WO2020052640A1

WO2020052640A1 - Deep learning algorithm-based electrocardiogram feature extraction method, apparatus, system, device, and classification method

Info

Publication number: WO2020052640A1
Application number: PCT/CN2019/105624
Authority: WO
Inventors: 李长岭; 姜文兵; 赵亚; 冷晓畅; 向建平
Original assignee: 杭州脉流科技有限公司
Priority date: 2018-09-14
Filing date: 2019-09-12
Publication date: 2020-03-19
Also published as: CN110897629A

Abstract

A deep learning algorithm-based electrocardiogram feature extraction method, an apparatus, a system, a device, and a classification method. The deep learning algorithm-based electrocardiogram feature extraction method comprises the following steps: randomly capturing a segment of continuous electrocardiogram signals in a 12-lead electrocardiogram to be processed, the electrocardiogram signals comprising at least two cardiac cycles (S1); and inputting the captured electrocardiogram signals into a feature extraction model in the form of pictures, and extracting electrocardiogram signal features, the feature extraction model being obtained by means of training on the basis of a ResNet mode, or an Inception model, or an Inception-ResNet model (S2). According to the deep learning algorithm-based electrocardiogram feature extraction method, the apparatus, the system, the device, and the classification method, incompleteness caused by artificial design of features can be reduced, thereby improving the accuracy and diversity of deep learning algorithm-based electrocardiogram feature extraction.

Description

ECG feature extraction method, device, system, device and classification method based on deep learning algorithm

Technical field

The present invention relates to the technical field of electrocardiogram signal processing, and in particular, to an electrocardiogram feature extraction method, device, system, device, and classification method based on a deep learning algorithm.

Background technique

ECG detection is the most important method for detecting and diagnosing heart diseases. The human electrocardiogram (ECG) is the comprehensive performance of the heart's electrical activity on the surface of the body. By extracting the characteristic information of the ECG, you can get the heart parts. Physiological condition.

Taking arrhythmia as an example, the current arrhythmia analysis mainly uses the waveform analysis method and the template matching method. The waveform analysis method first obtains characteristic waveform parameters, such as characteristic waveform amplitude, time length, rise / fall time, waveform interval, etc. These waveform parameters are compared with the judgment threshold obtained based on clinical experience, and the analysis result of arrhythmia can be obtained. . The template matching method mainly calculates the average RR interval and the average R wave shape of the subject's ECG signal as templates, and compares the RR interval and R wave shape of each heartbeat of the subject with the template. If the difference exceeds a certain range, arrhythmia is considered to have occurred.

The waveform analysis method uses the empirical threshold of the solidified characteristic waveform parameters as the basis for judgment. It is relatively simple and intuitive, with fewer characteristic values, limited classification types, and ECG morphology is very sensitive to noise. Various transform domains and statistical methods are useful for arrhythmia types. The definition is confusing, and the classification results and effects are also different. The template matching method can only make an effective judgment when the R-wave morphology of the subject is significantly different from that of the template, and cannot effectively judge the arrhythmia waveforms that are not significantly different.

The human body is a non-linear complex system. The shape of individual ECG signals will change over time, and physical health conditions also have a large impact on ECG signals. ECG signal data is very large and complex, and more adequate feature information extraction is required. .

technical problem

The invention provides a method, a device, a system, a device, and a classification method for extracting ECG features based on a deep learning algorithm, which improves the accuracy and diversity of ECG signal feature extraction.

Technical solutions

A method for extracting ECG features based on a deep learning algorithm includes the following steps: randomly extracting a continuous ECG signal from a 12-lead ECG to be processed, the ECG signal including at least two cardiac cycles; and the ECG signal to be intercepted The feature extraction model is input in the form of pictures, and the features of the ECG signal are extracted. The feature extraction model is obtained by training based on a ResNet model, an Inception model, or an Inception-ResNet model. The following also provides a number of optional methods, but it is not intended as an additional limitation on the above-mentioned overall scheme. It is only a further addition or preference. Without technical or logical contradiction, each of the alternative methods can be performed separately for the above-mentioned overall scheme Combination can also be a combination of multiple optional ways.

