CN116712080A

CN116712080A - Myocardial infarction detection and positioning method, device and medium based on single-lead electrocardiogram

Info

Publication number: CN116712080A
Application number: CN202310741361.0A
Authority: CN
Inventors: 董训德
Original assignee: South China University of Technology SCUT
Current assignee: South China University of Technology SCUT
Priority date: 2023-06-20
Filing date: 2023-06-20
Publication date: 2023-09-08

Abstract

The application discloses a single-lead electrocardiogram-based myocardial infarction detection and positioning method, device and medium, and belongs to the technical field of medical signal processing. The method comprises the following steps: acquiring an electrocardiosignal, and denoising the electrocardiosignal; dividing the denoised electrocardiosignals to obtain heart beats, and performing dimension reduction on the heart beats; and classifying the normal heart beat and the myocardial infarction dead heart beat by adopting a random forest method by taking the heart beat after dimension reduction as a classification characteristic. The application obtains the heart beat by processing the electrocardiosignal, classifies the heart beat as the characteristic, and gives out whether myocardial infarction or the location of myocardial infarction, so that the detection and the location of patients with myocardial infarction medical history can be more accurate.

Description

Myocardial infarction detection and positioning method, device and medium based on single-lead electrocardiogram

Technical Field

The application relates to the technical field of medical signal processing, in particular to a single-lead electrocardiogram-based myocardial infarction detection and positioning method, device and medium.

Background

Myocardial infarction is caused by myocardial ischemia due to coronary artery obstruction, and myocardial necrosis can be seriously caused, so that the threat to human health and life is not neglected. Early diagnosis and positioning have a vital effect on the treatment and prognosis of myocardial infarction, can help doctors to discover and treat myocardial infarction in time, avoid myocardial necrosis and reduce death rate. Can also help doctors to know the condition of patients and treat the symptoms, thereby shortening the treatment time and relieving the pain of the patients. Therefore, myocardial infarction detection and myocardial infarction location positioning have been one of the hot spots of clinical research. However, a simple technical scheme for detecting and positioning myocardial infarction is not available at present.

Disclosure of Invention

In order to solve at least one of the technical problems existing in the prior art to a certain extent, the application aims to provide a single-lead electrocardiogram-based myocardial infarction detection and positioning method, device and medium.

The technical scheme adopted by the application is as follows:

a myocardial infarction detection and positioning method based on single-lead electrocardiogram comprises the following steps:

acquiring an electrocardiosignal, and denoising the electrocardiosignal;

dividing the denoised electrocardiosignals to obtain heart beats, and performing dimension reduction on the heart beats;

and classifying the normal heart beat and the myocardial infarction dead heart beat by adopting a random forest method by taking the heart beat after dimension reduction as a classification characteristic.

Further, the denoising processing for the electrocardiosignal includes:

and removing baseline drift noise, power frequency noise and high-frequency noise of the electrocardiosignal.

Further, the removing baseline drift noise, power frequency noise and high frequency noise of the electrocardiograph signal includes:

using a 5-order high-pass Butterworth filter with a cutoff frequency of 0.5Hz to filter high-frequency noise in the electrocardiosignal;

a moving average filter is used to remove the power frequency interference of 50Hz to the electrocardiosignal, wherein the nuclear width is the period number of the 50Hz waveform.

Further, the segmenting the denoised electrocardiograph signal to obtain a heart beat includes:

performing R wave crest detection on the electrocardiosignal by using a Pan-Tomkins algorithm, and acquiring a heart beat according to the detected R wave crest; one of the R peaks corresponds to one beat.

Further, the step of performing dimension reduction processing on the heart beat comprises the following steps:

and performing dimension reduction treatment on the heart beats by adopting a principal component analysis method to obtain heart beats with preset dimensions for classification.

Further, the myocardial infarction dead heart beat includes the following 11 classes: lower wall myocardial infarction, anterior dividing wall myocardial infarction, anterior external wall myocardial infarction, anterior wall myocardial infarction, lower posterior wall myocardial infarction, lower lateral wall myocardial infarction, posterior wall myocardial infarction, lateral wall myocardial infarction, anterior septal lateral wall myocardial infarction, and lower posterior wall myocardial infarction;

the method for classifying the normal heart beat and the myocardial infarction dead heart beat by adopting the heart beat after dimension reduction as the classification characteristic and adopting a random forest method comprises the following steps:

and adopting the electrocardio data in the PTB diagnosis electrocardio database as experimental data to learn and train the decision tree.

