Myocardial ischemia index prediction device and method, electronic equipment and storage medium

Abstract

The invention provides a myocardial ischemia index prediction device and method, electronic equipment and a storage medium, the myocardial ischemia index prediction device comprises: a data acquisition module for acquiring a training data set, the data of the training data set comprising magnetocardiogram data and a myocardial ischemia index; the model training module is used for training a deep learning model according to the training data set to obtain a myocardial ischemia index prediction model; and the prediction result module is used for obtaining a myocardial ischemia index prediction result according to the myocardial ischemia index prediction model. The myocardial ischemia index prediction device can obtain a more accurate myocardial ischemia index prediction model, and the obtained myocardial ischemia index prediction model can obtain more accurate myocardial ischemia indexes without other data except for magnetocardiogram data.

Description

Technical Field

[0001] The present application belongs to the technical field of magnetocardiographic signal processing, and relates to a myocardial ischemia index prediction device, and in particular to a myocardial ischemia index prediction device, method, electronic equipment and storage medium. Background Art

[0002] Magnetocardiographic signal processing is an important technology used to study and diagnose heart diseases. Magnetocardiographic signals are weak magnetic fields generated by the heart, which can be detected and recorded by magnetically sensitive devices. These signals contain rich information that can help doctors understand the functional state and pathological changes of the heart. Magnetocardiographic signal processing involves knowledge and technology in multiple fields, including biomedical engineering, signal processing, computer science, and medical imaging. In terms of signal acquisition, magnetically sensitive devices need to have high sensitivity and high spatial resolution in order to accurately record the weak magnetic fields generated by the heart. In terms of signal processing, digital filtering, time-frequency analysis, spectral analysis and other technologies are needed to denoise, enhance and extract features from the collected magnetic heart signals in order to obtain reliable information.

[0003] At present, the main examinations for myocardial ischemia include computed tomography angiography (CTA), single photon emission computed tomography (SPECT), coronary arteriography (CAG), and fractional flow reserve (FFR). These examination methods are usually cumbersome and time-consuming, and are harmful to the human body to a certain extent, such as injection of contrast agents, radionuclides, etc., and the invasiveness of FFR. In recent years, with the development of magnetic heart technology, magnetic heart examination has reached a mature and usable level. Magnetic heart examination can reflect the electrical activity of the heart muscle and has a high sensitivity. It can detect even the slightest changes in the heart's activity. A large amount of research data proves that magnetic heart examination can be used to examine myocardial activity. Existing methods for predicting myocardial ischemia indicators based on magnetic heart examination include methods based on artificial rules, machine learning methods based on expert features, and model methods based on deep learning. Methods based on artificial rules and machine learning usually build models based on a small amount of data in a single examination scenario, usually dozens to hundreds of data; methods based on deep learning usually train models based on a certain scale of data in a single examination scenario, usually hundreds of data. These methods are usually validation model methods in a specific examination scenario, which are different from the actual magnetic heart examination in various scenarios and cannot be well applied to the actual application of magnetic heart examination. Summary of the invention

[0004] At least to address the above-mentioned problems, an embodiment of the present application provides a myocardial ischemia index prediction device, which includes: a data acquisition module, used to acquire a training data set, the data of the training data set including magnetic heart data and myocardial ischemia indicators; a model training module, used to train a deep learning model according to the training data set to obtain a myocardial ischemia index prediction model; and a prediction result module, used to obtain a myocardial ischemia index prediction result according to the myocardial ischemia index prediction model.

[0005] In a first aspect, the present application provides a myocardial ischemia index prediction device, which includes: a data acquisition module for acquiring a training data set, wherein the data of the training data set includes magnetic cardiotoc data and myocardial ischemia indicators; a model training module for training a deep learning model according to the training data set to obtain a myocardial ischemia index prediction model; and a prediction result module for obtaining a myocardial ischemia index prediction result according to the myocardial ischemia index prediction model.

