cumulative distribution function by the myocardial activity parameter value of the first weighting parameter, and adding the myocardial activity parameter value of the first level parameter to the function, is obtained as a composite waveform for the time interval of an R wave; obtaining a time waveform according to a function obtained by subtracting from 1 a function obtained by multiplying a cumulative distribution function specified by the myocardial activity parameter value of a parameter specifying the third unimodal distribution or the third cumulative distribution function by the myocardial activity parameter value of the third weighting parameter, and then adding the myocardial activity parameter value of the second level parameter to the resultant function, as a composite waveform for the time interval of the T wave; a composite signal representing a waveform indicating a cardiac cycle of the target heart is obtained by connecting the composite waveform of the R wave time interval and the composite waveform of the T wave time interval; a waveform synthesis step; A signal synthesis method comprising:

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

Images