Signal analysis device, signal analysis method, and program

Abstract

The signal analysis device comprises: a biological information acquisition unit, which acquires a waveform of a time interval of an R wave included in a waveform representing one cardiac cycle of a heart to be analyzed as an object time waveform; and an analysis unit, which acquires, as a parameter representing the activity of the myocardium of the heart, at least any one of at least a portion of the parameters of a first unimodal distribution, at least a portion of the parameters of a first cumulative distribution function, at least a portion of the parameters of a second unimodal distribution, and at least a portion of the parameters of a second cumulative distribution function when approximating the object time waveform by a time waveform generated by a difference or a weighted difference between a first cumulative distribution function and a second cumulative distribution function, or a time waveform generated by adding a level value to the difference or a weighted difference between the first cumulative distribution function and the second cumulative distribution function, i.e., an approximate time waveform.

Description

Technical Field

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

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

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

[0004] Prior art literature

[0005] Non-patent literature

[0006] Non-patent literature 1: Hiroshi Tanaka, "Methodology of the Electrocardiographic Inverse Problem", Medical Electronics and Bioengineering, 1985, Volume 23, No. 3, p.147-158 Summary of the Invention

[0007] Problems to be solved by the invention

[0008] However, the waveform of the electrocardiogram is not always sufficient to understand the condition of the heart. For example, even if the waveform of the electrocardiogram is similar, the pathogenesis of heart-related diseases is sometimes different. Thus, depending on the disease, it is sometimes difficult to understand the condition of the heart by simply observing the waveform of the electrocardiogram itself. For example, in the case of heart failure, the onset of the disease can be suppressed by observing the condition of the heart based on the waveform of the electrocardiogram in daily life. In order to further improve the accuracy of suppressing the onset of the disease, it is possible to consider using other technologies such as blood sampling to obtain other information, but this is not realistic in daily life. Therefore, depending on the disease, it is sometimes necessary to essentially understand the condition of the heart based only on the waveform of the electrocardiogram.

[0009] Furthermore, this situation is not limited to cases where the heart's condition is understood based on the electrocardiogram waveform. This situation is also common in cases where the heart's condition is understood based on a single channel of time-series biological information related to cardiac pulsation, acquired by sensors in contact with the body's surface, sensors close to the body's surface, sensors inserted into the body, sensors embedded in the body, and the like. Furthermore, examples of time-series biological information related to cardiac pulsation include waveforms showing changes in cardiac potential, changes in cardiac pressure, changes in blood flow, and changes in heart sounds. Furthermore, the electrocardiogram waveform is also an example of time-series biological information related to cardiac pulsation.

[0010] In view of the above circumstances, an object of the present invention is to provide a technology for obtaining useful information for understanding the cardiac state from one channel of time-series biological information related to cardiac pulsation.

[0011] Solutions to Problems

[0012] One embodiment of the present invention is a signal analysis device, comprising: a biological information acquisition unit that acquires a waveform of a time interval of an R wave included in a waveform representing one cardiac cycle of a heart to be analyzed as a target time waveform; and an analysis unit that acquires, as a parameter representing the activity of the myocardium of the heart, at least one of at least a portion of parameters that determine the first unimodal distribution, at least a portion of parameters that determine the first cumulative distribution function, at least a portion of parameters that determine the second unimodal distribution, and at least a portion of parameters that determine the second cumulative distribution function when approximating the target time waveform by a time waveform that is a difference or weighted difference between a first unimodal distribution cumulative distribution function, i.e., a first cumulative distribution function, and a second unimodal distribution cumulative distribution function, i.e., an approximate time waveform, or a time waveform that is a result of adding a level value to the difference or weighted difference between the first unimodal distribution cumulative distribution function, i.e., a first cumulative distribution function, and a second unimodal distribution cumulative distribution function, i.e., an approximate time waveform.

[0013] This makes it possible to provide a technique for obtaining useful information for understanding the cardiac state from one channel of time-series biological information related to cardiac pulsation.

[0014] One embodiment of the present invention is a signal analysis device comprising: a biological information acquisition unit that acquires, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and an analysis unit that, when approximating the target inverse time waveform by a waveform obtained by inverting the time axis of the target time waveform and adding a level value to a waveform obtained by approximating the target inverse time waveform, acquires, as a parameter representing the activity of the myocardium of the heart, at least one of at least some of the parameters that determine the third unimodal distribution, at least some of the parameters that determine the third cumulative distribution function, at least some of the parameters that determine the fourth unimodal distribution, and at least some of the parameters that determine the fourth cumulative distribution function.

[0015] One embodiment of the present invention is a signal analysis device comprising: a biological information acquisition unit that acquires, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and an analysis unit that uses a cumulative distribution function of a third unimodal distribution as a third cumulative distribution function, a cumulative distribution function of a fourth unimodal distribution as a fourth cumulative distribution function, a function obtained by subtracting the third cumulative distribution function from 1 as a third inverse cumulative distribution function, and a function obtained by subtracting the fourth cumulative distribution function from 1 as a fourth inverse cumulative distribution function, to obtain a signal obtained by combining the third inverse cumulative distribution function with the fourth inverse cumulative distribution function. When the object time waveform is approximated by the time waveform generated by the difference or weighted difference of the inverse cumulative distribution functions, i.e., the approximate time waveform, or the time waveform generated by adding the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function to the level value, at least any one of the parameters for determining at least a part of the parameters of the third unimodal distribution, the parameters for determining at least a part of the parameters of the third cumulative distribution function, the parameters for determining at least a part of the parameters of the fourth unimodal distribution, and the parameters for determining at least a part of the parameters of the fourth cumulative distribution function is used as a parameter showing the activity of the myocardium of the heart.

[0016] One embodiment of the present invention is a signal analysis method, which includes: a biological information acquisition step of acquiring a waveform of a time interval of an R wave included in a waveform representing one cardiac cycle of a heart to be analyzed as a target time waveform; and an analysis step of acquiring, as a parameter representing the activity of the myocardium of the heart, at least any one of at least a portion of parameters of the first unimodal distribution, at least a portion of parameters of the first cumulative distribution function, at least a portion of parameters of the second unimodal distribution, and at least a portion of parameters of the second cumulative distribution function when approximating the target time waveform by a time waveform (i.e., an approximate time waveform) generated by a difference or weighted difference between a first unimodal distribution cumulative distribution function, i.e., a first cumulative distribution function, and a second unimodal distribution cumulative distribution function, i.e., a second cumulative distribution function, or a time waveform (i.e., an approximate time waveform) obtained by adding a level value to a difference or weighted difference between a first unimodal distribution cumulative distribution function, i.e., a first cumulative distribution function, and a second unimodal distribution cumulative distribution function, as a time waveform generated by approximating the target time waveform.

[0017] This makes it possible to provide a technique for obtaining useful information for understanding the cardiac state from one channel of time-series biological information related to cardiac pulsation.

[0018] One embodiment of the present invention is a signal analysis method, comprising: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and an analysis step of acquiring, as a target inverse time waveform, a waveform obtained by inverting the time axis of the target time waveform, and acquiring, as a parameter representing myocardial activity of the heart, at least one of at least a portion of parameters of the third unimodal distribution, at least a portion of parameters of the third cumulative distribution function, at least a portion of parameters of the fourth unimodal distribution, and at least a portion of parameters of the fourth cumulative distribution function when approximating the target inverse time waveform, a waveform generated by a difference or weighted difference between a third cumulative distribution function (i.e., a third cumulative distribution function) and a fourth cumulative distribution function (i.e., a fourth cumulative distribution function) or a waveform generated by adding a level value to the difference or weighted difference between the third cumulative distribution function (i.e., a third cumulative distribution function) and a fourth cumulative distribution function (i.e., a fourth cumulative distribution function) to approximate the target inverse time waveform.

[0019] One embodiment of the present invention is a signal analysis method, which includes: a biological information acquisition step, acquiring a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed as a target time waveform; and an analysis step, using a cumulative distribution function of a third unimodal distribution as a third cumulative distribution function, using a cumulative distribution function of a fourth unimodal distribution as a fourth cumulative distribution function, subtracting the third cumulative distribution function from 1 as a third inverse cumulative distribution function, and subtracting the fourth cumulative distribution function from 1 as a fourth inverse cumulative distribution function, to obtain a signal obtained by combining the third inverse cumulative distribution function with the first inverse cumulative distribution function. When the object time waveform is approximated by the time waveform generated by the difference or weighted difference of four inverse cumulative distribution functions, or the time waveform generated by adding the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function to the level value, at least any one of the parameters of at least part of the parameters of the third unimodal distribution, the parameters of at least part of the parameters of the third cumulative distribution function, the parameters of at least part of the parameters of the fourth unimodal distribution, and the parameters of at least part of the parameters of the fourth cumulative distribution function is used as a parameter to show the activity of the myocardium of the heart.

[0020] One embodiment of the present invention is a program for causing a computer to function as the above-mentioned signal analysis device.

[0021] This makes it possible to provide a technique for obtaining useful information for understanding the cardiac state from one channel of time-series biological information related to cardiac pulsation.

[0022] That is, it is possible to provide a computer program that obtains useful information for understanding the cardiac state from one channel of time-series biological information related to cardiac pulsation.

[0023] Effects of the Invention

[0024] According to the present invention, it is possible to provide a technique for obtaining useful information for understanding the cardiac state from one channel of time-series biological information related to cardiac pulsation. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0026] Figure 2 The diagram schematically shows, for the first target time waveform, a function obtained by multiplying the first cumulative distribution function by a weight, a function obtained by multiplying the second cumulative distribution function by a weight, and an approximate time waveform which is a weighted difference between the first and second cumulative distribution functions.

[0027] Figure 3 This diagram schematically shows, with respect to the second target time waveform, 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 a weighted difference between the third and fourth inverse cumulative distribution functions, i.e., an approximate time waveform.

[0028] Figure 4 1 is a diagram showing an example of the results of fitting the waveform of the electrocardiogram of the target heart according to the embodiment using four cumulative distribution functions.

[0029] Figure 5 This is an explanatory diagram explaining that the difference between two cumulative distribution functions in the embodiment can be fitted to a substantially identical waveform with the waveform of the falling T wave.

[0030] Figure 6 It is a diagram schematically showing, with respect to the time waveform of the first object, a function of the first cumulative distribution function multiplied by a weight and added with a level value, a function of the second cumulative distribution function multiplied by a weight and added with a level value, and an approximate time waveform of the weighted difference between the first cumulative distribution function and the second cumulative distribution function plus the level value.

[0031] Figure 7 It is a diagram schematically showing the approximate time waveform of the second object, a function of the third inverse cumulative distribution function multiplied by a weight and added with a level value, a function of the fourth inverse cumulative distribution function multiplied by a weight and added with a level value, and the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function plus the level value.

[0032] Figure 8 is a diagram schematically showing a delta wave included in a target time waveform.

[0033] Figure 9 1 is a diagram showing an example of the functional configuration of the control unit 11 in the embodiment.

[0034] Figure 10 This is a flowchart showing an example of a process flow executed by the signal analysis device 1 according to the embodiment.

[0035] Figure 11 This is a first diagram showing an example of analysis results by the signal analysis device 1 according to the embodiment.

[0036] Figure 12 The second diagram shows an example of analysis results by the signal analysis device 1 according to the embodiment.

[0037] Figure 13 The third diagram shows an example of analysis results by the signal analysis device 1 according to the embodiment.

[0038] Figure 14 FIG4 is a fourth diagram showing an example of analysis results by the signal analysis device 1 according to the embodiment.

[0039] Figure 15 This is a first explanatory diagram of an example of analysis of an electrocardiogram of a ventricular extrasystole by the signal analysis device 1 according to the embodiment.

[0040] Figure 16 This is a second explanatory diagram of an example of analysis of an electrocardiogram of a ventricular extrasystole by the signal analysis device 1 according to the embodiment.

[0041] Figure 17 This is a third explanatory diagram of an example of analysis of an electrocardiogram of a ventricular extrasystole by the signal analysis device 1 according to the embodiment.

[0042] Figure 18 This is a first explanatory diagram showing analysis of an electrocardiogram of a subject's heart during the depolarization phase of Brugada syndrome type 1 by the signal analysis device 1 according to the embodiment.

[0043] Figure 19 This is a second explanatory diagram showing an electrocardiogram of a subject's heart in the depolarization phase of Brugada syndrome type 1 analyzed by the signal analysis device 1 according to the embodiment.

[0044] Figure 20 This is a third explanatory diagram showing analysis of an electrocardiogram of a subject's heart during the depolarization phase of Brugada syndrome type 1 by the signal analysis device 1 according to the embodiment.

[0045] Figure 21 This is a diagram showing a first example of an electrocardiogram analyzed by the signal analysis device 1 according to the embodiment.

[0046] Figure 22 This is a diagram showing a second example of an electrocardiogram analyzed by the signal analysis device 1 according to the embodiment.

[0047] Figure 23 This is a diagram showing a third example of an electrocardiogram analyzed by the signal analysis device 1 according to the embodiment.

[0048] Figure 24 This is a diagram showing a fourth example of electrocardiogram analysis by the signal analysis device 1 according to the embodiment.

[0049] Figure 25 This is a diagram showing a fifth example of an electrocardiogram analyzed by the signal analysis device 1 according to the embodiment.

[0050] Figure 26 This is a diagram showing a sixth example of electrocardiogram analysis by the signal analysis device 1 according to the embodiment.

[0051] Figure 27 This is a diagram showing a seventh example of electrocardiogram analysis by the signal analysis device 1 according to the embodiment.

[0052] Figure 28 This is a diagram showing an eighth example of electrocardiogram analysis by the signal analysis device 1 according to the embodiment.

[0053] Figure 29 This is a diagram showing a ninth example of electrocardiogram analysis by the signal analysis device 1 according to the embodiment.

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

[0055] Figure 31 This is a diagram showing an eleventh example of electrocardiogram analysis by the signal analysis device 1 according to the embodiment.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0074] Figure 50 This is a diagram showing the thirtieth example of electrocardiogram analyzed by the signal analysis device 1 according to the embodiment.