[0006] In this application, the magnetic heart data and myocardial ischemia index are used as a unified data set to train the deep learning model, and then the myocardial ischemia index is obtained according to the obtained myocardial ischemia index prediction model. This method of myocardial ischemia index prediction device can obtain the myocardial ischemia index without other data except magnetic heart data, and the obtained myocardial ischemia index is more accurate.

[0007] In an implementation of the first aspect, the data acquisition module includes: a magnetic heart data acquisition unit, used to acquire the magnetic heart data through magnetic heart examination; a myocardial ischemia index acquisition unit, used to acquire the myocardial ischemia index corresponding to the magnetic heart data through heart examination; a data set acquisition unit, used to take the magnetic heart data and the myocardial ischemia index corresponding to the magnetic heart data as a data sample, and acquire the training data set based on multiple data samples.

[0008] In an implementation of the first aspect, the cardiac examination includes a coronary artery computed tomography examination, a single photon emission computed tomography examination, a coronary angiography examination and / or a blood flow reserve fractional examination.

[0009] In an implementation of the first aspect, the model training module includes: a model optimization unit, which is used to obtain a loss function of the myocardial ischemia index prediction model using an actual myocardial ischemia index and a predicted myocardial ischemia index.

[0010] In an implementation of the first aspect, the prediction result module includes: a magnetocardiogram examination unit, used to obtain magnetocardiogram data to be processed based on magnetocardiogram examination; and a myocardial ischemia index acquisition unit, used to obtain the myocardial ischemia index prediction result based on the magnetocardiogram data to be processed and the myocardial ischemia index prediction model.

[0011] In an implementation of the first aspect, the prediction result module also includes: a result analysis unit, used to judge the size of the myocardial ischemia index prediction result and a first threshold value; if the myocardial ischemia index prediction result is greater than or equal to the first threshold value, the myocardial ischemia result is determined to be 1; if the myocardial ischemia index prediction result is less than the first threshold value, the myocardial ischemia result is determined to be 0.

[0012] In an implementation of the first aspect, the myocardial ischemia index prediction model includes a three-dimensional convolution module, a connection layer of multi-size convolution kernels, a ViT module and a prediction head.

[0013] In a second aspect, the present application provides a method for predicting myocardial ischemia indicators, comprising: obtaining a training data set, wherein the data in the training data set include magneticcardiogram data and myocardial ischemia indicators; training a deep learning model according to the training data set to obtain a myocardial ischemia indicator prediction model; and obtaining a myocardial ischemia indicator prediction result according to the myocardial ischemia indicator prediction model.

[0014] In a third aspect, the present application provides an electronic device, comprising: a memory for storing a computer program; and a processor for executing the computer program stored in the memory, so that the electronic device executes the myocardial ischemia index prediction method as described in the second aspect.

[0015] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the myocardial ischemia index prediction method described in the second aspect. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 Shown is a schematic diagram of an application scenario of the myocardial ischemia index prediction device described in the present application.

[0017] Figure 2 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0018] Figure 3 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0019] Figure 4 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0020] Figure 5 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0021] Figure 6 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0022] Figure 7 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0023] Figure 8 Shown is a schematic diagram of the structure of the myocardial ischemia index prediction device described in an embodiment of the present application.

[0024] Fig. 9 Shown is a flow chart of the myocardial ischemia index prediction method described in an embodiment of the present application.

[0025] Fig.10 Shown is a schematic diagram of the structure of an electronic device described in an embodiment of the present application.