Further, the single-lead electrocardiogram-based myocardial infarction detection and positioning method further comprises the following steps:

a 5-fold cross-validation experiment and a 10-fold cross-validation experiment were performed, respectively, to avoid the impact of the selection of training samples on the classification effect.

The application adopts another technical scheme that:

a single-lead electrocardiogram-based myocardial infarction detection and localization system, comprising:

the signal processing module is used for acquiring electrocardiosignals and denoising the electrocardiosignals;

the heart beat segmentation module is used for carrying out segmentation processing on the electrocardiosignals subjected to the denoising processing to obtain heart beats and carrying out dimension reduction processing on the heart beats;

and the heart beat classification module is used for classifying the normal heart beat and the myocardial infarction dead heart beat by adopting a random forest method by adopting the heart beat after dimension reduction as a classification characteristic.

The application adopts another technical scheme that:

a single-lead electrocardiogram-based myocardial infarction detection and positioning device, comprising:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method as described above.

The application adopts another technical scheme that:

a computer readable storage medium, in which a processor executable program is stored, which when executed by a processor is adapted to carry out the method as described above.

The beneficial effects of the application are as follows: the application processes the electrocardiosignal to obtain the heart beat, classifies the heart beat as the characteristic, and gives out whether myocardial infarction or the location of myocardial infarction, thereby realizing the simple and accurate detection and location of patients with medical history of myocardial infarction.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following description is made with reference to the accompanying drawings of the embodiments of the present application or the related technical solutions in the prior art, and it should be understood that the drawings in the following description are only for convenience and clarity of describing some embodiments in the technical solutions of the present application, and other drawings may be obtained according to these drawings without the need of inventive labor for those skilled in the art.

FIG. 1 is a diagram showing the filtering effect of a normal human II lead electrocardiosignal in an embodiment of the application;

FIG. 2 is a diagram showing the effect of filtering II-lead electrocardiosignals of patients suffering from arrhythmia in the embodiment of the application;

FIG. 3 is a graph showing the effects of detection and heart beat segmentation of the R wave of a normal human II-lead electrocardiogram in the embodiment of the application;

FIG. 4 is a graph showing the effects of detection and heart beat segmentation of an R wave of an II-lead electrocardiogram of a patient with myocardial infarction according to the embodiment of the application;

FIG. 5 is a scatter plot corresponding to the first two principal components after the center beat PCA reduces the dimension in an embodiment of the application;

FIG. 6 is a scatter plot corresponding to the first three principal components of the center beat PCA after dimension reduction in accordance with an embodiment of the present application;

fig. 7 is a schematic diagram of a classification confusion matrix when the training set=1:9 for the test set according to the embodiment of the present application;

fig. 8 is a schematic diagram of a classification confusion matrix when the training set=2:8 for the test set in the embodiment of the application;

fig. 9 is a schematic diagram of a classification confusion matrix when the training set=3:7 for the test set in the embodiment of the application;

FIG. 10 is a schematic diagram of test set identification accuracy under different ratio conditions of test sets in an embodiment of the application;

FIG. 11 is a graph of classification performance indicators for a 5-fold cross-validation experiment in accordance with an embodiment of the present application;

FIG. 12 is a graph of classification performance indicators for a 10-fold cross-validation experiment in accordance with an embodiment of the present application;

fig. 13 is a flowchart of steps of a single-lead electrocardiogram-based myocardial infarction detection and localization method according to an embodiment of the present application.

Detailed Description

Embodiments of the present application are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative only and are not to be construed as limiting the application. The step numbers in the following embodiments are set for convenience of illustration only, and the order between the steps is not limited in any way, and the execution order of the steps in the embodiments may be adaptively adjusted according to the understanding of those skilled in the art.

In the description of the present application, it should be understood that references to orientation descriptions such as upper, lower, front, rear, left, right, etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of description of the present application and to simplify the description, and do not indicate or imply that the apparatus or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application.

In the description of the present application, a number means one or more, a number means two or more, and greater than, less than, exceeding, etc. are understood to not include the present number, and above, below, within, etc. are understood to include the present number. The description of the first and second is for the purpose of distinguishing between technical features only and should not be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated or implicitly indicating the precedence of the technical features indicated.

Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.

In the description of the present application, unless explicitly defined otherwise, terms such as arrangement, installation, connection, etc. should be construed broadly and the specific meaning of the terms in the present application can be reasonably determined by a person skilled in the art in combination with the specific contents of the technical scheme.