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

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

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

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

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

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

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

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

[0083] Figure 59 This is a diagram showing a thirty-ninth example of an electrocardiogram analyzed by the signal analysis device 1 according to the embodiment. DETAILED DESCRIPTION

[0084] Figure 1 : is a diagram showing an example of the hardware structure of the signal analysis device 1 according to the embodiment. For simplicity of explanation, the signal analysis device 1 is described below by taking the case of performing analysis based on the waveform of one channel of the electrocardiogram as an example. However, the signal analysis device 1 is not limited to the waveform of the electrocardiogram, and can perform the same analysis based on biological information of a time series related to cardiac pulsation. Furthermore, biological information of a time series related to cardiac pulsation is, for example, a waveform showing changes in cardiac potential, a waveform showing changes in cardiac pressure, a waveform showing changes in blood flow, and a waveform showing changes in heart sounds. Therefore, the signal analysis device 1 is not limited to the waveform of an electrical signal obtained from the surface of the body, and can use any waveform as long as it is a waveform showing the cardiac cycle obtained from a point somewhere on the surface of the body or inside the body using a sensor in contact with the surface of the body, a sensor close to the surface of the body, a sensor inserted into the body, a sensor embedded in the body, or the like.

[0085] That is, the signal analysis device 1 may use a waveform showing changes in cardiac pressure instead of an electrocardiogram waveform as the biological information of a time series related to cardiac pulsation. Furthermore, the signal analysis device 1 may use a waveform showing changes in blood flow instead of an electrocardiogram waveform as the biological information of a time series related to cardiac pulsation. Furthermore, the signal analysis device 1 may use a waveform showing changes in heart sounds instead of an electrocardiogram waveform as the biological information of a time series related to cardiac pulsation. Furthermore, the electrocardiogram waveform is also an example of biological information of a time series related to cardiac pulsation. Furthermore, biological information of a time series related to cardiac pulsation may also be biological information of a time series related to the periodic pulsation of the heart.

[0086] A signal analysis device 1 obtains an electrocardiogram waveform of a heart to be analyzed (hereinafter referred to as the "target heart"). Based on the obtained electrocardiogram waveform, the signal analysis device 1 obtains parameters indicating the activity of the myocardium of the target heart (hereinafter referred to as "myocardial activity parameters"), including at least one of a parameter indicating the activity of a layer outside the myocardium of the target heart (hereinafter referred to as the "extramyocardial layer") (hereinafter referred to as the "extramyocardial layer parameter") and a parameter indicating the activity of a layer inside the myocardium of the target heart (hereinafter referred to as the "intramyocardial layer") (hereinafter referred to as the "intramyocardial layer parameter").

[0087] Here, we will explain the relationship between myocardial activity and electrocardiogram waveforms. In the medical field, a model known as the cardiac electromotive force dipole model (Reference 1) is known for describing the relationship between myocardial activity and electrocardiograms. According to the cardiac electromotive force dipole model, the myocardium is modeled as two layers: the outer myocardium and the inner myocardium.

[0088] Reference 1: Tanaka Yoshifumi, "The establishment and understanding of the electrocardiogram waveform and the activity potential of the heart muscles," Gakuken Mikaru Hidejunsha (2012).

[0089] In the cardiac electromotive force dipole model, the outer and inner myocardial layers are modeled as different sources of electromotive force. According to the cardiac electromotive force dipole model, the composite wave of the epicardial and endocardial myocardial action potentials roughly corresponds to the temporal variation of the body surface potential observed on the body surface. The graph representing the temporal variation of the body surface potential is the electrocardiogram waveform. The epicardial myocardial action potential is the result of directly measuring the variation of the electromotive force generated by the pulsation of the outer myocardium through the insertion of a catheter electrode. The endocardial myocardial action potential is the result of directly measuring the variation of the electromotive force generated by the pulsation of the inner myocardium through the insertion of a catheter electrode. This is a brief description of the cardiac electromotive force dipole model.

[0090] However, the outer myocardium in the cardiac electromotive force dipole model is a collection of cells. Therefore, the timing of the beats of the outer myocardium cells during a single beat is not necessarily the same for all cells, and there is a possibility of a distribution in the beat timing. The same is true for the inner myocardium. In other words, the timing of the beats of the inner myocardium cells during a single beat is not necessarily the same for all cells, and there is a possibility of a distribution in the beat timing. However, the cardiac electromotive force dipole model does not assume this possibility of a distribution in the beat timing of the cells.

[0091] Furthermore, there is a distribution in the distance between each cell and the electrodes located on the body surface, and the structure of the body tissue between each cell and the electrodes located on the body surface also varies. Therefore, the conversion efficiency of the excitation of cells in the outer myocardium, as reflected in the electrocardiogram waveform, may not be the same for all cells, and there may be a distribution in the conversion efficiency of the pulsation reflected in the electrocardiogram waveform. Similarly, the conversion efficiency of cells in the inner myocardium, as reflected in the electrocardiogram waveform, may not be the same for all cells, and there may be a distribution in the conversion efficiency of the pulsation reflected in the electrocardiogram waveform. However, the cardiac electromotive force dipole model does not assume that there may be a distribution in the conversion efficiency of the pulsation of cells reflected in the electrocardiogram waveform.

[0092] In the signal analysis device 1, taking into account the possibility of distribution in the timing of cell pulsation and the possibility of distribution in the conversion efficiency of cell pulsation reflected in the waveform of the electrocardiogram, analysis is performed assuming that the distribution of the timing of the start of pulsation of each cell in the outer layer of the myocardium as expressed in the waveform of the electrocardiogram, the distribution of the timing of the start of pulsation of each cell in the inner layer of the myocardium as expressed in the waveform of the electrocardiogram, the distribution of the timing of the end of pulsation of each cell in the outer layer of the myocardium as expressed in the waveform of the electrocardiogram, and the distribution of the timing of the end of pulsation of each cell in the inner layer of the myocardium as expressed in the waveform of the electrocardiogram are each Gaussian distributions. That is, in the signal analysis device 1, analysis is performed assuming that the start of the activity of the inner myocardium caused by all cells of the inner myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, the start of the activity of the outer myocardium caused by all cells of the outer myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, the end of the activity of the inner myocardium caused by all cells of the inner myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution, and the end of the activity of the outer myocardium caused by all cells of the outer myocardium is included in the waveform of the electrocardiogram as a cumulative Gaussian distribution.

[0093] Furthermore, while the signal analysis device 1 can use a cumulative Gaussian distribution function, a Sigmoid function, a Gompertz function, a logistic function, or the like can also be used in place of the cumulative Gaussian distribution function. Specifically, the signal analysis device 1 can use a unimodal cumulative distribution function instead of the cumulative Gaussian distribution function, i.e., a cumulative distribution function corresponding to a distribution in which the value monotonically increases before reaching its maximum value and monotonically decreases after reaching its maximum value. However, the cumulative distribution function used by the signal analysis device 1 must be one whose shape can be determined by parameters representing the shape of the cumulative distribution function or by parameters representing the shape of the unimodal distribution that is the source of the cumulative distribution function. Hereinafter, parameters representing the shape of the cumulative distribution function (i.e., parameters that determine the cumulative distribution function) will be referred to as the shape parameters of the cumulative distribution function, and parameters representing the shape of the unimodal distribution (i.e., parameters that determine the unimodal distribution) will be referred to as the shape parameters of the unimodal distribution. However, the shape parameters of the cumulative distribution function and the shape parameters of the unimodal distribution are, of course, essentially the same. For example, if the cumulative distribution function used by the signal analysis device 1 is a cumulative Gaussian distribution function, the standard deviation (or variance) and mean value of the Gaussian distribution as the cumulative source of the cumulative Gaussian distribution function are shape parameters of the unimodal distribution and are also shape parameters of the cumulative distribution function.

[0094] The signal analysis device 1 uses the waveform of either the R wave or the T wave time interval included in the waveform of one cycle of the acquired electrocardiogram of the target heart as the target time waveform. When approximating the target time waveform using a time waveform generated by the difference or weighted difference between a first cumulative distribution function (a first unimodal distribution) and a second cumulative distribution function (a second unimodal distribution) (hereinafter referred to as an "approximate time waveform"), the parameters defining the first unimodal distribution or the first cumulative distribution function and the parameters defining the second unimodal distribution or the second cumulative distribution function are obtained as parameters representing the characteristics of the target time waveform, i.e., as myocardial activity parameters. Hereinafter, approximating the target time waveform using the approximate time waveform, i.e., determining the approximate time waveform, is referred to as "fitting," and the first and second cumulative distribution functions included in the approximate time waveform are referred to as "fitting results." Furthermore, when the signal analysis device 1 approximates by weighted difference, the weight assigned to the first cumulative distribution function and the weight assigned to the second cumulative distribution function can be obtained as parameters representing the characteristics of the object time waveform (i.e., myocardial activity parameters), and the ratio of the weight assigned to the first cumulative distribution function to the weight assigned to the second cumulative distribution function can be obtained as a parameter representing the characteristics of the object time waveform (i.e., myocardial activity parameters).

[0095] When approximating a target time waveform using an approximate time waveform generated by the difference between a first cumulative distribution function and a second cumulative distribution function, for example, the signal analysis device 1 generates a time waveform generated by the difference between the first and second cumulative distribution functions (hereinafter referred to as a "candidate time waveform") using combinations (M×N) of parameters defining the cumulative distribution function for each of a plurality (M) candidates for the first cumulative distribution function and parameters defining the cumulative distribution function for each of a plurality (N) candidates for the second cumulative distribution function. The candidate time waveform closest to the target time waveform among the generated M×N candidate time waveforms is determined as the approximate time waveform. The parameters defining the first and second candidates used in generating the determined approximate time waveform are used as parameters representing the characteristics of the target time waveform. The process of determining the candidate time waveform closest to the target time waveform as the approximate time waveform can be performed, for example, by determining the candidate time waveform that minimizes the squared error between the candidate time waveform and the target time waveform.

[0096] Alternatively, for example, the signal analysis device 1 repeatedly performs an operation of obtaining a candidate time waveform as a time waveform generated by the difference between a candidate for a first cumulative distribution function and a candidate for a second cumulative distribution function that approximates the object time waveform, and an operation of updating at least any one of the parameters of each cumulative distribution function in a direction in which the square error between the candidate time waveform and the object time waveform becomes smaller until the square error becomes below a prescribed benchmark, or repeats the above operation a prescribed number of times, thereby determining the candidate time waveform finally obtained as the approximate time waveform, and obtaining the parameters for determining the candidate for the first cumulative distribution function and the parameters for determining the candidate for the second cumulative distribution function used in generating the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0097] When the target time waveform is approximated by an approximate time waveform generated by 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 that is a time waveform generated by a weighted difference between the first and second cumulative distribution functions, using combinations (K×L×M×N types) of parameters defining the cumulative distribution function for each of the plurality (M) candidates for the first cumulative distribution function, parameters defining the cumulative distribution function for each of the plurality (N) candidates for the second cumulative distribution function, a plurality (K) candidates for weights assigned to the first cumulative distribution function, and a plurality (L) candidates for weights assigned to the second cumulative distribution function. The signal analysis device 1 determines, as the approximate time waveform, the candidate time waveform that is closest to the target time waveform among the generated K×L×M×N candidate time waveforms. The parameters defining the first cumulative distribution function candidates, the parameters defining the second cumulative distribution function candidates, the weights assigned to the first cumulative distribution function, and the weights assigned to the second cumulative distribution function used in generating the determined approximate time waveform are acquired as parameters representing the characteristics of the target time waveform.

[0098] Alternatively, for example, the signal analysis device 1 repeatedly performs an operation of obtaining a candidate time waveform as a time waveform generated by a weighted difference between a candidate for a first cumulative distribution function and a candidate for a second cumulative distribution function that approximates the object time waveform, and updates at least any one of the parameters for determining each cumulative distribution function and the weight assigned to each cumulative distribution function in a direction in which the square error between the candidate time waveform and the object time waveform becomes smaller until the square error becomes below a prescribed benchmark, or repeats the above operation a prescribed number of times, thereby determining the candidate time waveform finally obtained as the approximate time waveform, and obtaining the parameters for determining the candidate for the first cumulative distribution function, the parameters for determining the candidate for the second cumulative distribution function, the weight assigned to the first cumulative distribution function, and the weight assigned to the second cumulative distribution function used in generating the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0099] Hereinafter, the process of acquiring parameters representing the characteristics of a target time waveform included in the acquired waveform corresponding to one cycle of the electrocardiogram of the target heart is referred to as myocardial activity information parameter acquisition processing.

[0100] If the information representing the moment is set to x, when using Gaussian distribution as the first unimodal distribution and the second unimodal distribution, the first unimodal distribution is expressed by the following formula (1), the first cumulative distribution function f1(x) is expressed by formula (2), the second unimodal distribution is expressed by formula (3), and the second cumulative distribution function f2(x) is expressed by formula (4).

[0101] [Formula 1]

[0102]

[0103] [Formula 2]

[0104]

[0105] [Formula 3]

[0106]

[0107] [Formula 4]

[0108]

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

[0110] The difference between the first cumulative distribution function and the second cumulative distribution function is expressed by, for example, the following equation (5): The function expressed by the following equation (5) is a function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0111] [Formula 5]

[0112]

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

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

[0115] [Formula 6]

[0116]

[0117] That is, when approximating the target time waveform using an approximating time waveform that is the weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least the parameters defining the first unimodal distribution or the parameters defining the first cumulative distribution function, namely, the mean μ1 and the standard deviation σ1, and the parameters defining the second unimodal distribution or the parameters defining the second cumulative distribution function, namely, the mean μ2 and the standard deviation σ2, are acquired as parameters representing the characteristics of the target time waveform. Furthermore, the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function, or the ratio (k1 / k2 or k2 / k1) of the weight k1 of the first cumulative distribution function to the weight k2 of the second cumulative distribution function, may also be acquired as parameters representing the characteristics of the target time waveform.

[0118] If the function in equation (6) is the 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, then both weights k1 and k2 are positive. However, if the subject's heart is in a specific state, the possibility that at least one of the weights k1 and k2 obtained by fitting is not positive cannot be ruled out. Therefore, while the signal analysis device 1 can fit so that both weights k1 and k2 are positive, it is not necessary to fit so that both weights k1 and k2 are positive.