[0026] Component number description

[0027] 1 Magnetocardiography device

[0028] 11. Magnetocardiography equipment

[0029] 12 Processor

[0030] 13 Display Terminal

[0031] 100 Myocardial ischemia index prediction device

[0032] 110 Data acquisition module

[0033] 111 Magnetocardiographic data acquisition unit

[0034] 112 Myocardial ischemia index acquisition unit

[0035] 113 Dataset acquisition unit

[0036] 120 Model training module

[0037] 121 Model Optimization Unit

[0038] 130 Prediction result module

[0039] 131 Magnetocardiography Unit

[0040] 132 Myocardial ischemia index acquisition unit

[0041] 133 Result Analysis Unit

[0042] 200 Electronic equipment

[0043] 210 Memory

[0044] 220 processors

[0045] 230 Display

[0046] Steps S11 to S13 DETAILED DESCRIPTION

[0047] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0048] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present application, and thus the drawings only show components related to the present application rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed at will, and the component layout may also be more complicated.

[0049] Figure 1 The display is a schematic diagram of an application scenario of the myocardial ischemia index prediction device described in the present application. The magnetic heart examination device 1 can be used to deploy the myocardial ischemia index prediction device provided in the embodiment of the present application, but the application scenario of the myocardial ischemia index prediction device provided in the embodiment of the present application is not limited to Figure 1 The magnetic heart examination device 1 shown. Figure 1 As shown, the magnetic heart examination device 1 includes a magnetic heart examination device 11, a processor 12 and a display terminal 13. The myocardial ischemia index prediction device provided in the embodiment of the present application can be deployed in the processor 12.

[0050] in, Figure 1 The server 11 in the example may be a processor cluster or a cloud computing center composed of a single processor or multiple processors, and the specific details are not limited here. Figure 1 Only one magnetic heart examination device 11, one processor 12 and one display terminal 13 are shown, but it should be understood that Figure 1 The examples are only used to understand this solution. The specific number of display terminals and servers should be flexibly determined based on actual conditions.

[0051] In some other implementations, the magnetic heart examination device 1 may not include the display terminal 13, but only include a processor 12 with a display function and a magnetic heart examination device 11. The processor 12 with a display function may include a tablet computer, a laptop computer, a PDA, a mobile phone, a personal computer (PC), which is not limited here.

[0052] The technical solutions in the embodiments of the present application will be described in detail below in conjunction with the drawings in the embodiments of the present application.

[0053] The following embodiments of the present application provide a myocardial ischemia index prediction device 100, which can be used, for example, Figure 1 The processor 12 shown implements . Figure 2 The structure diagram of the myocardial ischemia index prediction device 100 according to the embodiment of the present application is shown as follows: Figure 2 As shown, the myocardial ischemia index prediction device 100 includes a model training module 120 and a prediction result module 130 .

[0054] The data acquisition module 110 is used to acquire a training data set, wherein the data of the training data set includes magnetic heart data and myocardial ischemia index. Optionally, the magnetic heart data and myocardial ischemia index are used as a sample data, and a plurality of the sample data constitute the training data set.

[0055] The model training module 120 is used to train the deep learning model according to the training data set to obtain a myocardial ischemia index prediction model.

[0056] The prediction result module 130 is used to obtain a myocardial ischemia index prediction result according to the myocardial ischemia index prediction model.

[0057] In the embodiment of the present application, the magnetic heart data and the myocardial ischemia index are used as a unified data set to train the deep learning model, and then the myocardial ischemia index is obtained according to the obtained myocardial ischemia index prediction model. The myocardial ischemia index prediction device in this way can obtain the myocardial ischemia index without other data except the magnetic heart data, and the obtained myocardial ischemia index is also more accurate.

[0058] Figure 3 The schematic diagram of the structure of the myocardial ischemia index prediction device described in the embodiment of the present application is shown. Figure 3 As shown, the data acquisition module 110 includes a magnetocardiographic data acquisition unit 111 , a myocardial ischemia index acquisition unit 112 and a data set acquisition unit 113 .

[0059] The magnetocardiographic data acquisition unit 111 is used to acquire the magnetocardiographic data through magnetocardiographic examination.

[0060] The myocardial ischemia index obtaining unit 112 is used to obtain the myocardial ischemia index through a heart examination.