Electrocardiography (ECG) is one of the most commonly used cardiac disease detection tools in clinic, and a clinician can realize detection of myocardial infarction according to changes in electrocardiographic waveforms, particularly ST-T segment waveforms. Therefore, the application provides a simple myocardial infarction detection and positioning method based on a principal component analysis (Principal component analysis, PCA) method and a Random Forest (RF) method for analyzing II-lead electrocardiosignals.

As shown in fig. 13, the present embodiment provides a single-lead electrocardiogram-based myocardial infarction detection and localization method, which includes the following steps:

s1, acquiring an electrocardiosignal, and denoising the electrocardiosignal;

s2, carrying out segmentation processing on the electrocardiosignals subjected to denoising processing to obtain heart beats, and carrying out dimension reduction processing on the heart beats;

s3, classifying the normal heart beat and the myocardial infarction dead heart beat by adopting a random forest method by adopting the heart beat after dimension reduction as a classification characteristic.

The above method is explained in detail below with reference to the drawings and specific examples.

The embodiment provides a single-lead electrocardiogram myocardial infarction positioning method based on PCA and RF, which specifically comprises the following steps:

step 1: and acquiring an electrocardiosignal, and denoising the electrocardiosignal, wherein the method mainly comprises the step of removing baseline drift noise, power frequency noise and other high-frequency noise.

In this embodiment, the denoising step is as follows: first, a 5-order high-pass Butterworth filter with a cut-off frequency of 0.5Hz is used to filter out high-frequency noise. Second, a moving average filter is used to remove 50Hz power frequency interference from the ECG signal, where the kernel width is the number of cycles of the 50Hz waveform.

As shown in fig. 1 and 2, fig. 1 is a filtering effect diagram of II-lead electrocardiographic signals of electrocardiographic recording (event 104/s0306 lre) of a normal person. FIG. 2 is a graph of the filtering effect of an II lead electrocardiographic signal of an electrocardiographic recording (patient 001/s0010_re) of an arrhythmia patient.

Step 2: heart beat segmentation.

The R peak needs to be detected before the heart beat segmentation, and the R peak is detected by using a Pan-Tomkins algorithm in the embodiment. Taking the R peak as the center and taking 250ms forwards and 300ms backwards as a heart beat, wherein the experimental database adopted by the embodiment is a PTB diagnosis electrocardio database (PTB Diagnostic ECG Database), the sampling frequency is 1000Hz, and one heart beat comprises 650 sampling points. FIGS. 3 and 4 show the R-wave detection results of a normal human electrocardiographic record (event 104/s0306 lre) and an II-lead electrocardiographic record (event 001/s 0010_re) of a myocardial infarction patient in the PTB diagnosis electrocardiographic database, and the heart beats obtained by dividing according to the detected positions of the R-waves. Wherein (a) in fig. 3 is an R-wave detection effect graph of a II-lead electrocardiogram of a normal person, and (b) in fig. 3 is a beat of the normal person based on the detected QRS wave segmentation; fig. 4 (a) shows the effect of detecting R waves of an II-lead electrocardiogram of a patient suffering from myocardial infarction, and fig. 4 (b) shows a heart beat of a patient suffering from myocardial infarction, which is obtained based on QRS wave segmentation detected.

Step 3: heart beat reduces the blood-lipid level.

In order to meet the low power consumption requirement of the wearable electrocardiograph monitoring equipment, the heart beats are subjected to dimension reduction processing by adopting a principal component analysis method (Principal Component Analysis, PCA), and then the heart beats after dimension reduction are classified. In the embodiment, the heart beat of 650 dimensions is reduced to 15 dimensions by adopting a PCA method, and the heart beat data of 15 dimensions after the dimension reduction keeps the variance of 98.21% of the original data, namely the cumulative variance contribution rate of the first 15 main components is 98.21%. Fig. 5 shows a two-dimensional scatter diagram formed by the first 100 normal heart beats and the first 100 abnormal heart beats with the first two principal components after the dimension is reduced, fig. 6 shows a three-dimensional scatter diagram formed by the first 100 normal heart beats and the first 100 abnormal heart beats with the first three principal components after the dimension is reduced, and it can be seen that the first three classifications have a high degree of distinction between the normal heart beats and the abnormal heart beats.