The features extracted by the feature extraction model should be understood broadly, including both the information directly extracted from the electrocardiogram and the information obtained by progressively processing the directly extracted information, and further processing including classification and other processing methods.

Optionally, the feature extraction model includes a convolution layer, a pooling layer, a dropout layer, and a fully-connected layer, wherein the convolution layer, the pooling layer, and the dropout layer all adopt a Relu activation function or a variant thereof, and are fully connected The layer uses the softmax function.

Optionally, the first convolution layer in the convolution layer includes 6 convolution channels, and the convolution kernel of each convolution channel is {{1, 1, 2}, {1, 2, 1}, {2,1,1}}.

Optionally, the size of the pooling window of the pooling layer is (2, 2). Optionally, the fully connected layer includes three layers, the number of neurons in the first layer is 448, the number of neurons in the second layer is 112, and the number of neurons in the third layer is 28. Optionally, the dormancy ratio of the neurons in the dropout layer is 0.5. An ECG feature extraction device based on a deep learning algorithm includes: a preprocessing module for randomly intercepting a continuous ECG signal from a 12-lead ECG to be processed, the ECG signal including at least two cardiac cycles; extraction The module is used for inputting the intercepted electrocardiogram signal into a feature extraction model in the form of a picture to extract the characteristics of the electrocardiogram signal; the feature extraction model is obtained by training based on a ResNet model, an Inception model, or an Inception-ResNet model. A computer device includes a memory and a processor. The memory stores a computer program. When the processor executes the computer program, the processor implements the following steps: Randomly intercepts a continuous ECG signal in a 12-lead ECG to be processed. The ECG signal includes at least two cardiac cycles; the intercepted ECG signal is input into a feature extraction model in the form of a picture to extract the characteristics of the ECG signal; the feature extraction model is based on a ResNet model, or an Inception model, or an Inception-ResNet model training get. An ECG feature extraction system based on a deep learning algorithm includes a terminal and a server, the server includes a memory and a processor, the memory stores a computer program, and the server obtains an electrocardiogram from the terminal; the processor executes all When describing a computer program, the following steps are implemented:

Randomly intercept a continuous ECG signal from the 12-lead ECG to be processed, and the ECG signal includes at least two cardiac cycles;

The intercepted electrocardiogram signal is input into a feature extraction model in the form of a picture, and the features of the electrocardiogram signal are extracted; the feature extraction model is obtained by training based on a ResNet model, or an Inception model, or an Inception-ResNet model.

A computer-readable storage medium stores a computer program in the computer-readable storage medium. When the computer program is executed by a computer processor, the following steps are implemented:

A method for classifying an ECG signal based on a deep neural network includes the following steps: Randomly intercept a segment of a continuous ECG signal from a 12-lead ECG to be processed, the ECG signal including at least two cardiac cycles; and dividing the intercepted ECG signal into The picture form is input into a classification model to obtain a classification result; the classification model is obtained by training based on a ResNet model, or an Inception model, or an Inception-ResNet model.

Beneficial effect

The ECG feature extraction method, device, system, equipment and classification method based on the deep learning algorithm provided by the present invention reduce the incompleteness caused by artificial design features and improve the accuracy of ECG feature extraction and classification based on the deep learning algorithm. And diversity.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flowchart of an ECG feature extraction method based on a deep learning algorithm in an embodiment; FIG.

FIG. 2 is a schematic diagram of an intercepted electrocardiogram signal; FIG. 3 is a schematic diagram of a computer device in one embodiment; and FIG. 4 is a flowchart of an ECG feature extraction method based on a deep learning algorithm in one embodiment.

Embodiments of the invention

In the following, the technical solutions in the embodiments of the present invention will be clearly and completely described with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

As shown in Figures 1 and 4, a method for extracting ECG features based on a deep learning algorithm includes the following steps:

In step S1, a continuous ECG signal is randomly intercepted from the 12-lead ECG to be processed, and the ECG signal includes at least two cardiac cycles.

The ECG signal (ie, the electrocardiogram signal) can be a unipolar ECG signal or a bipolar ECG signal. For example, in a 12-lead ECG, an individual's ECG can be recorded by one or more electrodes placed on the surface of the human body. The 12-lead ECG can provide spatial information about ECG activity, and each of the 12 leads represents a specific location in the space where the heart's ECG activity is measured.