[0061] The data set acquisition unit 113 is used to take the magnetocardiographic data and the myocardial ischemia index corresponding to the magnetocardiographic data as a data sample, and acquire the training data set according to a plurality of the data samples.

[0062] In some possible implementations, the magnetic heart data of a subject or patient is obtained. If the source of the magnetic heart data is a physical examination channel, the myocardial ischemia index (Ischemia) label corresponding to the magnetic heart data of a normal subject is set to 0 according to the physical examination diagnosis result, and the magnetic heart data of an abnormal subject is discarded. If the source of the magnetic heart data is a cardiac examination such as CTA, SPECT, CAG or FFR, the value of the myocardial ischemia label is obtained by the myocardial ischemia index of the examination.

[0063] In the embodiment of the present application, the acquired magnetic heart data and the myocardial ischemia index of the magnetic heart data constitute a data sample, and multiple data samples constitute a training data set as training data for the deep learning model. The myocardial ischemia index prediction model obtained by such a training data set can obtain the myocardial ischemia index without the need for complex examinations, and the obtained myocardial ischemia index is also more accurate.

[0064] In one embodiment of the present application, the cardiac examination includes a coronary artery computed tomography examination, a single photon emission computed tomography examination, a coronary angiography examination and / or a blood flow reserve fraction examination.

[0065] In one embodiment of the present application, the model training module 120 includes a model optimization unit 121 .

[0066] The model optimization unit 121 is used to obtain the loss function of the myocardial ischemia index prediction model by using the actual myocardial ischemia index and the predicted myocardial ischemia index.

[0067] In some possible implementations, Figure 4 The structure diagram of the myocardial ischemia index prediction model described in the embodiment of the present application is shown. Figure 4 As shown, the training data set used to train the myocardial ischemia index prediction model is a magnetic heart signal obtained through a magnetic heart examination, and a label of myocardial ischemia obtained through a heart examination corresponding to the magnetic heart signal.

[0068] In some other possible implementations, Figure 5 The schematic diagram of the structure of the myocardial ischemia index prediction device described in the embodiment of the present application is shown. Figure 5As shown, a data sample is obtained from the training data set, the magnetic heart signal of the data sample is input into the deep learning model, and the myocardial ischemia label result is predicted according to the deep learning model. The error between the predicted myocardial ischemia label result and the myocardial ischemia label result of the data sample is calculated. The error can be calculated using a cross entropy loss function or a mean square error loss function. Optionally, a partial differential derivative of the loss with respect to the model parameter is performed once, and the partial derivative of the model parameter with respect to the loss can be obtained, and the model parameter value is adjusted in the opposite direction according to the partial derivative. For example, the model parameter is represented by θ, and the calculated partial derivative is B, then the calculation formula is:

[0069] θ 2 =θ 1 -αB,

[0070] Among them, α represents the preset learning rate, θ 1 and θ 2 They are the parameter status before and after the update respectively.

[0071] Figure 6 The schematic diagram of the structure of the myocardial ischemia index prediction device described in the embodiment of the present application is shown. Figure 6 As shown, the prediction result module 130 includes a magnetic heart examination unit 131 and a myocardial ischemia index acquisition unit 132 .

[0072] The magnetocardiography examination unit 131 is used to obtain magnetocardiography data to be processed according to magnetocardiography examination.

[0073] The myocardial ischemia index acquisition unit 132 is used to acquire the myocardial ischemia index prediction result according to the magnetocardiographic data to be processed and the myocardial ischemia index prediction model.

[0074] Figure 7 The schematic diagram of the structure of the myocardial ischemia index prediction device described in the embodiment of the present application is shown. Figure 7 As shown, the prediction result module 130 further includes a result analysis unit 133 .