[0119] Furthermore, the signal analysis device 1 can use the R wave and the T wave included in the waveform of one cycle of the electrocardiogram of the target heart as the first target time waveform and the T wave as the second target time waveform, and obtain the above-mentioned parameters representing the characteristics of the target time waveform for the first target time waveform and the second target time waveform, respectively.

[0120] For example, when the first target time waveform (i.e., R wave) is approximated by the difference between the first cumulative distribution function and the second cumulative distribution function, the first target time waveform is approximated by the approximate time waveform of formula (9), and the parameter of the first cumulative distribution function, i.e., the mean μ, is determined. a and standard deviation σ a , and determine the parameter of the second cumulative distribution function, namely the mean μ band standard deviation σ b The parameters representing the characteristics of the first target time waveform (ie, R wave) are obtained. The above formula (9) is obtained from the first cumulative distribution function f represented by formula (7). a (x) minus the second cumulative distribution function f expressed by equation (8) b (x) and the function obtained.

[0121] [Formula 7]

[0122]

[0123] [Formula 8]

[0124]

[0125] [Formula 9]

[0126]

[0127] For example, when the first target time waveform (i.e., R wave) is approximated by the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the first target time waveform is approximated by the approximation time waveform of formula (10), and at least the parameter of the first cumulative distribution function, i.e., the mean μ, is determined. a and standard deviation σ a , and determine the parameter of the second cumulative distribution function, namely the mean μ b and standard deviation σ b The parameters representing the characteristics of the first target time waveform (ie, R wave) are obtained. The above formula (10) is obtained from the first cumulative distribution function f represented by formula (7). a (x) multiplied by weight k a The second cumulative distribution function f expressed by formula (8) is subtracted from the function b (x) multiplied by weight k b Furthermore, the weight k of the first cumulative distribution function can also be a and the weight k of the second cumulative distribution function b , or the weight k of the first cumulative distribution function a and the weight k of the second cumulative distribution function b The ratio (k a / k b or k b / k a ) is also obtained as a parameter representing the characteristics of the parameter representing the characteristics of the first target time waveform (ie, R wave).

[0128] [Formula 10]

[0129]

[0130] If the function from equation (10) is the first cumulative distribution function multiplied by the weight k a The second cumulative distribution function is subtracted from the function multiplied by the weight k b The weight k is a and weight k b However, if the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. a and weight k b Therefore, the signal analysis device 1 can be fitted so that the weight k a and weight k b are all positive values, but it is not necessary to fit them so that the weight k a and weight k b All are positive values.

[0131] Since the R wave corresponds to the excitation of all myocardial cells starting in sequence according to the Gaussian distribution, for the R wave, it is sufficient to approximate the target time waveform in the positive direction of time by the approximate time waveform of the difference or weighted difference of two cumulative Gaussian distributions as described above. On the other hand, if it is seen from the perspective that the excitation of all myocardial cells corresponding to the T wave awakens in sequence according to the Gaussian distribution, it can be interpreted as a phenomenon that the T wave is in the opposite direction of the R wave on the time axis. That is, for the T wave, it is sufficient to approximate the waveform after the time axis of the target time waveform is reversed by the difference or weighted difference of the cumulative Gaussian distribution. This will be referred to as the first method below. In addition, if it is seen from the perspective that the T wave corresponds to the excitation of all myocardial cells awakening from the state according to the Gaussian distribution, it can also be said that for the T wave, it is sufficient to approximate the target time waveform in the positive direction of time by the difference or weighted difference of two functions (the function obtained by subtracting the cumulative Gaussian distribution from 1). This will be referred to as the second method below. The following describes specific examples of the first method and the second method. In order to avoid confusion between the above-mentioned cumulative distribution function for the R wave and the cumulative distribution function for the T wave described below, the above-mentioned first cumulative distribution function will be referred to as the third cumulative distribution function, and the above-mentioned second cumulative distribution function will be referred to as the fourth cumulative distribution function for the T wave.

[0132] When the first method is used to approximate the second target time waveform (i.e., T wave) by the difference between the third cumulative distribution function and the fourth cumulative distribution function, the information representing the time in the reverse direction is set to x′, and the waveform after the time axis of the second target time waveform is reversed is called the second target inverse time waveform. The second target inverse time waveform is approximated by the approximate inverse time waveform of formula (13), and the parameter of the third cumulative distribution function, i.e., the mean μ, is determined. e and standard deviation σ e , and determine the parameters of the fourth cumulative distribution function, namely the mean μ g and standard deviation σg The parameters representing the characteristics of the second target time waveform (ie, T wave) are obtained. The above formula (13) is obtained from the third cumulative distribution function f represented by formula (11). e (x′) minus the fourth cumulative distribution function f expressed by equation (12) g (x′) is the function obtained.

[0133] [Formula 11]

[0134]

[0135] [Formula 12]

[0136]

[0137] [Formula 13]

[0138]

[0139] For example, when the first method is used to approximate the second object time waveform (i.e., T wave) by the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, the second object inverse time waveform is approximated by the approximate inverse time waveform of formula (14), and at least the parameter of the third cumulative distribution function, i.e., the mean μ, is determined. e and standard deviation σ e , and determine the parameters of the fourth cumulative distribution function, namely the mean μ g and standard deviation σ g The parameters representing the characteristics of the second target time waveform (ie, T wave) are obtained. The above formula (14) is obtained from the third cumulative distribution function f represented by formula (11). e (x′) multiplied by the weight k e Subtract the fourth cumulative distribution function f expressed by formula (12) from the function g (x′) multiplied by the weight k g Furthermore, the weight k of the third cumulative distribution function can also be e and the weight k of the fourth cumulative distribution function g , or the weight k of the third cumulative distribution function e With the weight k of the fourth cumulative distribution function g The ratio (k e / k g or k g / k e ) is also obtained as a parameter representing the characteristics of the second object time waveform (ie, T wave).

[0140] [Formula 14]

[0141]

[0142] If the function from equation (14) is the third cumulative distribution function multiplied by the weight k e The function of subtracting the fourth cumulative distribution function multiplied by the weight k g The weight k is the function obtained by the function e and weight k g However, if the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. e and weight k g Therefore, the signal analysis device 1 can be fitted so that the weight k e and weight k g are all positive values, but it is not necessary to fit them so that the weight k e and weight k g All are positive values.

[0143] For example, when using the second method by subtracting the third cumulative distribution function f expressed in equation (15) from 1 c (x) and the function f′ c (x) (hereinafter referred to as the "third inverse cumulative distribution function") is equal to the fourth cumulative distribution function f expressed by equation (16) subtracted from 1. d (x) and the function f′ d When the second target time waveform (i.e., T wave) is approximated by the difference between (x) and (x) (hereinafter referred to as the "fourth inverse cumulative distribution function"), the second target time waveform is approximated by the approximate time waveform of formula (17), and the parameter of the third cumulative distribution function, i.e., the mean μ, is determined. c and standard deviation σ c , and determine the parameters of the fourth cumulative distribution function, namely the mean μ d and standard deviation σ d To obtain the parameters that represent the characteristics of the second target time waveform (ie, T wave), the equation (17) is obtained from the third inverse cumulative distribution function f′ c (x) minus the fourth inverse cumulative distribution function f′ d (x) and the function obtained.

[0144] [Formula 15]

[0145]

[0146] [Formula 16]

[0147]

[0148] [Formula 17]

[0149]

[0150] For example, when the second method is used to approximate the second object time waveform (i.e., T wave) by the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, the second object time waveform is approximated by the approximate time waveform of formula (18), and the parameter of the third cumulative distribution function, i.e., the mean μ, is determined. c and standard deviation σ c , and determine the parameters of the fourth cumulative distribution function, namely the mean μ d and standard deviation σ d To obtain the parameters that represent the characteristics of the second object time waveform (ie, T wave), the formula (18) is obtained from the third inverse cumulative distribution function f′ c (x) multiplied by weight k c Subtract the fourth inverse cumulative distribution function f′ from the function d (x) multiplied by weight k d Furthermore, the weight k of the third inverse cumulative distribution function can also be c and the weight k of the fourth inverse cumulative distribution function d , or the weight k of the third inverse cumulative distribution function c and the weight k of the fourth inverse cumulative distribution function d The ratio (k c / k d or k d / k c ) is also obtained as a parameter representing the characteristics of the second object time waveform (ie, T wave).

[0151] [Formula 18]

[0152]

[0153] If the function from equation (18) is the third inverse cumulative distribution function multiplied by the weight k c The fourth inverse cumulative distribution function is subtracted from the function multiplied by the weight k d The weight k is the function obtained by the function c and weight k d However, if the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. c and weight k d Therefore, the signal analysis device 1 can be fitted so that the weight k c and weight k d are all positive values, but it is not necessary to fit them so that the weight k c and weight k d All are positive values.

[0154] Furthermore, the approximate time waveform of formula (17) is derived from the fourth cumulative distribution function f d(x) minus the third cumulative distribution function f c (x), is the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d In addition, the approximate time waveform of formula (18) is the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d The shape of the curve obtained by adding a constant term to the weighted difference of (x) is similar to the third cumulative distribution function f c (x) and the fourth cumulative distribution function f d Furthermore, 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 third cumulative distribution function f e (x) and the fourth cumulative distribution function f g Therefore, in the following description of the T wave, sometimes only the cumulative distribution function is used instead of describing the cumulative distribution function and the inverse cumulative distribution function.

[0155] Figure 2 is a schematic diagram showing the first cumulative distribution function f with respect to the first target time waveform (ie, R wave). a (x) multiplied by weight k a Function k a f a (x), the second cumulative distribution function f b (x) multiplied by weight k b Function k b f b (x), the weighted difference between the first cumulative distribution function and the second cumulative distribution function is the approximate time waveform k a f a (x)-k b f b (x). The single-point dashed line is the first cumulative distribution function f a (x) multiplied by weight k a Function k a f a (x), the double-dotted line is the second cumulative distribution function f b (x) multiplied by weight k b Function k b f b (x), the dotted line is the approximate time waveform k a f a (x)-k b f b (x). The approximate time waveform k a f a(x)-k b f b (x) is a waveform that approximates the first target time waveform (ie, R wave).

[0156] Figure 3 The third inverse cumulative distribution function f′ is schematically shown with respect to the second object time waveform (ie, T wave). c (x) = 1 - f c (x) multiplied by weight k c Function k c f′ c (x), the fourth inverse cumulative distribution function f′ d (x) = 1 - f d (x) multiplied by weight k d Function k d f′ d (x), the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function is the approximate time waveform k c f′ c (x)-k d f′ d (x). The single-point dashed line is the third inverse cumulative distribution function f′ c (x) = 1 - f c (x) multiplied by weight k c Function k c f′ c (x), the double-dotted line is the fourth inverse cumulative distribution function f′ d (x) = 1 - f d (x) multiplied by weight k d Function k d f′ d (x), the dotted line is the approximate time waveform k c f′ c (x)-k d f′ d (x). The approximate time waveform k c f′ c (x)-k d f′ d (x) is a waveform that approximates the second target time waveform (ie, T wave).

[0157] Figure 4 This figure schematically shows the result of fitting each of the first and second target time waveforms using the difference between the two cumulative distribution functions, with the R wave included in the waveform of one cycle of the electrocardiogram of the target heart in an embodiment as the first target time waveform and the T wave as the second target time waveform. Figure 4The horizontal axis represents time, and the vertical axis represents potential. The units of both the horizontal and vertical axes are arbitrary units.

[0158] Specifically, Figure 4 This example shows how the first target time waveform (i.e., R wave) is fitted using the difference between the first and second cumulative distribution functions, and the second target time waveform (i.e., T wave) is fitted using the difference between the third and fourth cumulative distribution functions. The domain of the first and second cumulative distribution functions is the same, covering the time interval of the first target time waveform (i.e., R wave) from time T1 to time T3. The domain of the third and fourth cumulative distribution functions is the same, covering the time interval of the third and fourth cumulative distribution functions from time T4 to time T6.

[0159] Figure 4 The “first fitting result” and “second fitting result” are the fitting results of the R wave. Figure 4 The “third fitting result” and “fourth fitting result” in FIG. 5 are the fitting results of the T wave.

[0160] exist Figure 4 In FIG, “first fitting result” shows the first cumulative distribution function in the fitting result of the electrocardiogram to the first object time waveform (ie, R wave). Figure 4 In FIG, “Second fitting result” shows the second cumulative distribution function in the fitting result of the electrocardiogram to the first object time waveform (ie, R wave). Figure 4 In FIG, “the third fitting result” shows the third cumulative distribution function in the fitting result of the electrocardiogram to the second object time waveform (ie, T wave). Figure 4 In FIG, “the fourth fitting result” shows the fourth cumulative distribution function in the fitting result of the electrocardiogram to the second object time waveform (ie, T wave). Figure 4 In the figure, "body surface potential" represents the waveform of the electrocardiogram of the fitting subject.

[0161] Furthermore, the signal analysis device 1 does not perform fitting for the period from time T3 to time T4, which does not belong to the time interval of the first target time waveform (i.e., R wave) nor the time interval of the second target time waveform (i.e., T wave). Figure 4 In FIG, the first fitting result at time T3 and the third fitting result at time T4 are connected by a line, and the second fitting result at time T3 and the fourth fitting result at time T4 are connected by a line. That is, when the signal analysis device 1 displays the fitting results, as shown in FIG. Figure 4As shown, the signal analysis 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, by a predetermined function such as a constant function or a linear function.

[0162] Furthermore, when the signal analysis device 1 displays the fitting results, the weight values 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, although the actual first fitting result at time T3 is k a f a (T3), the actual third fitting result at time T4 is k c f′ c (T4), but we can find the solution that satisfies k a f a (T3) = α1k c f′ c (T4) of α1, thus using α1k c Instead of weight k c To display the fitting results, you can also use k a / α1 replaces weight k a Similarly, when the signal analysis device 1 displays the fitting results, the weight value can be corrected so that the second fitting result at time T3 and the fourth fitting result at time T4 can be displayed with the same value. That is, although the second fitting result at time T3 is k b f b (T3), the fourth fitting result at time T4 is k d f′ d (T4), but we can find the solution that satisfies k b f b (T3) = α2k d f′ d (T4) of α2, thus using α2k d Instead of weights kd to display the fitting results, you can also use k b / α2 instead of weight k b to display the fitting results.