Step 4: classification with RF

And classifying the normal heart beat and the class 11 myocardial infarction dead heart beat by taking the post-dimensional heart beat (15 dimensions) as a classification characteristic and adopting a random forest method. In the embodiment, electrocardiographic data in a PTB diagnosis electrocardiographic database is used as experimental data, 368 electrocardiographic data of 148 myocardial infarction patients and 80 electrocardiographic data of 52 healthy people in the database are selected for verification, 169371 heart beats are extracted in total, wherein the total number of heart beats of myocardial infarction is 11, and the number of heart beats of each myocardial infarction and healthy heart beats is shown in table 1:

table 1 verifies the distribution of all heart beats used in the experiment

The classification effect evaluates classification performance in terms of classification accuracy, recall, accuracy, and F1 score. FIG. 7 shows a classification confusion matrix for a test set at a test set to training set ratio of 1:9, with only 6 beats misclassified, and an identification accuracy of 99.98%. FIG. 8 shows a classification confusion matrix for a test set at a test set to training set ratio of 2:8, with a recognition accuracy of 100%. FIG. 9 shows that the classification confusion matrix for the test set slightly reduces the recognition performance and the recognition accuracy is 99.88% when the ratio of the test set to the training set is 3:7. Fig. 10 shows the change of the corresponding accuracy of the test set ratio from 0.1 to 0.9 (0.1 increase each time), and it can be seen that the accuracy reaches 97.70% even though the ratio of the test set to the training set is 1:9. This fully illustrates the advantages of the method of the application.

In order to avoid the influence of the selection of training samples on the classification effect, a 5-fold cross validation experiment is performed in the embodiment, and the experimental results are shown in fig. 11 and table 2; and 10 fold cross validation experiments were performed, the results of which are shown in fig. 12 and table 3. The experimental result shows that the method has excellent classification performance on heart beats corresponding to different myocardial infarction types, and the difference of performance indexes corresponding to different folds is small, for example, the recall rate of a 1 st fold experiment in a 10-fold cross validation experiment is 99.82% at the minimum, and the difference between the recall rate and the highest recall rate is 100.00% is only 0.18%. These fully demonstrate the effectiveness and feasibility of the method.

Table 25 fold cross validation test results

	Accuracy rate of	Precision of	Recall rate of recall	F1 fraction
					Folding 1	99.91％	99.96％	99.95％	0.9995
Fold 2	99.96％	99.98％	99.95％	0.9997
					Folding 3	99.94％	99.96％	99.71％	0.9984
Fold 4	99.97％	99.98％	99.98％	0.9998
					Folding 5	99.91％	99.95％	99.90％	0.9992
Average value of	99.94％	99.97％	99.90％	0.9993

TABLE 3 results of 10 fold Cross validation experiments

In general, the embodiment provides a single-lead electrocardiogram myocardial infarction positioning method based on PCA and RF, which takes a standard II lead as a lead for positioning myocardial infarction, takes a heart beat after dimension reduction as a classification characteristic, and takes RF as a classifier, so that excellent classification performance is achieved for various myocardial infarction dead heart beats.

In practical application, after the II lead electrocardiosignals of a patient to be detected are subjected to pretreatment such as denoising, heart beat segmentation and the like, the heart beat is subjected to dimension reduction treatment by adopting PCA, and then all the heart beats of the II lead electrocardiosignals subjected to dimension reduction are classified by adopting RF, so that whether myocardial infarction or the positioning of myocardial infarction can be given according to the classification result of the heart beat. In particular, the detection and localization of patients with a history of myocardial infarction will be more accurate.

Compared with other manually selected features, the embodiment adopts the heart beat after dimension reduction as the classification feature, so that the troublesome problems of feature extraction and feature combination optimization in the traditional method are avoided. Compared with a related method based on deep learning, the method has the advantages of less parameters and low calculation amount, and the method based on deep learning generally has a complex network structure, a large number of parameters need to be adjusted, and has higher requirements on calculation resources and unexplainability.

In summary, the myocardial infarction detection and positioning method only needs the II-lead electrocardiosignal, has simple operation, low calculation amount and excellent detection performance, and is suitable for being carried into wearable electrocardiograph monitoring equipment with very limited memory and power consumption.

The embodiment also provides a myocardial infarction detection and positioning system based on a single-lead electrocardiogram, which comprises the following steps:

The myocardial infarction detection and positioning system based on the single-lead electrocardiogram can execute any combination implementation steps of the method based on the single-lead electrocardiogram provided by the method embodiment of the application, and has the corresponding functions and beneficial effects.

The embodiment also provides a myocardial infarction detection and positioning device based on the single-lead electrocardiogram, which comprises:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method as shown in fig. 13.

The myocardial infarction detection and positioning device based on the single-lead electrocardiogram can execute any combination implementation steps of the method based on the single-lead electrocardiogram provided by the method embodiment of the application, and has the corresponding functions and beneficial effects.