Of the 12 leads, lead I (right arm to left arm), lead II (right arm to left leg) and lead III (left arm to left leg) are bipolar limb leads, leads V1, V2 , V3, V4, V5 and V6 are unipolar chest leads. In order to form a bipolar limb lead for measurement, two electrodes need to be in contact with the two skin surfaces of the limbs, such as the right and left arms. In order to form a monopolar chest lead for measurement, an electrode is required to make contact with the skin surface of the chest. The term "contact" herein may refer to the direct contact between the electrode and the bare skin, or the indirect contact between the electrode and the bare skin with a conductive material (such as a conductive patch or conductive clothing).

The ECG data originally collected was discrete data. One-dimensional ECG signals were obtained by Bspline interpolation. The one-dimensional ECG signals were pre-processed to generate noise reduction signals, and then the ECG signal feature extraction was performed.

The noise sources of the ECG signal include the background of the electromyographic signal, baseline drift, power frequency interference, and motion artifacts. Preprocessing can use existing techniques, for example, wavelet transform or filter to remove baseline drift and power frequency from the ECG signal. Interference and high-frequency noise.

In step S2, the intercepted electrocardiogram signal is input into a feature extraction model in the form of a picture, and the features of the electrocardiogram signal are extracted; the feature extraction model is obtained by training based on a ResNet model, an Inception model, or an Inception-ResNet model.

The ECG signal is input to the feature extraction model in the form of a picture as shown in FIG. 2, and the picture size of the ECG signal is 1200 × 600 pixels.

In the prior art, the process of extracting ECG features usually requires artificial design features, which are prone to miss detection and misjudgment. In this embodiment, the feature extraction model is based on the ResNet model, or Inception model, or Inception-ResNet model training, can be automatically and effectively extracted from a large number of electrocardiograms to fully describe the characteristics of the electrocardiogram, including low-dimensional heartbeat features, improve the integrity and diversity of ECG signal feature extraction, robust Stronger performance and better detection accuracy.

After the feature extraction model is used for a predetermined time, the feature extraction model is retrained to ensure that the feature extraction model has a better extraction accuracy rate.

In one embodiment, the feature extraction model includes a convolution layer, a pooling layer, a dropout layer, and a fully connected layer, wherein the convolution layer, the pooling layer, and the dropout layer all adopt a Relu activation function or a variant thereof, a fully connected layer Using the softmax function.

In one embodiment, the first convolution layer in the convolution layer includes 6 convolution channels, and the convolution kernel of each convolution channel is {{1, 1, 2}, {1, 2, 1}, {2,1,1}}.

The first convolution layer is used to enhance boundary information of a picture. In one embodiment, the pooling window size of the pooling layer is (2, 2).

The pooling layer is used for dimensionality reduction, thereby reducing operation parameters and improving calculation speed. In one embodiment, the fully connected layer includes three layers, the number of neurons in the first layer is 448, the number of neurons in the second layer is 112, and the number of neurons in the third layer is 28. The fully connected layer is used to integrate multi-channel data into a one-dimensional feature vector. According to the characteristics of the multilayer neural network, the abstract one-dimensional feature vector is classified at the fully connected layer. The number of neurons in the third layer of the fully connected layer determines the number of extracted ECG features. In this embodiment, 28 types of ECG features can be extracted. In one embodiment, the proportion of dormant neurons in the dropout layer is 0.5. A dropout layer is added between every two fully connected layers to avoid overfitting. According to the working principle of the dropout layer, the neurons that are trained each time will temporarily sleep according to the set ratio. When the next training, the neurons in this round are again Participate in training. In this embodiment, the dormancy ratio of the neurons is set to 0.5 according to experience, that is, each time an ECG sample enters the model, half of the neurons in the fully connected layer will not participate in the training of the sample.

The feature extraction model is trained in deep learning frameworks TensorFlow and Keras. The training process of the feature extraction model includes:

Step a, construct a training database and a test database. The data of the training database and test database can come from hospitals and physical examination centers. The total number of ECGs in the training database and test database is not less than 100,000, of which 70% are normal ECGs, and the rest are abnormal ECGs. Each ECG has passed at least two ECG experts Manually label abnormal features independently. There is no overlap between the ECG data in the training database and the test database, and the amount of ECG data in the training database is greater than the amount of ECG data in the test database.