[0163] Furthermore, fitting the first target time waveform and fitting the second target time waveform do not need to be performed separately. That is, fitting the first target time waveform and the second target time waveform can be performed as a combined fit. For example, when performing the combined fit of the first target time waveform and the second target time waveform, the signal analysis device 1 can also perform fitting that also takes into account minimizing the difference between the first fitting result at time T3 and the third fitting result at time T4, and minimizing the difference between the second fitting result at time T3 and the fourth fitting result at time T4.

[0164] Figure 5 This is an explanatory diagram explaining that typical characteristics of the T wave can be visualized by approximating the T wave with a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function and displaying the third and fourth inverse cumulative distribution functions or the parameters that determine each cumulative distribution function. Figure 5 Four images are shown: image G1, image G2, image G3, and image G4. Each of images G1 to G4 shows a graph with time on the horizontal axis and potential on the vertical axis. Figure 5 The units of the horizontal axis and the vertical axis of each of images G1 to G4 are arbitrary units.

[0165] Figure 5 The "first function" is an example of the third inverse cumulative distribution function. Figure 5 The "second function" is an example of the fourth inverse cumulative distribution function. Figure 5 The “third function” in represents a function obtained by subtracting the “second function” from the “first function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The "third function" has a shape that is roughly the same as the shape of the T wave of a normal heart.

[0166] Figure 5 The "fourth function" is an example of the third inverse cumulative distribution function. Figure 5 The "fifth function" is an example of the fourth inverse cumulative distribution function. Figure 5 The “sixth function” of represents a function obtained by subtracting the “fifth function” from the “fourth function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The "sixth function" has a shape that is substantially the same as the shape of a T wave decrease in one of the three typical patterns of T wave abnormality. Figure 5 The interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in image G2 of a normal heart is narrower than the interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in image G1 of a normal heart. Therefore, when the T wave is reduced, the delay between the activity of the outer layer of the visualized myocardium and the activity of the inner layer of the myocardium is smaller.

[0167] Figure 5 The "seventh function" is an example of the third inverse cumulative distribution function. Figure 5 The "eighth function" is an example of the fourth inverse cumulative distribution function. Figure 5 The “ninth function” of represents a function obtained by subtracting the “eighth function” from the “seventh function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The "ninth function" has a shape that is substantially the same as the shape of an elevation of the T wave, which is one of the three typical patterns of T wave abnormality. Figure 5 The interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G3 of the normal heart is wider than the interval between the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G1 of the normal heart. Therefore, when the T wave increases, the delay between the activity of the outer layer of the visualized myocardium and the activity of the inner layer of the myocardium is larger.

[0168] Figure 5 The "tenth function" is an example of the third inverse cumulative distribution function. Figure 5 The "eleventh function" is an example of the fourth inverse cumulative distribution function. Figure 5 The “twelfth function” of represents a function obtained by subtracting the “eleventh function” from the “tenth function”, that is, a function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function. Figure 5 The "twelfth function" has a shape substantially identical to the shape of a negative T wave in one of the three typical patterns of T wave abnormality. Figure 5 The order of the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G4 is opposite to the order of the descending part of the third inverse cumulative distribution function and the descending part of the fourth inverse cumulative distribution function in the image G1 of the normal heart. Thus, in the case of a negative T wave, the outer layer of the visualized myocardium ends activity earlier than the inner layer of the myocardium.

[0169] The function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can express waves whose widths in the vertical and horizontal axes differ, as in the "third function," "sixth function," and "ninth function." Furthermore, the function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function can express negative waves, as in the "twelfth function." That is, by approximating the T wave using the function obtained by subtracting the fourth inverse cumulative distribution function from the third inverse cumulative distribution function, or by approximating the T wave using the function obtained by subtracting the fourth cumulative distribution function from the third inverse cumulative distribution function by reversing the time axis, it is possible to express the activities of the inner and outer myocardium layers included in the T wave, as well as the relationship between the activities of the inner and outer myocardium layers. This also applies to the case of approximating the R wave using the function obtained by subtracting the second cumulative distribution function from the first cumulative distribution function.

[0170] In this manner, signal analysis device 1 fits the waveform of the R wave or T wave of the subject's electrocardiogram using the difference or weighted difference between the two cumulative distribution functions. Signal analysis device 1 then obtains parameters that define the approximate time waveform determined by the fitting as parameters representing myocardial activity.

[0171] [Approximation by adding the difference or weighted difference of two cumulative distribution functions]

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

[0173] When using a weighted difference, for example, the signal analysis device 1 uses a combination (J×K×L×M×N types) of parameters defining the cumulative distribution function of each of the plurality (M) candidates for the first cumulative distribution function, parameters defining the cumulative distribution function of each of the plurality (N) candidates for the second cumulative distribution function, a plurality (K) candidates for weights assigned to the first cumulative distribution function, a plurality (L) candidates for weights assigned to the second cumulative distribution function, and a plurality (J) candidates for level values to generate a candidate time waveform that is a time waveform resulting from adding the level value to the weighted difference between the first and second cumulative distribution functions. The signal analysis device 1 determines the candidate time waveform that is closest to the target time waveform among the generated J×K×L×M×N candidate time waveforms as the approximate time waveform, and obtains the parameters defining the first cumulative distribution function candidate, the parameters defining the second cumulative distribution function candidate, the weights assigned to the first cumulative distribution function, the weights assigned to the second cumulative distribution function, and the level value used in generating the determined approximate time waveform as parameters representing the characteristics of the target time waveform.

[0174] Alternatively, for example, the signal analysis device 1 repeatedly performs the operation of obtaining a candidate time waveform of a time waveform obtained by adding a weighted difference between a candidate for the first cumulative distribution function and a candidate for the second cumulative distribution function that approximate the object time waveform, and updating at least any one of the parameters for determining each cumulative distribution function, the weight assigned to each cumulative distribution function, and the level value in a direction in which the square error between the candidate time waveform and the object time waveform becomes smaller until the square error becomes below a predetermined reference, or repeats the above operation a predetermined number of times, determines the candidate time waveform finally obtained as the approximate time waveform, and obtains the parameters for determining the candidate for the first cumulative distribution function, the parameters for determining the candidate for the second cumulative distribution function, the weight assigned to the first cumulative distribution function, the weight assigned to the second cumulative distribution function, and the level value used in generating the determined approximate time waveform as parameters representing the characteristics of the object time waveform.

[0175] In this case, the signal analysis device 1 first sets the starting point of the target time waveform (equivalent to the R wave) to the R wave. Figure 4 The potential at the time T1 in the target time waveform is obtained as the level value. When the target time waveform is a T wave, the end of the target time waveform (equivalent to Figure 4 The potential at time T6 in the signal analysis apparatus 1 is obtained as the level value. The signal analysis apparatus 1 then uses a combination (K×L×M×N types) of parameters defining the cumulative distribution function of each of the plurality (M) candidates for the first cumulative distribution function, parameters defining the cumulative distribution function of each of the plurality (N) candidates for the second cumulative distribution function, a plurality (K) candidates for weighting the first cumulative distribution function, and a plurality (L) candidates for weighting the second cumulative distribution function to generate a candidate time waveform that is a time waveform resulting from adding the level value to the weighted difference between the first and second cumulative distribution functions. The signal analysis apparatus 1 determines the candidate time waveform that is closest to the target time waveform among the generated K×L×M×N candidate time waveforms as the approximate time waveform. The parameters defining the first cumulative distribution function candidate, the parameters defining the second cumulative distribution function candidate, the weighting assigned to the first cumulative distribution function, the weighting assigned to the second cumulative distribution function, and the level value determined in the initial processing used to generate the determined approximate time waveform are obtained as parameters representing the characteristics of the target time waveform.

[0176] Alternatively, for example, when the target time waveform is an R wave, the signal analysis device 1 first changes the starting point of the target time waveform (equivalent to Figure 4 The potential at the time T1 in the target time waveform is obtained as the level value. When the target time waveform is a T wave, the end of the target time waveform (equivalent to Figure 4The potential at time T6 in the signal analysis apparatus 1 is obtained as the level value. The signal analysis apparatus 1 then repeatedly performs the operation of obtaining a candidate time waveform, which is a time waveform generated by adding the level value to the weighted difference between the first cumulative distribution function candidate and the second cumulative distribution function candidate that approximate the target time waveform, and the operation of updating at least one of the parameters determining each cumulative distribution function and the weight assigned to each cumulative distribution function in a direction that reduces the square error between the candidate time waveform and the target time waveform, until the square error becomes less than a predetermined reference, or repeats this operation a predetermined number of times. The resulting candidate time waveform is determined as the approximate time waveform, and the parameters determining the candidate first cumulative distribution function, the parameters determining 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 determined in the initial processing are obtained as parameters representing the characteristics of the target time waveform.

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

[0178] [Formula 19]

[0179]

[0180] When approximating the target time waveform using the approximate time waveform of equation (19) obtained by adding the level value to the weighted difference between the first cumulative distribution function and the second cumulative distribution function, at least the parameters that determine the first unimodal distribution or the parameters that determine the first cumulative distribution function, namely, the mean μ1 and the standard deviation σ1; the parameters that determine the second unimodal distribution or the parameters that determine the second cumulative distribution function, namely, the mean μ2 and the standard deviation σ2; and the level value β are obtained as parameters representing the characteristics of the target time waveform. Furthermore, the weight k1 of the first cumulative distribution function and the weight k2 of the second cumulative distribution function, or the ratio (k1 / k2 or k2 / k1) of the weight k1 of the first cumulative distribution function to the weight k2 of the second cumulative distribution function, may also be obtained as parameters representing the characteristics of the target time waveform.

[0181] If the function in equation (19) is obtained by subtracting the function of the second cumulative distribution function multiplied by the weight k2 from the function of the first cumulative distribution function multiplied by the weight k1 and adding the level value β, both weights k1 and k2 are positive. However, if the subject's heart is in a specific state, the possibility that at least one of the weights k1 and k2 obtained by fitting is not positive cannot be ruled out. Therefore, while the signal analysis device 1 can fit so that both weights k1 and k2 are positive, it is not necessary to fit so that both weights k1 and k2 are positive.

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

[0183] For example, when the first target time waveform (i.e., R wave) is approximated by a function obtained by adding a level value to the weighted difference between the first cumulative distribution function and the second cumulative distribution function, the potential at the beginning of the first target time waveform is set to the level value β. R , the first object time waveform is approximated by the approximate time waveform of formula (20), and at least the parameter of the first cumulative distribution function, i.e., the mean μ, is determined. a and standard deviation σ a , determine the parameter of the second cumulative distribution function, namely the mean μ b and standard deviation σ b , and the level value β R The parameters representing the characteristics of the first target time waveform (ie, R wave) are obtained. The above formula (20) is obtained from the first cumulative distribution function f represented by formula (7). a (x) multiplied by weight k a The second cumulative distribution function f expressed by formula (8) is subtracted from the function b (x) multiplied by weight k b function and add the level value β R Furthermore, the weight k of the first cumulative distribution function can also be a and the weight k of the second cumulative distribution function b , or the weight k of the first cumulative distribution function a and the weight k of the second cumulative distribution function b The ratio (k a / k b or k b / k a ) is also obtained as a parameter representing the characteristics of the parameter representing the characteristics of the first target time waveform (ie, R wave).

[0184] [Formula 20]

[0185]

[0186] If the function from Equation (20) is the first cumulative distribution function multiplied by the weight k a The second cumulative distribution function is subtracted from the function multiplied by the weight k b function and add the level value β R From the function of a and weight k b However, if the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. a and weight k b Therefore, the signal analysis device 1 can be fitted so that the weight k a and weight k b are all positive values, but it is not necessary to fit them so that the weight k a and weight k b All are positive values.

[0187] For example, when the second target inverse time waveform (i.e., T wave) is approximated by a function obtained by adding a level value to the weighted difference between the third cumulative distribution function and the fourth cumulative distribution function, the potential at the end of the second target time waveform is set to the level value β. T , the second object inverse time waveform is approximated by the approximate inverse time waveform of formula (21), and at least the parameter of the third cumulative distribution function, namely the mean μ, is determined. e and standard deviation σ e , determine the parameter of the fourth cumulative distribution function, namely the mean μ g and standard deviation σ g , and the level value β T The parameters representing the characteristics of the second target time waveform (ie, T wave) are obtained. The above formula (21) is obtained from the third cumulative distribution function f represented by formula (11). e (x′) multiplied by the weight k e Subtract the fourth inverse cumulative distribution function f expressed by formula (12) from the function g (x′) multiplied by the weight k g function and add the level value β T Furthermore, the weight k of the third cumulative distribution function can also be e and the weight k of the fourth cumulative distribution function g , or the weight k of the third cumulative distribution function e With the weight k of the fourth cumulative distribution function g The ratio (k e / k g or k g / k e ) obtains a parameter that represents the characteristics of a parameter that represents the characteristics of the second object time waveform (ie, T wave).

[0188] [Formula 21]

[0189]

[0190] If the function from Equation (21) is the third cumulative distribution function multiplied by the weight k e The function of subtracting the fourth cumulative distribution function multiplied by the weight k g function and add the level value β T And the obtained function shows that the weight k e and weight k g However, if the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. e and weight k g Therefore, the signal analysis device 1 can be fitted so that the weight k e and weight k g are all positive values, but it is not necessary to fit them so that the weight k e and weight k g All are positive values.

[0191] 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 a function obtained by subtracting the third cumulative distribution function from 1 (i.e., the third inverse cumulative distribution function) and a function obtained by subtracting the fourth cumulative distribution function from 1 (i.e., the fourth inverse cumulative distribution function), the potential at the end of the second target time waveform is set to the level value β. T , the second object time waveform is approximated by the approximate time waveform of formula (22), and the parameter of the third cumulative distribution function, namely the mean μ, is determined c and standard deviation σ c , determine the parameter of the fourth cumulative distribution function, namely the mean μ d and standard deviation σ d , and the level value β T The parameters representing the characteristics of the second object time waveform (ie, T wave) are obtained. The above formula (22) is obtained from the third inverse cumulative distribution function f c (x) multiplied by weight k c Subtract the fourth inverse cumulative distribution function f from the function d (x) multiplied by weight k d function and add the level value β T Furthermore, the weight k of the third inverse cumulative distribution function can also bec and the weight k of the fourth inverse cumulative distribution function d , or the weight k of the third inverse cumulative distribution function c and the weight k of the fourth inverse cumulative distribution function d The ratio (k c / k d or k d / k c ) is obtained as a parameter representing the characteristics of the second object time waveform (ie, T wave).