Embodiments of the present application also disclose a computer program product or computer program comprising computer instructions stored in a computer readable storage medium. The computer instructions may be read from a computer-readable storage medium by a processor of a computer device, and executed by the processor, to cause the computer device to perform the method shown in fig. 13.

The embodiment also provides a storage medium which stores instructions or programs for executing the single-lead electrocardiogram myocardial infarction detection and positioning method provided by the embodiment of the method, and when the instructions or programs are run, the steps can be implemented by any combination of the embodiment of the method, so that the method has the corresponding functions and beneficial effects.

In some alternative embodiments, the functions/acts noted in the block diagrams may occur out of the order noted in the operational illustrations. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved. Furthermore, the embodiments presented and described in the flowcharts of the present application are provided by way of example in order to provide a more thorough understanding of the technology. The disclosed methods are not limited to the operations and logic flows presented herein. Alternative embodiments are contemplated in which the order of various operations is changed, and in which sub-operations described as part of a larger operation are performed independently.

Furthermore, while the application is described in the context of functional modules, it should be appreciated that, unless otherwise indicated, one or more of the described functions and/or features may be integrated in a single physical device and/or software module or one or more functions and/or features may be implemented in separate physical devices or software modules. It will also be appreciated that a detailed discussion of the actual implementation of each module is not necessary to an understanding of the present application. Rather, the actual implementation of the various functional modules in the apparatus disclosed herein will be apparent to those skilled in the art from consideration of their attributes, functions and internal relationships. Accordingly, one of ordinary skill in the art can implement the application as set forth in the claims without undue experimentation. It is also to be understood that the specific concepts disclosed are merely illustrative and are not intended to be limiting upon the scope of the application, which is to be defined in the appended claims and their full scope of equivalents.

The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.

Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.

More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.

It is to be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.

In the foregoing description of the present specification, reference has been made to the terms "one embodiment/example", "another embodiment/example", "certain embodiments/examples", and the like, means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the spirit and principles of the application, the scope of which is defined by the claims and their equivalents.

While the preferred embodiment of the present application has been described in detail, the present application is not limited to the above embodiments, and various equivalent modifications and substitutions can be made by those skilled in the art without departing from the spirit of the present application, and these equivalent modifications and substitutions are intended to be included in the scope of the present application as defined in the appended claims.

Claims

1. The myocardial infarction detection and positioning method based on the single-lead electrocardiogram is characterized by comprising the following steps of:

acquiring an electrocardiosignal, and denoising the electrocardiosignal;

2. The method for detecting and locating myocardial infarction based on single lead electrocardiogram according to claim 1, wherein the denoising treatment of the electrocardiosignal comprises:

3. The single-lead electrocardiogram-based myocardial infarction detection and localization method as set forth in claim 2, wherein the removing baseline wander noise, power frequency noise, and high frequency noise of the electrocardiograph signal includes:

and removing the power frequency interference of 50Hz on the electrocardiosignal by using a moving average filter.

4. The method for detecting and locating myocardial infarction based on single lead electrocardiogram according to claim 1, wherein the dividing the denoised electrocardiograph signal to obtain a heart beat comprises:

5. The method for detecting and locating myocardial infarction based on single lead electrocardiogram according to claim 1, wherein the step of performing the dimension reduction processing on the heart beat comprises the steps of:

6. The single-lead electrocardiogram-based myocardial infarction detection and localization method as set forth in claim 1, wherein the myocardial infarction dead-beat includes the following 11 classes: lower wall myocardial infarction, anterior dividing wall myocardial infarction, anterior external wall myocardial infarction, anterior wall myocardial infarction, lower posterior wall myocardial infarction, lower lateral wall myocardial infarction, posterior wall myocardial infarction, lateral wall myocardial infarction, anterior septal lateral wall myocardial infarction, and lower posterior wall myocardial infarction;

7. The single-lead electrocardiogram-based myocardial infarction detection and localization method as set forth in claim 6, further comprising the steps of:

8. A single-lead electrocardiogram-based myocardial infarction detection and positioning device, comprising:

9. A single-lead electrocardiogram-based myocardial infarction detection and positioning device, comprising:

at least one processor;

at least one memory for storing at least one program;

the at least one program, when executed by the at least one processor, causes the at least one processor to implement the method of any one of claims 1-7.

10. A computer readable storage medium, in which a processor executable program is stored, characterized in that the processor executable program is for performing the method according to any of claims 1-7 when being executed by a processor.