The ECG data in the training database and the test database are from different individuals, including males between the ages of 10 and 99 and females between the ages of 10 and 99. The proportion of males and females is half. The training and test databases Randomly selected from the data set of the electrocardiograph, including various representative waveform and artifact record numbers, various uncommon but clinically significant data, and some complex ventricular, nodular, and supraventricular arrhythmias And conduction abnormalities.

Each ECG signal data in the training database and test database includes the ECG signal format, sampling frequency, length, and related information about the patient, such as the place of collection, the patient's condition, and medication, etc. The signal analysis results mainly include information such as heartbeat, rhythm and signal quality. The annotations of each ECG signal data in the training database and test database contain the basic information of ECG signals and the results of signal analysis by ECG diagnostic experts, such as: signal duration, heart rate, signal quality, location of arrhythmic beats, Number and arrhythmia characteristics.

The abnormal characteristics of electrical signals in the training database and test data center determine the features that the feature extraction model can extract.

The characteristics of ECG signals include, but are not limited to, the following types of heartbeats: normal, pacing, sinus tachycardia, sinus bradycardia, sinus arrhythmia, sinus arrest, escape beat, and escape beat rhythm (Further divided into atrial rhythm, borderline rhythm, ventricular rhythm, other escape beats and escape beat rhythms) Atrial premature beat, supraventricular dual rhythm, premature ventricular beat, fast ventricular rate, slow ventricular rate, supraventricular Tachycardia, ventricular tachycardia, atrial flutter, atrial fibrillation, first degree atrioventricular block, second degree atrioventricular block, third degree atrioventricular block, indoor block (further divided into left bundle branch Conduction block, right bundle branch block, left anterior branch block, other indoor blocks), noise, others.

Step b, preprocessing each ECG in the training database and the test database. For a certain ECG in the training database and the test database, a continuous ECG signal is randomly intercepted, and the ECG signal includes at least two cardiac cycles. The electrocardiogram signal is converted into a picture format, which is the same as the method for converting the central electrocardiogram signal into a picture format in step S2.

The ECG signals converted into pictures in the training database are used as input for the ResNet model (or Inception model or Inception-ResNet model) for training. The trained models are tested using the ECG in the test database. The test results are divided into four types: True positive (TP), true negative (TN), false positive (FP), false negative (FN).

The four results are used to evaluate the feature extraction results. The evaluation uses the following four parameters: Sensitivity (Sen), calculated as: Sen = TP / (TP + FN); Specificity (Spe), calculated as: Spe = TN / (FP + TN); Positive rate (PPV), calculated as: PPV = TP / (TP + FP) ; Accuracy (Acc), calculated as: Acc = (TP + TN) / (TP + FP + FN + TN). The evaluation results are shown in Table 1.

Table 1

Zh

SenSen	SpeSpe	AccAcc	PPVPPV
99%99%	98.6%98.6%	98.8%98.8%	98.1%98.1%

It should be understood that although the steps in the flowchart of FIG. 1 are sequentially displayed according to the directions of the arrows, these steps are not necessarily performed sequentially in the order indicated by the arrows. Unless explicitly stated in this document, the execution of these steps is not strictly limited, and these steps can be performed in other orders. Moreover, at least a part of the steps in FIG. 1 may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but may be executed at different times. The execution of these sub-steps or stages The sequence is not necessarily performed sequentially, but may be performed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

In one embodiment, an ECG feature extraction device based on a deep learning algorithm includes a preprocessing module for randomly intercepting a continuous ECG signal from a 12-lead ECG to be processed, where the ECG signal includes at least Two cardiac cycles; an extraction module, which is used to input the intercepted ECG signals into a feature extraction model in the form of pictures to extract the characteristics of the ECG signal; the feature extraction model is based on a ResNet model, or an Inception model, or an Inception-ResNet model. . For the function limitation in each module, please refer to the limitation on the ECG feature extraction method based on the deep learning algorithm above, which is not repeated here. Each module in the above-mentioned ECG feature extraction device based on a deep learning algorithm may be implemented in whole or in part by software, hardware, and a combination thereof. The above-mentioned modules may be embedded in the hardware form or independent of the processor in the computer device, or may be stored in the memory of the computer device in the form of software, so that the processor calls and performs the operations corresponding to the above modules.