[0192] [Formula 22]

[0193]

[0194] If the function from Equation (22) is the third inverse cumulative distribution function multiplied by the weight k c The fourth inverse cumulative distribution function is subtracted from the function multiplied by the weight k d function and add the level value β T And the obtained function shows that the weight k c and weight k d However, if the subject's heart is in a specific state, the weight k obtained by fitting cannot be denied. c and weight k d Therefore, the signal analysis device 1 can be fitted so that the weight k c and weight k d are all positive values, but it is not necessary to fit them so that the weight k c and weight k d All are positive values.

[0195] Figure 6 is a schematic diagram showing the first cumulative distribution function f with respect to the first target time waveform (ie, R wave). a (x) multiplied by weight k a And add the level value β R The function k a f a (x)+β R , the second cumulative distribution function f b (x) multiplied by weight k b And add the level value β R The function k b f b (x)+β R , the weighted difference between the first and second cumulative distribution functions plus the level value β R The approximate time waveform k after a f a (x)-k b fb (x)+β R The single-point dash line is the first cumulative distribution function f a (x) multiplied by weight k a And add the level value β R Function k a f a (x)+β R , the double-dotted line is the second cumulative distribution function f b (x) multiplied by weight k b And add the level value β R Function k b f b (x)+β R , the dotted line is the approximate time waveform k a f a (x)-k b f b (x)+β R The approximate time waveform k a f a (x)-k b f b (x)+β R This is a waveform that approximates the first target time waveform (ie, R wave).

[0196] Figure 7 is a schematic diagram showing the third inverse cumulative distribution function f′ with respect to the second object time waveform (ie, T wave). c (x) = 1 - f c (x) multiplied by weight k c And add the level value β T Function k c f′ c (x)+β T , the fourth inverse cumulative distribution function f′ d (x) = 1 - f d (x) multiplied by weight k d And add the level value β T Function k d f′ d (x)+β T , the weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function plus the level value β T The approximate time waveform k after c f′ c (x)-k d f′ d (x)+β T The single-point dash line is the third inverse cumulative distribution function f′ c (x) = 1 - f c (x) multiplied by weight k cAnd add the level value β T Function k c f′ c (x)+β T , the double-dotted line is the fourth inverse cumulative distribution function f′ d (x) = 1 - f d (x) multiplied by weight k d And add the level value β T Function k d f′ d (x)+β T , the dotted line is the approximate time waveform k c f′ c (x)-k d f′ d (x)+β T The approximate time waveform k c f′ c (x)-k d f′ d (x)+β T This is a waveform that approximates the second target time waveform (ie, T wave).

[0197] Furthermore, the potential at the beginning of the R wave (more precisely, the QRS wave) is the level value β R It is a value indicating the magnitude of the DC component at the beginning of the R wave. If there is an abnormality in the coronary artery, the level value β R Sometimes it drops to the negative side. In addition, the potential at the end of the T wave, that is, the level value β T It is a value indicating the magnitude of the DC component at the end of the T wave. When there is an abnormality in myocardial repolarization, the level value β T Sometimes it rises to the positive side.

[0198] [Approximation of the Difference Between the Target Time Waveform and the Approximate Time Waveform]

[0199] When the R wave or T wave in a special state is used as the target time waveform, a portion that cannot approximate the target time waveform (hereinafter referred to as "residual portion") may remain in the above-mentioned approximate time waveform. For example, when early repolarization or conduction disorder (such as the auxiliary conduction pathway) occurs in the target heart, Figure 8 The Δ wave indicated by the dotted line is sometimes included in the target time waveform (R wave). This Δ wave portion cannot approximate the target time waveform in the aforementioned approximate time waveform, and remains as a residual. Considering that this residual portion is also a time waveform caused by some kind of cardiac activity, the signal analysis device 1 can analyze this residual portion by assuming it is a cumulative Gaussian distribution, a function obtained by multiplying a cumulative Gaussian distribution by a weight, a difference between cumulative Gaussian distributions, or a weighted difference between cumulative Gaussian distributions.

[0200] That is, the signal analysis device 1 can also approximate the residual time waveform by a cumulative distribution function (conveniently called the "fifth cumulative distribution function") of a certain unimodal distribution (conveniently called the "fifth unimodal distribution") or a function obtained by multiplying the fifth cumulative distribution function by a weight, for the difference between the object time waveform and the approximate time waveform, that is, the residual time waveform, and the parameters that determine the fifth unimodal distribution or the parameters that determine the fifth cumulative distribution function are also obtained as parameters representing the characteristics of the object time waveform.

[0201] Alternatively, the signal analysis device 1 may also, for the difference between the object time waveform and the approximate time waveform, i.e., the residual time waveform, approximate the residual time waveform by using a time waveform (hereinafter referred to as the "approximate residual time waveform") generated by the difference or weighted difference between the cumulative distribution function (conveniently referred to as the "fifth cumulative distribution function") of a certain unimodal distribution (conveniently referred to as the "fifth cumulative distribution function") and the cumulative distribution function (conveniently referred to as the "sixth cumulative distribution function") of a unimodal distribution different from the fifth unimodal distribution (conveniently referred to as the "sixth unimodal distribution"), and obtain the parameters of the fifth unimodal distribution or the parameters of the fifth cumulative distribution function, and the parameters of the sixth unimodal distribution or the parameters of the sixth cumulative distribution function as parameters representing the characteristics of the object time waveform.

[0202] More specifically, when the residual time waveform is approximated by the cumulative distribution function of the fifth unimodal distribution expressed by formula (23), that is, the fifth cumulative distribution function, the signal analysis device 1 approximates the residual time waveform by the approximate time waveform f5(x) of the fifth cumulative distribution function expressed by formula (24) for the difference between the object time waveform and the approximate time waveform. In addition to the above-mentioned parameters representing the characteristics of the object time waveform, the parameters that determine the fifth cumulative distribution function, that is, the mean μ5 and the standard deviation σ5, are also obtained as parameters representing the characteristics of the object time waveform.

[0203] [Formula 23]

[0204]

[0205] [Formula 24]

[0206]

[0207] When approximating the residual time waveform using a function that multiplies the fifth cumulative distribution function by a weight, the signal analysis device 1 approximates the residual time waveform, which is the difference between the target time waveform and the approximated time waveform, by multiplying the fifth cumulative distribution function f5(x) expressed by equation (24) by an approximated time waveform k5f5(x) that is a function that multiplies the weight k5. In addition to the aforementioned parameters that characterize the target time waveform, the mean μ5 and standard deviation σ5, which are parameters that determine the fifth cumulative distribution function, are also acquired as parameters that characterize the target time waveform. Furthermore, the signal analysis device 1 may also acquire the weight k5 as a parameter that characterizes the target time waveform.

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

[0209] [Formula 25]

[0210]

[0211] [Formula 26]

[0212]

[0213] When approximating the residual time waveform using the weighted difference between the fifth and sixth cumulative distribution functions, the signal analysis device 1 approximates the residual time waveform by subtracting the function k6f6(x) obtained by multiplying the sixth cumulative distribution function f6(x) by the weight k6 from the function k5f5(x) obtained by multiplying the fifth cumulative distribution function f5(x) by the weight k5, i.e., the approximate time waveform k5f5(x)-k6f6(x). In addition to the aforementioned parameters characteristic of the target time waveform, the parameters defining the fifth cumulative distribution function, i.e., the mean μ5 and standard deviation σ5, and the parameters defining the sixth cumulative distribution function, i.e., the mean μ6 and standard deviation σ6, are also acquired as parameters characteristic of the target time waveform. Furthermore, the signal analysis device 1 may also acquire the weights k5 and k6, or the ratio of the weights k5 to k6 (k5 / k6 or k6 / k5), as parameters characteristic of the target time waveform.

[0214] If the residual time waveform is approximated using the approximate time waveform obtained by subtracting the function multiplied by the sixth cumulative distribution function and weight k6 from the function multiplied by the fifth cumulative distribution function and weight k5, both weights k5 and k6 are positive. However, if the subject's heart is in a specific state, the possibility that at least one of the weights k5 and k6 obtained through fitting is not positive cannot be ruled out. Therefore, while the signal analysis device 1 can fit so that both weights k5 and k6 are positive, it is not necessary to do so.

[0215] Return to Figure 1 Signal analysis device 1 includes a control unit 11 that executes a program. The control unit 11 includes a processor 91 such as a CPU and a memory 92 connected via a bus. By executing the program, signal analysis device 1 functions as a device including control unit 11, input unit 12, communication unit 13, storage unit 14, and output unit 15.

[0216] More specifically, processor 91 reads a program stored in storage unit 14 and stores the read program in memory 92. Processor 91 executes the program stored in memory 92, thereby enabling signal analysis device 1 to function as a device including control unit 11, input unit 12, communication unit 13, storage unit 14, and output unit 15.

[0217] The control unit 11 controls the operation of the various functional units included in the signal analysis device 1. For example, the control unit 11 executes a myocardial activity information parameter acquisition process. For example, the control unit 11 controls the operation of the output unit 15 so that the output unit 15 outputs the results of the myocardial activity information parameter acquisition process. The control unit 11 records various information generated by the execution of the myocardial activity information parameter acquisition process in the storage unit 14.

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

[0219] 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 to the input unit 12 for a plurality of candidates for each cumulative distribution function used for fitting.

[0220] Furthermore, the distribution shape specifying information may be pre-stored in the storage unit 14. In this case, the distribution shape specifying information stored in the storage unit 14 does not need to be input from the input unit 12. For simplicity, the following description of the signal analysis device 1 takes as an example the case where the distribution shape specifying information is pre-stored in the storage unit 14.

[0221] The communication unit 13 is configured to include a communication interface for connecting the signal analysis device 1 to an external device. The communication unit 13 communicates with the external device via a wired or wireless connection. For example, the external device is a device that transmits the electrocardiogram waveform of the subject's heart. For example, the device that transmits the electrocardiogram waveform of the subject's heart is an electrocardiogram device. If the external device is an electrocardiogram device, for example, the communication unit 13 obtains the electrocardiogram waveform from the electrocardiogram device through communication. Alternatively, the electrocardiogram waveform may be input to the input unit 12.

[0222] The storage unit 14 is configured using a non-transitory computer-readable storage medium such as a magnetic hard disk drive or a semiconductor memory device. The storage unit 14 stores various information related to the signal analysis device 1. For example, the storage unit 14 stores information input via the input unit 12 or the communication unit 13. For example, the storage unit 14 stores an electrocardiogram input via the input unit 12 or the communication unit 13. For example, the storage unit 14 stores various information generated by executing the myocardial activity information parameter acquisition process.

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

[0224] Figure 9 1 is a diagram showing an example of the functional configuration of the control unit 11 in the embodiment. The control unit 11 includes an electrocardiogram acquisition unit 110 , a fitting information acquisition unit 120 , an analysis unit 130 , and a recording unit 140 .

[0225] The electrocardiogram acquisition unit 110 acquires a waveform corresponding to one cycle from the waveform of the electrocardiogram of the subject's heart input to the input unit 12 or the communication unit 13, and outputs it to the analysis unit 130. The waveform of the electrocardiogram of the subject's heart is a waveform in which waveforms corresponding to multiple beats (multiple cycles) are arranged in a time series. Even if an abnormality occurs in the subject's heart, the waveforms of all beats included in the waveform of the electrocardiogram are not special waveforms, and only the waveforms of any few beats included in the waveform of the electrocardiogram become characteristic waveforms. Preferably, this characteristic waveform is used as a target in the myocardial activity information parameter acquisition process. Therefore, the electrocardiogram acquisition unit 110 acquires a waveform corresponding to one cycle as a characteristic waveform from the waveform of the electrocardiogram of the subject's heart. For example, the electrocardiogram acquisition unit 110 can acquire a waveform corresponding to one cycle as a characteristic waveform from the waveform of the electrocardiogram of the subject's heart using a known technique for determining similarity or specificity. In addition, for example, the electrocardiogram acquisition unit 110 can also cause the output unit 15 to display the waveform of the electrocardiogram, cause the input unit 12 to accept the designation of the waveform for one cycle by a user such as a doctor, and obtain the waveform for one cycle corresponding to the designation accepted by the input unit 12 from the waveform of the electrocardiogram.

[0226] The electrocardiogram acquisition unit 110 outputs the waveform corresponding to one cycle as digital time-series data sampled at a predetermined sampling frequency. The predetermined sampling frequency refers to the sampling frequency of the signal used in the processing by the fitting information acquisition unit 120, and is, for example, 250 Hz. If the input electrocardiogram waveform is sampled at the predetermined sampling frequency, the electrocardiogram acquisition unit 110 can simply cut out the digital time-series data corresponding to one cycle of the waveform from the digital time-series data of the input electrocardiogram waveform and output it. If the input electrocardiogram waveform is sampled at a sampling frequency different from the predetermined sampling frequency, the electrocardiogram acquisition unit 110 can cut out the digital time-series data corresponding to one cycle of the waveform from the digital time-series data of the input electrocardiogram waveform, convert it to the predetermined sampling frequency, and output it.

[0227] Furthermore, the electrocardiogram acquisition unit 110 determines the time interval of the R wave and the time interval of the T wave included in the waveform of one cycle of the electrocardiogram, obtains information determining the time interval of the R wave and the time interval of the T wave, and outputs the information to the analysis unit 130. For example, the electrocardiogram acquisition unit 110 can determine the start and end of the R wave, the start and end of the T wave using known techniques, and obtain the sample numbers corresponding to the determined start and end of the R wave, the start and end of the T wave, and the relative time from the start of the waveform as the information determining the time interval of the R wave and the information determining the time interval of the T wave. Furthermore, the R wave in this specification refers to the QRS wave, to be precise. While there are various interpretations as to which point in the waveform represents the start of the R wave (i.e., the start of the QRS wave), the electrocardiogram acquisition unit 110 can simply use a point determined using any known technique as the start of the R wave.