[0075] The result analysis unit 133 is used to determine the size of the myocardial ischemia index prediction result and the first threshold value. If the myocardial ischemia index prediction result is greater than or equal to the first threshold value, the myocardial ischemia result is determined to be 1; if the myocardial ischemia index prediction result is less than the first threshold value, the myocardial ischemia result is determined to be 0.

[0076] Figure 8 The schematic diagram of the structure of the myocardial ischemia index prediction device described in the embodiment of the present application is shown. Figure 8 As shown, the myocardial ischemia model includes a three-dimensional convolution module, a connection layer of multi-size convolution kernels, a ViT module and a prediction head.

[0077] In some possible implementations, magnetic heart data is obtained, and the magnetic heart data is divided into a QRS band and a T band. The QRS band and the T band are respectively input into a three-dimensional convolution module, and after convolution processing by the three-dimensional convolution module, they are input into a connection layer of a basic module and a multi-scale convolution kernel, and then the output results of the QRS band and the T band in the connection layer of the multi-scale convolution kernel are integrated and input into a ViT module, and the ViT module uses a Vision Transformer model. The results of the ViT model are input into a prediction head, and the prediction head outputs a myocardial ischemia index.

[0078] Fig. 9 The flowchart of the method for predicting myocardial ischemia index described in the embodiment of the present application is shown. Fig. 9 As shown, the myocardial ischemia index prediction method includes the following steps S11 to S13.

[0079] Step S11, obtaining a training data set, wherein the data of the training data set include magnetic heart data and myocardial ischemia index.

[0080] Step S12: training the deep learning model according to the training data set to obtain a myocardial ischemia index prediction model.

[0081] Step S13, obtaining a myocardial ischemia index prediction result according to the myocardial ischemia index prediction model.

[0082] It should be noted that steps S11 to S13 in the myocardial ischemia index prediction method are similar to Figure 2 The modules 110 to 130 of the myocardial ischemia index prediction device 100 are in one-to-one correspondence and are not described in detail here.

[0083] In the several embodiments provided in the present application, it should be understood that the disclosed system, device or method can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of modules / units is only a logical function division. There may be other division methods in actual implementation, such as multiple modules or units can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or modules or units, which can be electrical, mechanical or other forms.

[0084] The modules / units described as separate components may or may not be physically separated, and the components displayed as modules / units may or may not be physical modules, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules / units may be selected according to actual needs to achieve the purpose of the embodiments of the present application. For example, the functional modules / units in the various embodiments of the present application may be integrated into one processing module, or each module / unit may exist physically separately, or two or more modules / units may be integrated into one module / unit.

[0085] Those of ordinary skill in the art should further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0086] An embodiment of the present application also provides an electronic device. Fig.10 The structure diagram of the electronic device 900 described in the embodiment of the present application is shown. Fig.10 As shown, in this embodiment, the electronic device 200 includes a memory 210 and a processor 220 .

[0087] The memory 210 is used to store computer programs; preferably, the memory 210 includes: ROM, RAM, magnetic disk, USB flash drive, memory card or optical disk, etc., various media that can store program codes.

[0088] Specifically, the memory 210 may include a computer system readable medium in the form of a volatile memory, such as a random access memory (RAM) and / or a cache memory. The electronic device 200 may further include other removable / non-removable, volatile / non-volatile computer system storage media. The memory 210 may include at least one program product having a set (e.g., at least one) of program modules that are configured to perform the functions of the various embodiments of the present application.

[0089] The processor 220 is connected to the memory 210 and is used to execute the computer program stored in the memory 210 so that the electronic device 200 executes the myocardial ischemia index prediction method described in any embodiment of the present application.

[0090] Optionally, the processor 220 can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, and discrete hardware components.

[0091] Optionally, the electronic device 200 in this embodiment may further include a display 230. The display 230 is communicatively connected to the memory 210 and the processor 220, and is used to display a graphical user interface (GUI) interaction interface related to the myocardial ischemia index prediction method in this embodiment of the application.

[0092] The embodiment of the present application further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method for predicting myocardial ischemia indexes described in any embodiment of the present application is implemented.

[0093] The descriptions of the processes or structures corresponding to the above-mentioned figures have different emphases. For parts that are not described in detail in a certain process or structure, please refer to the relevant descriptions of other processes or structures.

[0094] The above embodiments are merely illustrative of the principles and effects of the present application and are not intended to limit the present application. Anyone familiar with the technology may modify or change the above embodiments without violating the spirit and scope of the present application. Therefore, all equivalent modifications or changes made by a person of ordinary skill in the art without departing from the spirit and technical ideas disclosed in the present application shall still be covered by the claims of the present application.

Claims

1. A myocardial ischemia index prediction device, It is characterized in that include: A data acquisition module, used to acquire a training data set, wherein the data of the training data set includes magnetic heart data and myocardial ischemia index; A model training module, used to train a deep learning model according to the training data set to obtain a myocardial ischemia index prediction model; The prediction result module is used to obtain the myocardial ischemia index prediction result according to the myocardial ischemia index prediction model.

2. The myocardial ischemia index prediction device according to claim 1, It is characterized in that The data acquisition module comprises: A magnetocardiogram data acquisition unit, configured to acquire the magnetocardiogram data through magnetocardiogram examination; a myocardial ischemia index obtaining unit, configured to obtain the myocardial ischemia index corresponding to the magnetic heart data through a cardiac examination; The data set acquisition unit is used to take the magnetocardiographic data and the myocardial ischemia index corresponding to the magnetocardiographic data as a data sample, and acquire the training data set according to a plurality of the data samples.

3. The myocardial ischemia index prediction device according to claim 2, It is characterized in that The cardiac examination includes a coronary artery computed tomography examination, a single photon emission computed tomography examination, a coronary angiography examination and / or a fractional flow reserve examination.

4. The myocardial ischemia index prediction device according to claim 1, It is characterized in that The model training module includes: The model optimization unit is used to obtain the loss function of the myocardial ischemia index prediction model using the actual myocardial ischemia index and the predicted myocardial ischemia index.

5. The myocardial ischemia index prediction device according to claim 1, It is characterized in that The prediction result module comprises: A magnetocardiographic examination unit, used for obtaining magnetocardiographic data to be processed according to magnetocardiographic examination; The myocardial ischemia index acquisition unit is used to acquire the myocardial ischemia index prediction result according to the magnetocardiographic data to be processed and the myocardial ischemia index prediction model.

6. The myocardial ischemia index prediction device according to claim 5, It is characterized in that The prediction result module also includes: The result analysis unit is used to determine the size of the myocardial ischemia index prediction result and the first threshold value. If the myocardial ischemia index prediction result is greater than or equal to the first threshold value, the myocardial ischemia result is determined to be 1; if the myocardial ischemia index prediction result is less than the first threshold value, the myocardial ischemia result is determined to be 0.

7. The myocardial ischemia index prediction device according to claim 1, It is characterized in that The myocardial ischemia index prediction model includes: a three-dimensional convolution module, a connection layer of multi-size convolution kernels, a ViT module and a prediction head.

8. A method for predicting myocardial ischemia indicators, It is characterized in that include: Acquire a training data set, wherein the data of the training data set includes magnetic heart data and myocardial ischemia index; Training the deep learning model according to the training data set to obtain a myocardial ischemia index prediction model; A myocardial ischemia index prediction result is obtained according to the myocardial ischemia index prediction model.

9. An electronic device, It is characterized in that The electronic device comprises: Memory for storing computer programs; A processor, wherein the processor is used to execute the computer program stored in the memory so that the electronic device executes the myocardial ischemia index prediction method as claimed in claim 8.

10. A computer-readable storage medium having a computer program stored thereon, It is characterized in that When the program is executed by a processor, the method for predicting myocardial ischemia indicators according to claim 8 is implemented.

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