[0228] Furthermore, the subject's electrocardiogram waveform sometimes exhibits characteristic waveforms that change over time. Therefore, the electrocardiogram acquisition unit 110 may also acquire waveforms corresponding to multiple cycles from the subject's electrocardiogram waveform as waveforms for which myocardial activity information parameter acquisition processing is performed. Specifically, the electrocardiogram acquisition unit 110 may acquire a predetermined long-term time series waveform (trend graph) from the subject's electrocardiogram waveform and output to the analysis unit 130 information regarding the waveforms of each cycle included in the acquired waveform, information identifying the time interval of the R wave included in the waveform, and information identifying the time interval of the T wave included in the waveform.

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

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

[0231] The fitting unit 131 fits the target time waveform, which is the waveform of the time interval of at least one of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram acquired by the electrocardiogram acquisition unit 110 , using the candidate cumulative distribution function indicated by the distribution shape designation information.

[0232] The myocardial activity information parameter acquisition unit 132 acquires parameters representing the characteristics of the target time waveform based on the fitting results performed by the fitting unit 131. As parameters representing the characteristics of the target time waveform, the myocardial activity information parameter acquisition unit 132 acquires, for example, parameters that define a first unimodal distribution or a first cumulative distribution function when approximating the target time waveform using an approximated time waveform, which is a time waveform resulting from the difference or weighted difference between the first cumulative distribution function of the first unimodal distribution and the second cumulative distribution function of the second unimodal distribution. The parameters representing the characteristics of the target time waveform acquired by the myocardial activity information parameter acquisition unit 132 are examples of myocardial activity parameters.

[0233] In this way, the analyzing unit 130 acquires the myocardial activity parameter based on the target time waveform, which is the waveform of the time interval of at least one of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram of the target heart, and the distribution candidate information.

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

[0235] Figure 10 This is a flowchart showing an example of a processing flow performed by the signal analysis device 1 in the embodiment. The electrocardiogram acquisition unit 110 acquires the waveform of one cycle of the electrocardiogram of the target heart, information for determining the time interval of the R wave, and information for determining the time interval of the T wave via the input unit 12 or the communication unit 13 (step S101). Next, the fitting information acquisition unit 120 acquires distribution shape designation information (step S102). Next, the fitting unit 131 fits the waveform of at least one of the time intervals of the R wave and the T wave included in the waveform of one cycle of the electrocardiogram acquired in step S101, i.e., the target time waveform, using the candidate of the cumulative distribution function indicated by the distribution shape designation information (step S103). Next, the myocardial activity information parameter acquisition unit 132 acquires myocardial activity parameters based on the fitting results (step S104). The acquired myocardial activity parameters are output to the output unit 15 (step S105).

[0236] In step S105, a graph of the fitting results for each target time waveform may be displayed. Furthermore, the process of step S102 may be performed before the process of step S103, or before the process of step S101. The processes of steps S103 and S104 are examples of the processes performed by the analysis unit 130.

[0237] Figure 11 1 is a first diagram showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 11 This is an example of an analysis result of the waveform of the electrocardiogram of a subject's heart that is operating normally, obtained by the signal analysis device 1 . Figure 11 The horizontal axis represents time, and the vertical axis represents potential. The unit of the vertical axis is arbitrary unit.

[0238] Figure 11 Shows the use of k a / α1 replaces weight k a In order to make the first fitting result at time T3 and the third fitting result at time T4 the same value, k b / α2 instead of weight k b An example of the results of the first and second fittings performed on the depolarization R wave of the electrocardiogram of a subject heart with normal heart function, and the results of the fourth and third fittings performed on the repolarization T wave of the electrocardiogram of a subject heart with normal heart function, are displayed so that the second fitting result at time T3 and the fourth fitting result at time T4 become the same value. Hereinafter, the result of connecting the first and third fitting results with modified weights via a straight line is referred to as the inner myocardial layer activity approximation function, and the result of connecting the second and fourth fitting results with modified weights via a straight line is referred to as the outer myocardial layer activity approximation function.

[0239] Figure 11 In a normal heart, during depolarization, the inner myocardium starts activity (i.e., ion channel activity) earlier than the outer myocardium and progresses rapidly. The outer myocardium starts activity slightly later than the onset of ion channel activity in the inner myocardium, and the difference between the onset of ion channel activity in the inner myocardium and the onset of activity in the outer myocardium results in a positive, sharp R wave. Specifically, the mean and standard deviation of the inner myocardial activity approximation function, which is part of the first fitting result, and the mean and standard deviation of the outer myocardial activity approximation function, which is part of the second fitting result, are parameters that represent the timing and progression of ion channel activity during depolarization in a normal heart.

[0240] also, Figure 11In a normal heart, during the repolarization phase, the outer myocardium begins inactivating ion channel activity earlier than the inner myocardium, and inner myocardial inactivation begins later than outer myocardial inactivation. Both outer and inner myocardial inactivation progress slowly, and the difference between inner and outer myocardial inactivation results in a positive, gentle T wave. Specifically, the mean and standard deviation of the fourth fitting result of the outer myocardial activity approximation function and the mean and standard deviation of the third fitting result of the inner myocardial activity approximation function are parameters representing the timing and progression of ion channel activity inactivation during the repolarization phase of a normal heart. As described above, the inner and outer myocardial activity approximation functions obtained through the first to fourth fitting processes correspond to the timing and progression of collective ion channel activation during depolarization and collective inactivation during repolarization. The mean and standard deviation of each fitting result of each function are parameters representing myocardial activity.

[0241] Figure 11 The shapes of the inner and outer myocardial layer activity approximation functions are generally consistent with the results of direct measurement of myocardial electromotive force (EMF) by inserting a catheter electrode into the myocardium of a normally functioning subject's heart. This demonstrates that signal analysis device 1 can obtain information indicating myocardial activity solely from an electrocardiogram, without inserting a catheter electrode.

[0242] Figure 12 2 is a second diagram showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 12 This is an example of an analysis result of the waveform of the electrocardiogram of a subject's heart that is operating normally, obtained by the signal analysis device 1 .

[0243] Figure 12 The three results are shown as graph G5, graph G6 and result G7. Figure 12 In FIG, the “inner layer side cumulative distribution function” shows the fitting result of the function representing the collective channel activity timing distribution of ion channels present in the inner layer of the myocardium. Figure 12 In FIG, the “outer layer side cumulative distribution function” shows the fitting result of the function representing the collective channel activity timing distribution of ion channels present in the outer layer of the myocardium. Figure 12 In FIG, “the potential of the body surface” is a function representing the temporal change of the potential of the body surface, and is the waveform of the electrocardiogram. Figure 12 The horizontal axis represents time, and the vertical axis represents potential. The units of the horizontal and vertical axes are both arbitrary units. Figures 12 to 14 The time length represented by each scale interval on the horizontal axis is the same. Figures 12 to 14 On the vertical axis, 1 represents the maximum value of the cumulative Gaussian distribution.

[0244] Graph G5 shows the entire waveform of the electrocardiogram generated in one beat. Graph G6 shows an enlarged view of the T wave region as a part of Graph G5. The T wave region is Figure 12 The area A1 is shown in the figure. Result G7 shows the statistics of two Gaussian distributions: the Gaussian distribution that serves as the cumulative source of the inner-side cumulative distribution function and the Gaussian distribution that serves as the cumulative source of the outer-side cumulative distribution function. Each value in 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 serve as the cumulative sources of the inner-side and outer-side cumulative distribution functions.

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

[0246] Figure 13 The three results are shown as graph G8, graph G9 and result G10. Figure 13 In FIG, the “inner layer side cumulative distribution function” shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the inner layer of the myocardium. Figure 13 In FIG, the “Outer Layer Cumulative Distribution Function” shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the outer layer of the myocardium. Figure 13 In FIG, “the potential of the body surface” is a function representing the temporal change of the potential of the body surface, and is the waveform of the electrocardiogram. Figure 13 The horizontal axis represents time, and the vertical axis represents potential. The units of both the horizontal and vertical axes are arbitrary units.

[0247] Graph G8 shows the entire waveform of the electrocardiogram generated in one beat. Graph G9 shows an enlarged view of the T wave region as a part of Graph G8. The T wave region is Figure 13 The area A2 is shown in the figure. Result G10 shows the statistics of two Gaussian distributions: the Gaussian distribution that serves as the cumulative source of the inner-side cumulative distribution function and the Gaussian distribution that serves as the cumulative source of the outer-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 serve as the cumulative source of the inner-side cumulative distribution function and the outer-side cumulative distribution function.

[0248] Figure 13The shapes of the inner and outer cumulative distribution functions are generally consistent with the results of directly measuring the changes in electromotive force generated by the pulsation of the outer myocardium of a subject's heart exhibiting T-prolongation type 3 behavior by inserting a catheter electrode. This demonstrates that signal analysis device 1 can acquire information indicating myocardial activity solely from an electrocardiogram, without inserting a catheter electrode.

[0249] Figure 14 4 is a fourth diagram showing an example of the analysis result of the signal analysis device 1 in the embodiment. More specifically, Figure 14 This is an example of an analysis result of the waveform of the electrocardiogram of a target heart that is behaving in QT prolongation type 1 by the signal analysis device 1 .

[0250] Figure 14 The three results are shown as graph G11, graph G12 and result G13. Figure 14 In FIG, the “inner layer side cumulative distribution function” shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the inner layer of the myocardium. Figure 14 In FIG, the “Outer Layer Cumulative Distribution Function” shows the fitting result of the function representing the collective channel activity timing distribution of the channels existing in the outer layer of the myocardium. Figure 14 In FIG, “the potential of the body surface” is a function representing the temporal change of the potential of the body surface, and is the waveform of the electrocardiogram. Figure 14 The horizontal axis represents time, and the vertical axis represents potential. The units of both the horizontal and vertical axes are arbitrary units.

[0251] Graph G11 shows the entire waveform of the electrocardiogram generated in one beat. Graph G12 shows an enlarged view of the T wave region as a part of Graph G11. The T wave region is Figure 13 The area A3 is shown in the figure. Result G13 shows the statistics of two Gaussian distributions: the Gaussian distribution that serves as the cumulative source of the inner-side cumulative distribution function and the Gaussian distribution that serves as the cumulative source of the outer-side cumulative distribution function. Each value in 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 serve as the cumulative sources of the inner-side and outer-side cumulative distribution functions.

[0252] Figure 14 The shapes of the inner and outer cumulative distribution functions are generally consistent with the results of directly measuring changes in electromotive force generated by the pulsation of the outer myocardium of a subject's heart exhibiting QT prolongation type 3 behavior by inserting a catheter electrode. This demonstrates that signal analysis device 1 can acquire information indicating myocardial activity solely from an electrocardiogram, without inserting a catheter electrode.

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

[0254] use Figures 15 to 17 Regarding the electrocardiogram of ventricular extrasystoles, the signal analysis device 1 can also obtain only information indicating myocardial activity from the electrocardiogram. Figures 15 to 17 The horizontal axis represents time (seconds), and the vertical axis represents potential (mV).

[0255] Figure 15 This is a first explanatory diagram for explaining an example of analyzing an electrocardiogram of a ventricular extrasystole by the signal analysis device 1 according to the embodiment. Figure 16 This is a second explanatory diagram of an example of analysis of an electrocardiogram of a ventricular extrasystole by the signal analysis device 1 according to the embodiment. Figure 17 This is a third explanatory diagram of an example of analysis of an electrocardiogram of a ventricular extrasystole by the signal analysis device 1 according to the embodiment.

[0256] More specifically, Figure 15 Shows the cardiac potential on the body surface. Figure 15 The electrocardiogram shows a normal heartbeat and two consecutive ventricular extrasystoles. More specifically, Figure 16 The figure shows an inner myocardial activity approximation function including the inner cumulative distribution function of the depolarization of the ventricular extrasystole and the inner cumulative distribution function of the repolarization phase analyzed by the signal analyzing device 1, and an outer myocardial activity approximation function including the outer cumulative distribution function of the depolarization of the ventricular extrasystole and the outer cumulative distribution function of the repolarization phase analyzed by the signal analyzing device 1. More specifically, Figure 17 An example of the waveform of a ventricular extrasystole in an actually measured electrocardiogram is shown.

[0257] Figure 16 The results show that the inner-side cumulative distribution function of depolarization precedes the outer-side cumulative distribution function, and the standard deviations of both are larger than those of a normal heartbeat, indicating that the spread of excitement is slow. This analysis is consistent with the waveform characteristics of the R wave with a wide hem.

[0258] Figure 16 Since the inner layer cumulative distribution function starts to deactivate earlier than the outer layer cumulative distribution function in the repolarization phase, the order of deactivation of the inner layer and the outer layer is shown as the magnitude relationship of the average values of the two cumulative distribution functions in the repolarization phase. Figure 16 The function obtained by subtracting the outer cumulative distribution function from the inner cumulative distribution function is consistent with the characteristics of the large negative T wave in the repolarization phase, such as Figure 17 As shown in FIG. 1 , the waveform obtained by subtracting the outer cumulative distribution function from the inner cumulative distribution function is substantially consistent with the waveform of the ventricular extrasystole in the actually measured electrocardiogram.

[0259] Furthermore, Figures 15 to 17 The results show that the analysis by the signal analysis device 1 corresponds to an example of giant waves or negative potentials generated by altered conduction, early repolarization or delayed repolarization of the myocardial excitation. Figures 15 to 17 In the depolarization phase, the mean μ of the cumulative distribution function of the inner layer is -1 and the standard deviation σ is 0.32. Figures 15 to 17 In the figure, the mean μ of the cumulative distribution function of the outer layer of depolarization is -0.8 and the standard deviation σ is 0.21. Figures 15 to 17 In the case of the inner side of the repolarization phase, the mean μ of the cumulative distribution function is 1 and the standard deviation σ is 1. In addition, Figures 15 to 17 In the figure, the mean μ of the cumulative distribution function of the outer layer of the repolarization phase is 2.99, and the standard deviation σ is 0.7.

[0260] use Figures 18 to 20 Next, regarding the electrocardiogram of the subject's heart during the depolarization phase of Brugada syndrome type 1, the signal analysis device 1 can also acquire information indicating myocardial activity only from the electrocardiogram. Figures 18 to 20 The vertical axis represents the potential in millivolts.

[0261] Figure 18 This is a first explanatory diagram for explaining an example of analyzing an electrocardiogram of a subject's heart during the depolarization phase of Brugada syndrome type 1 by the signal analysis device 1 according to the embodiment. Figure 19 This is a second explanatory diagram of an example of analysis of an electrocardiogram of a subject's heart during the depolarization phase of Brugada syndrome type 1 by the signal analysis device 1 according to the embodiment. Figure 20 This is a third explanatory diagram of an example of analysis of an electrocardiogram of a subject's heart during the depolarization phase of Brugada syndrome type 1 by the signal analysis device 1 according to the embodiment.

[0262] More specifically, Figure 18 An electrocardiogram showing chest second induction in Brugada syndrome. Figure 18 In the figure, the inner frame W1 shows the depolarization phase and the inner frame W2 shows the division of the repolarization phase. Figure 19 The same is true. That is, in Figure 19 In FIG, too, the inner frame W1 shows the depolarization phase, and the inner frame W2 shows the repolarization phase.

[0263] More specifically, Figure 19 The inner layer cumulative distribution function and the outer layer cumulative distribution function in the depolarization and repolarization phases analyzed by the signal analysis device 1 are shown. Figure 19 In the example shown, the repolarization of the inner side cumulative distribution function begins after the depolarization phase, showing the characteristics of early repolarization. Figure 19In the example, the potential amplitude of the outer layer cumulative distribution function differs between the depolarization phase and the repolarization phase. Figure 19 The gap and anisotropy between the depolarization phase and repolarization phase of the outer cumulative distribution function are shown. Thus, the signal analysis device 1 can represent the early repolarization, depolarization, and repolarization anisotropy, which are characteristic of the waveform shown in the electrocardiogram of the subject heart with Brugada syndrome, by using the means and standard deviations of the depolarization phase and repolarization phase of the inner and outer cumulative distribution functions, 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).

[0264] Figure 20 is the comparison between the analytical results and the measured values. More specifically, Figure 20 Show Figure 19 The difference between the inner and outer cumulative distribution functions in the depolarization and repolarization phases of . Figure 20 The actual measured values of the electrocardiogram are also shown. The analysis results are generally consistent with the measured values, except for the very end, which refers to the potential at a later time.

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

[0266] Figures 21 to 59 shows the use of a public ECG database<https: / / physionet.org / about / database / > The result of the analysis by the signal analysis device 1 is shown. Figures 21 to 59 The inner layer cumulative distribution function, the outer layer cumulative distribution function, and the fitting result obtained as a result of the analysis of the cardiac potential by the signal analysis device 1 are shown.

[0267] Figures 21 to 59 Each of the figures shows an example of an electrocardiogram analyzed by the signal analysis device 1 according to the embodiment. The determined points shown in each figure represent, in order from the left of the figure, the Q point, R point, S point, T start point, and T end point of the cardiac potential. The determined points are determined using an inflection point detection and peak detection algorithm. Figures 21 to 59The graphs show the inner cumulative distribution function and the outer cumulative distribution function in each interval obtained for the interval of the depolarization phase (QRS wave) and the interval of the repolarization phase (T wave). Figures 21 to 59 This shows that the signal analysis device 1 can display substantially the same shape for various QRS waves and T waves by adjusting the mean and standard deviation of the inner and outer cumulative distribution functions. Figures 21 to 59 The lower part of the figure shows the original waveform of the electrocardiogram and the fitting result. Figures 21 to 59 The results are the result of 300Hz sampling. Therefore, Figures 21 to 59 The origin of the horizontal axis of each graph represents 0 seconds, and a value of 1 represents 3.33 milliseconds.

[0268] The signal analysis device 1 configured in this manner uses a waveform representing a time interval of either the R wave or the T wave included in a waveform representing one cardiac cycle of a target heart as a target time waveform. The device acquires parameters that define a first unimodal distribution or a first cumulative distribution function when approximating the target time waveform using an approximated time waveform, and parameters that define a second unimodal distribution or a second cumulative distribution function, as parameters representing the characteristics of the target time waveform. The approximated time waveform is a time waveform resulting from the difference or weighted difference between a first cumulative distribution function, which is a cumulative distribution function of the first unimodal distribution, and a second cumulative distribution function, which is a cumulative distribution function of the second unimodal distribution. The first cumulative distribution function represents information indicating the activity of the inner myocardium of the target heart, and the second cumulative distribution function represents information indicating the activity of the outer myocardium of the target heart. Therefore, the parameters representing the shape of the first cumulative distribution function represent parameters indicating the activity of the inner myocardium of the target heart (inner myocardial layer parameters), and the parameters representing the shape of the second cumulative distribution function represent parameters indicating the activity of the outer myocardium of the target heart (outer myocardial layer parameters). Conventional analysis of electrocardiogram waveforms has not yielded information such as these parameters that clearly characterize the motion of the inner myocardium and the outer myocardium of the subject's heart. Therefore, the signal analysis device 1 can obtain useful information for understanding the heart's condition from the electrocardiogram waveform.

[0269] (Only some parameters are obtained)

[0270] If only the characteristics of the activity of the inner myocardium of the target heart are to be understood, the signal analysis device 1 may acquire only the inner myocardial layer parameters. Alternatively, if only the characteristics of the activity of the outer myocardium of the target heart are to be understood, the signal analysis device 1 may acquire only the outer myocardial layer parameters. Furthermore, the signal analysis device 1 may acquire only a portion of the parameters representing the shape of the cumulative distribution function as the inner or outer myocardial layer parameters.

[0271] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 acquires, as a parameter indicating the myocardial activity of the target heart, at least any one of at least a portion of the parameters of the first unimodal distribution, at least a portion of the parameters of the first cumulative distribution function, at least a portion of the parameters of the second unimodal distribution, and at least a portion of the parameters of the second cumulative distribution function, when approximating the waveform of the time interval of the R wave of the target heart, namely the first target time waveform, by the time waveform generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, namely the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, namely the second cumulative distribution function, or by the time waveform generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, namely the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, namely the second cumulative distribution function, plus a level value, namely the first approximate time waveform, as a parameter indicating the myocardial activity of the target heart, namely the myocardial activity parameter.

[0272] For example, if both the first and second unimodal distributions are Gaussian distributions, the myocardial activity parameter acquisition unit 132 may acquire at least one of the mean value, standard deviation, or variance of the first unimodal distribution, and the mean value, standard deviation, or variance of the second unimodal distribution as the myocardial activity parameter. Furthermore, the mean value of the Gaussian distribution is the time at which the frequency value of the unimodal distribution reaches its maximum value, and the time at which the slope of the cumulative distribution function of the unimodal distribution reaches its maximum value. Therefore, for example, regardless of whether the first and second unimodal distributions are Gaussian distributions, the myocardial activity parameter acquisition unit 132 may acquire at least one of the time corresponding to the maximum value of the first unimodal distribution, the time corresponding to the maximum slope of the first cumulative distribution function, the time corresponding to the maximum value of the second unimodal distribution, and the time corresponding to the maximum slope of the second cumulative distribution function as the myocardial activity parameter.

[0273] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 acquires, as a parameter indicating the myocardial activity of the target heart, at least one of at least a portion of the parameters of the third unimodal distribution, at least a portion of the parameters of the third cumulative distribution function, at least a portion of the parameters of the fourth unimodal distribution, and at least a portion of the parameters of the fourth cumulative distribution function, when approximating a waveform of the time interval of the T wave of the target heart, namely, a waveform obtained by inverting the time axis of the second target time waveform, namely, a second target inverse time waveform, by using the second approximate inverse time waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, namely, the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, namely, the fourth cumulative distribution function, or the second approximate inverse time waveform generated by adding the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, namely, the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, namely, the fourth cumulative distribution function, as a parameter indicating the myocardial activity of the target heart, namely, the myocardial activity parameter.

[0274] For example, if both the third and fourth unimodal distributions are Gaussian, the myocardial activity parameter acquisition unit 132 may acquire at least one of the mean value, standard deviation, or variance of the third unimodal distribution, and the mean value, standard deviation, or variance of the fourth unimodal distribution as the myocardial activity parameter. Furthermore, for example, regardless of whether the third and fourth unimodal distributions are Gaussian, the myocardial activity parameter acquisition 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. Furthermore, when acquiring a time as the myocardial activity parameter, the myocardial activity parameter acquisition unit 132 acquires the time (the value of x in the above example) rather than information indicating the time in the reverse direction (the value of x′ in the above example), even when performing approximation of a waveform with the time axis reversed.

[0275] For example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 uses the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, a function obtained by subtracting the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and a function obtained by subtracting the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function. When approximating the waveform of the time interval of the T wave of the target heart, namely, the second target time waveform, which is a waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a waveform generated by adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, the myocardial activity parameter is acquired as a parameter indicating the myocardial activity of the target heart.

[0276] For example, if both the third and fourth unimodal distributions are Gaussian distributions, the myocardial activity information parameter acquisition unit 132 may acquire at least one of the mean value, standard deviation, or variance of the third unimodal distribution, and the mean value, standard deviation, or variance of the fourth unimodal distribution as the myocardial activity parameter. Furthermore, for example, regardless of whether the third and fourth unimodal distributions are Gaussian distributions, the myocardial activity information parameter acquisition 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.

[0277] Similarly, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 also acquires at least one of at least a portion of the parameters of the fifth unimodal distribution and at least a portion of the parameters of the fifth cumulative distribution function as a parameter indicating the myocardial activity of the target heart, namely, a myocardial activity parameter, when approximating the time waveform of the difference between the first object time waveform and the first approximate time waveform, namely, the residual time waveform, or the time waveform of the difference between the second object time waveform and the second approximate time waveform, namely, the residual time waveform, or the time waveform of the difference between the second object time waveform and the waveform obtained by inverting the time axis of the second approximate inverse time waveform, namely, the residual time waveform, using the cumulative distribution function of the fifth unimodal distribution, namely, the fifth cumulative distribution function, or the time waveform generated by multiplying the fifth cumulative distribution function by a weight, namely, the approximate residual time waveform.

[0278] For example, if the fifth unimodal distribution is a Gaussian distribution, the myocardial activity information parameter acquisition unit 132 may acquire at least one of the mean value, standard deviation, or 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 acquisition unit 132 may acquire as the myocardial activity parameter 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.

[0279] In addition, for example, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 also obtains at least any one of at least a portion of the parameters for determining the fifth unimodal distribution, at least a portion of the parameters for determining the fifth cumulative distribution function, at least a portion of the parameters for determining the sixth unimodal distribution, and at least a portion of the parameters for determining the sixth cumulative distribution function as a parameter showing the myocardial activity of the object heart, namely, a myocardial activity parameter, when approximating the residual time waveform by the time waveform generated by the difference or weighted difference between the cumulative distribution function of the fifth unimodal distribution, namely, the fifth cumulative distribution function, and the cumulative distribution function of the sixth unimodal distribution, namely, the sixth cumulative distribution function.

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

[0281] [Acquisition of Myocardial Activity Parameters by Calculation of Fitted Parameters]

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

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

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

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

[0286] The following describes an example in which, when all the first to fourth unimodal distributions are Gaussian distributions, the value obtained by calculating the means obtained through fitting is used as a myocardial activity parameter. If the first to fourth unimodal distributions are not Gaussian distributions, the "mean" in the following example can be replaced with "the time when the value in the unimodal distribution reaches its maximum," i.e., "the time corresponding to the maximum value of the unimodal distribution," "the time when the slope in the cumulative distribution function reaches its maximum," i.e., "the time corresponding to the maximum slope of the cumulative distribution function," or the like.

[0287] (1) Parameter indicating the time from depolarization to repolarization

[0288] It is known that when the time from depolarization of the inner or outer myocardial layer to repolarization is extremely short or extremely long, sudden death may occur due to arrhythmia. In other words, a shortened or prolonged time from depolarization to repolarization of the myocardium may indicate the possibility of a disease state occurring in the myocardium. Therefore, the signal analysis device 1 can obtain the time from depolarization of the inner or outer myocardial layer to repolarization of the inner or outer myocardial layer as a myocardial activity parameter. Specifically, it can obtain at least one of the following four parameters (1A) to (1D) as a myocardial activity parameter.

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

[0290] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can acquire as a myocardial activity parameter the difference between the mean of the first unimodal distribution obtained by the above fitting of the waveform of the time interval of the R wave included in the waveform representing one cardiac cycle of the target heart, that is, the first target time waveform, and the mean of the third unimodal distribution obtained by the above fitting of the waveform of the time interval of the T wave included in the waveform representing one cardiac cycle of the target heart. For example, as in the above example, assuming that the mean of the first unimodal distribution is μ a , the mean of the third unimodal distribution is μ c When the myocardial activity information parameter acquisition unit 132 can also obtain |μ a -μ c | as a parameter of myocardial activity. Furthermore, if μ c In terms of time, μ a Later on, the myocardial activity information parameter acquisition unit 132 may also acquire μ c -μ a As a myocardial activity parameter, this myocardial activity parameter is a parameter indicating the time required for switching from depolarization of the inner layer of the myocardium to repolarization of the inner layer of the myocardium.

[0291] (1B) Parameter indicating the time from depolarization of the outer layer of the myocardium to repolarization of the outer layer of the myocardium

[0292] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can acquire as the myocardial activity parameter the difference between the mean of the second unimodal distribution obtained by the above fitting of the waveform of the time interval of the R wave included in the waveform representing one cardiac cycle of the target heart, that is, the first target time waveform, and the mean of the fourth unimodal distribution obtained by the above fitting of the waveform of the time interval of the T wave included in the waveform representing one cardiac cycle of the target heart. For example, as in the above example, assuming that the mean of the second unimodal distribution is μ b , the mean of the fourth unimodal distribution is μ d When the myocardial activity information parameter acquisition unit 132 can also obtain |μ b -μ d | as a parameter of myocardial activity. Furthermore, if μ d In terms of time, μ b Later on, the myocardial activity information parameter acquisition unit 132 may also acquire μ d -μ b As a myocardial activity parameter, this myocardial activity parameter is a parameter indicating the time required for switching from depolarization of the outer layer of the myocardium to repolarization of the outer layer of the myocardium.

[0293] (1C) Parameter indicating the time required to switch from depolarization of the outer myocardium to repolarization of the inner myocardium

[0294] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can acquire as the myocardial activity parameter the difference between the mean of the second unimodal distribution obtained by the above fitting of the waveform of the time interval of the R wave included in the waveform representing one cardiac cycle of the target heart, that is, the first target time waveform, and the mean of the third unimodal distribution obtained by the above fitting of the waveform of the time interval of the T wave included in the waveform representing one cardiac cycle of the target heart. For example, as in the above example, assuming that the mean of the second unimodal distribution is μ b , the mean of the third unimodal distribution is μ c When the myocardial activity information parameter acquisition unit 132 can also obtain |μ b -μ c | as a parameter of myocardial activity. Furthermore, if μ c In terms of time, μ b Later on, the myocardial activity information parameter acquisition unit 132 may also acquire μ c -μ bAs a myocardial activity parameter, this myocardial activity parameter is a parameter indicating the time required for switching from depolarization of the outer layer of the myocardium to repolarization of the inner layer of the myocardium.

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

[0296] The myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can acquire as a myocardial activity parameter the difference between the mean of the first unimodal distribution obtained by the above fitting of the waveform of the time interval of the R wave included in the waveform representing one cardiac cycle of the target heart, that is, the first target time waveform, and the mean of the fourth unimodal distribution obtained by the above fitting of the waveform of the time interval of the T wave included in the waveform representing one cardiac cycle of the target heart. For example, as in the above example, assuming that the mean of the first unimodal distribution is μ a , the mean of the fourth unimodal distribution is μ d When the myocardial activity information parameter acquisition unit 132 can also obtain |μ a -μ d | as a parameter of myocardial activity. Furthermore, if μ d In terms of time, μ a Later on, the myocardial activity information parameter acquisition unit 132 may also acquire μ d -μ a As a myocardial activity parameter, this myocardial activity parameter is a parameter indicating the time required for switching from depolarization of the inner layer of the myocardium to repolarization of the outer layer of the myocardium.

[0297] (2) Parameters representing the time difference and sequence of inner and outer layer activities during depolarization

[0298] When the time difference between the inner layer activity and the outer layer activity during depolarization is longer than normal, there is a possibility that the conduction of myocardial excitation may be delayed or blocked, or the place where the excitation starts or the order of the excitation transmission may be different from the normal pattern, which particularly suggests an obstruction of the myocardial stimulation conduction system, an ischemic state of the myocardium, or the presence of extrasystoles. Therefore, the signal analysis device 1 can obtain parameters representing the time difference and order between the inner layer activity and the outer layer activity during depolarization as myocardial activity parameters. Specifically, the myocardial activity information parameter acquisition unit 132 of the analysis unit 130 of the signal analysis device 1 can also obtain the waveform of the time interval of the R wave included in the waveform of one cycle of the cardiac cycle of the object heart, that is, the difference between the mean of the first unimodal distribution obtained by the above-mentioned fitting and the mean of the second unimodal distribution of the first object time waveform as the myocardial activity parameter. For example, as in the above example, assuming that the mean of the first unimodal distribution is μ a , the mean of the second unimodal distribution is μ bWhen the myocardial activity information parameter acquisition unit 132 obtains μ a -μ b or μ b -μ a as a parameter of myocardial activity.

[0299] (3) Parameters representing the time difference and sequence of inner and outer layer activities during repolarization

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

[0301] (Correction of myocardial activity parameters)

[0302] Furthermore, the myocardial activity parameters related to temporal width among the myocardial activity parameters acquired by the myocardial activity information parameter acquisition unit 132 are characterized by the influence of heart rate fluctuations and individual differences such as age and gender, similar to conventional parameters representing cardiac activity (e.g., QT interval). Conventional parameters related to temporal width are known to be corrected to reduce the influence of heart rate fluctuations 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 acquisition unit 132 can also be corrected to reduce the influence of heart rate fluctuations and individual differences, and the corrected values can be used as parameters for evaluating myocardial activity. For example, the myocardial activity parameters acquired through the above-described fitting can be corrected for heart rate fluctuations using a linear or nonlinear calculation based on the time interval between adjacent R-peaks (hereinafter referred to as "RR interval"), and the corrected values can be used as the myocardial activity parameters. Furthermore, to account for individual differences, the myocardial activity parameters obtained through the fitting can be corrected by calculating the relationship between the RR interval and the myocardial activity parameters based on the electrocardiogram data of each individual, and the corrected values can be used as the myocardial activity parameters. This correction can be performed by another device after the signal analysis device 1 outputs the myocardial activity parameters obtained through fitting, or it can be performed by the signal analysis device 1 itself. When the signal analysis device 1 corrects the myocardial activity parameters, for example, the myocardial activity information parameter acquisition unit 132 corrects the myocardial activity parameters obtained through the fitting and outputs the corrected values as the myocardial activity parameters. Furthermore, when the correction is performed within the signal analysis device 1, the myocardial activity parameters obtained through the fitting become intermediate parameters obtained within the device. However, as with the case where the parameters are output outside the device, they are parameters representing myocardial activity.

[0303] (Variation)

[0304] Furthermore, the electrocardiogram is preferably one that closely resembles the induction of the cardiac electromotive force vector. For example, an electrocardiogram that closely resembles the induction of the cardiac electromotive force vector is preferably one that can obtain three-dimensional information about cardiac potentials, such as the II induction, the V4 induction, and the V5 induction. The more channels an electrocardiogram has, the greater the amount of information it contains. Therefore, the more channels an electrocardiogram has, the better. Specifically, the waveforms of each channel of a multi-channel electrocardiogram can be analyzed by the signal analysis device 1 to obtain myocardial activity parameters as analysis results for each channel.

[0305] Furthermore, the signal analysis device 1 may be implemented using a plurality of information processing devices that are communicably connected via a network. In this case, the functional units included in the signal analysis device 1 may be distributed and implemented in the plurality of information processing devices.

[0306] The electrocardiogram acquisition unit 110 is an example of a biological information acquisition unit.

[0307] Furthermore, all or part of the various functions of the signal analysis device 1 may be implemented using hardware such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA (Field Programmable Gate Array). The program may be recorded on a computer-readable recording medium. Computer-readable recording media include removable media such as floppy disks, magneto-optical disks, ROMs, CD-ROMs, and storage devices such as hard disks built into computer systems. The program may also be transmitted via a telecommunications line.

[0308] While the embodiments of the present invention have been described in detail with reference to the drawings, the specific configuration is not limited to the embodiments and includes designs and the like that do not depart from the scope of the present invention.

[0309] Description of Reference Signs

[0310] 1...signal analysis device, 11...control unit, 12...input unit, 13...communication unit, 14...storage unit, 15...output unit, 91...processor, 92...memory, 110...electrocardiogram acquisition unit, 120...fitting information acquisition unit, 130...analysis unit, 131...fitting unit, 132...myocardial activity information parameter acquisition unit, 140...recording unit.

Claims

1. A signal analysis device, wherein: have: a biological information acquisition unit that acquires, as a target time waveform, a waveform of a time interval of an R wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analyzing unit obtains, when approximating the object time waveform by a time waveform, i.e., an approximate time waveform, generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or a time waveform, i.e., an approximate time waveform, generated by adding a level value to the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, at least any one of at least a part of the parameters of the first unimodal distribution, at least a part of the parameters of the first cumulative distribution function, at least a part of the parameters of the second unimodal distribution, and at least a part of the parameters of the second cumulative distribution function, as a parameter showing the activity of the myocardium of the heart.

2. A signal analysis device, wherein: have: a biological information acquisition unit that acquires, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analyzing unit takes the waveform after the time axis of the object time waveform is inverted as the object inverse time waveform, and obtains at least any one of at least a part of the parameters of the third unimodal distribution, at least a part of the parameters of the third cumulative distribution function, at least a part of the parameters of the fourth unimodal distribution, and at least a part of the parameters of the fourth cumulative distribution function when approximating the object inverse time waveform by obtaining a waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or a waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, plus a level value, as a parameter showing the activity of the myocardium of the heart.

3. A signal analysis device, wherein: have: a biological information acquisition unit that acquires, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analyzing unit uses the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, uses the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, subtracts the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and subtracts the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function. When the target time waveform is approximated, at least one of at least some of the parameters that determine the third unimodal distribution, at least some of the parameters that determine the third cumulative distribution function, at least some of the parameters that determine the fourth unimodal distribution, and at least some of the parameters that determine the fourth cumulative distribution function is obtained as a parameter indicating the activity of the myocardium of the heart, which is a time waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a time waveform generated by adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, is obtained.

4. The signal analysis device according to claim 1 or 3, wherein: The analyzing unit also obtains, as a parameter indicating the activity of the myocardium of the heart, at least one of at least a part of the parameters that determine the fifth unimodal distribution and at least a part of the parameters that determine the fifth cumulative distribution function when approximating a time waveform of the difference between the target time waveform and the approximate time waveform, namely, a residual time waveform, using a cumulative distribution function of a fifth unimodal distribution, namely, a fifth cumulative distribution function, or a time waveform obtained by multiplying the fifth cumulative distribution function by a weight, namely, an approximate residual time waveform. or The analysis unit also obtains at least any one of the parameters that determine at least a part of the parameters of the fifth unimodal distribution, at least a part of the parameters of the fifth cumulative distribution function, at least a part of the parameters of the sixth unimodal distribution, and at least a part of the parameters of the sixth cumulative distribution function when approximating the time waveform of the difference between the object time waveform and the approximate time waveform, i.e., the residual time waveform, by the time waveform generated by the difference or weighted difference between the cumulative distribution function of the fifth unimodal distribution, i.e., the fifth cumulative distribution function and the cumulative distribution function of the sixth unimodal distribution, as a parameter showing the activity of the myocardium of the heart.

5. The signal analysis device according to claim 1, wherein: When the target time waveform is an R wave, the analyzing unit sets the potential at the starting end of the target time waveform as the level value.

6. The signal analysis device according to claim 2 or 3, wherein: When the target time waveform is a T wave, the analyzing unit sets the potential at the end of the target time waveform as the level value.

7. The signal analysis device according to any one of claims 1 to 3, wherein: The unimodal distributions are Gaussian distributions.

8. A signal analysis method, wherein: include: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of an R wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analysis step is to obtain a time waveform, i.e., an approximate time waveform, generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or a time waveform, i.e., an approximate time waveform, generated by adding the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, to approximate the object time waveform, and at least any one of the parameters of determining at least a part of the parameters of the first unimodal distribution, determining at least a part of the parameters of the first cumulative distribution function, determining at least a part of the parameters of the second unimodal distribution, and determining at least any one of the parameters of at least a part of the parameters of the second cumulative distribution function as a parameter showing the activity of the myocardium of the heart.

9. A signal analysis method, wherein: include: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; as well as The analysis step is to obtain a waveform after the time axis of the object time waveform is inverted as the object inverse time waveform, and obtain a waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or a waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, plus a level value to approximate the object inverse time waveform, and at least any one of at least a part of the parameters of the third unimodal distribution, at least a part of the parameters of the third cumulative distribution function, at least a part of the parameters of the fourth unimodal distribution, and at least a part of the parameters of the fourth cumulative distribution function as a parameter showing the activity of the myocardium of the heart.

10. A signal analysis method, wherein: include: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; as well as The analyzing step includes: using the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, using the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, subtracting the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and subtracting the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function; and obtaining, as a parameter indicating the activity of the myocardium of the heart, at least one of the parameters that determine the third unimodal distribution, at least one of the parameters that determine the third cumulative distribution function, at least one of the parameters that determine the fourth unimodal distribution, and at least one of the parameters that determine the fourth cumulative distribution function when approximating the target time waveform, namely, a time waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a time waveform generated by adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function.

11. A recording medium having a computer program recorded thereon, wherein the computer program executes the following steps: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of an R wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analysis step is to obtain a time waveform, i.e., an approximate time waveform, generated by the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, or a time waveform, i.e., an approximate time waveform, generated by adding the difference or weighted difference between the cumulative distribution function of the first unimodal distribution, i.e., the first cumulative distribution function, and the cumulative distribution function of the second unimodal distribution, i.e., the second cumulative distribution function, to approximate the object time waveform, and at least any one of the parameters of determining at least a part of the parameters of the first unimodal distribution, determining at least a part of the parameters of the first cumulative distribution function, determining at least a part of the parameters of the second unimodal distribution, and determining at least any one of the parameters of at least a part of the parameters of the second cumulative distribution function as a parameter showing the activity of the myocardium of the heart.

12. A recording medium having a computer program recorded thereon, wherein the computer program executes the following steps: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analysis step is to obtain a waveform after the time axis of the object time waveform is inverted as the object inverse time waveform, and obtain a waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, or a waveform generated by the difference or weighted difference between the cumulative distribution function of the third unimodal distribution, i.e., the third cumulative distribution function, and the cumulative distribution function of the fourth unimodal distribution, i.e., the fourth cumulative distribution function, plus a level value to approximate the object inverse time waveform, and at least any one of at least a part of the parameters of the third unimodal distribution, at least a part of the parameters of the third cumulative distribution function, at least a part of the parameters of the fourth unimodal distribution, and at least a part of the parameters of the fourth cumulative distribution function as a parameter showing the activity of the myocardium of the heart.

13. A recording medium having a computer program recorded thereon, wherein the computer program executes the following steps: a biological information acquisition step of acquiring, as a target time waveform, a waveform of a time interval of a T wave included in a waveform representing one cardiac cycle of a heart to be analyzed; and The analyzing step includes: using the cumulative distribution function of the third unimodal distribution as the third cumulative distribution function, using the cumulative distribution function of the fourth unimodal distribution as the fourth cumulative distribution function, subtracting the third cumulative distribution function from 1 as the third inverse cumulative distribution function, and subtracting the fourth cumulative distribution function from 1 as the fourth inverse cumulative distribution function; and obtaining, as a parameter indicating the activity of the myocardium of the heart, at least one of the parameters that determine the third unimodal distribution, at least one of the parameters that determine the third cumulative distribution function, at least one of the parameters that determine the fourth unimodal distribution, and at least one of the parameters that determine the fourth cumulative distribution function when approximating the target time waveform, namely, a time waveform generated by the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function, or a time waveform generated by adding a level value to the difference or weighted difference between the third inverse cumulative distribution function and the fourth inverse cumulative distribution function.

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