The ECG feature extraction device based on the deep learning algorithm provided in this embodiment may be configured at a remote end, and the ECG signal may be obtained through a remote terminal connected to the device. Alternatively, the device of this embodiment may be configured at the terminal (for example, a computer used by a user). Or medical detection equipment) to obtain the ECG signal directly through the ECG acquisition device.

In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in FIG. 3. The computer equipment includes a processor, a memory, a network interface, a display screen, and an input device connected through a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for running an operating system and computer programs in a non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal through a network connection. The computer program is executed by a processor to implement an ECG feature extraction method based on a deep learning algorithm. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the computer device casing. , Or an external keyboard, trackpad, or mouse.

Those skilled in the art can understand that the structure shown in FIG. 3 is only a block diagram of a part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer equipment to which the scheme of the present application is applied. The specific computer equipment may be Include more or fewer parts than shown in the figure, or combine certain parts, or have a different arrangement of parts.

In one embodiment, a computer device is provided, including a memory and a processor. The memory stores a computer program, and the processor implements the following steps when the computer program executes:

In one embodiment, an ECG feature extraction system based on a deep learning algorithm is provided, which includes a terminal and a server. The server includes a memory and a processor. The memory stores a computer program, and the server obtains the information from the terminal. ECG; when the processor executes the computer program, the following steps are implemented:

The terminal may be, but is not limited to, a computer, a laptop, a smart phone, a tablet, and a portable wearable device. The server may be a remote background server or a server in a cloud platform. An independent server or It is implemented by a server cluster composed of multiple servers. The ECG is transmitted between the terminal and the server through a communication network. The communication network can be, but is not limited to, 3G, 4G, 5G, and wifi.

In addition to transmitting the original ECG from the terminal to the server, information related to the ECG can be transmitted from the terminal to the server. The related information of the ECG includes, but is not limited to, user information and detection time. The server receives the ECG data and transmits the extracted feature results to the terminal through the communication network. The server stores the received electrocardiogram data and feature extraction results.

In one embodiment, a computer-readable storage medium is provided. The computer-readable storage medium stores a computer program, and when the computer program is executed by a computer processor, the following steps are implemented:

Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. In execution, the computer program may include the processes of the embodiments of the methods as described above. Wherein, any reference to the storage, storage, database, or other media used in the embodiments provided in this application may include non-volatile and / or volatile storage. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

In one embodiment, an ECG signal classification method based on a deep neural network is provided, including the following steps:

The intercepted electrocardiogram signals are input into a classification model in the form of pictures to obtain a classification result; the classification model is obtained by training based on a ResNet model, or an Inception model, or an Inception-ResNet model.

The classification categories of ECG signals include, but are not limited to: normal, pacing, sinus tachycardia, sinus bradycardia, sinus arrhythmia, sinus arrest, escape beat, and escape beat rhythm (further divided into atrial Heart rhythm, border rhythm, ventricular rhythm, other escape beats and escape beat rhythms) Atrial premature beats, supraventricular duplex, premature ventricular beats, fast ventricular rate, slow ventricular rate, supraventricular tachycardia, ventricular Tachycardia, atrial flutter, atrial fibrillation, first degree atrioventricular block, second degree atrioventricular block, third degree atrioventricular block, indoor block (further divided into left bundle branch block, right bundle Branch block, left anterior branch block, other indoor blocks), noise, other.

The technical features of the above embodiments can be arbitrarily combined. In order to make the description concise, all possible combinations of the technical features in the above embodiments have not been described. However, as long as there is no contradiction in the combination of these technical features, they should be It is considered to be the range described in this specification.

The above-mentioned embodiments only express several implementation manners of the present application, and their descriptions are more specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be noted that, for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can be made, which all belong to the protection scope of the present application. Therefore, the protection scope of this application patent shall be subject to the appended claims.

Claims

A method for extracting ECG features based on a deep learning algorithm is characterized in that it includes the following steps:

Randomly intercept a continuous ECG signal from the 12-lead ECG to be processed, and the ECG signal includes at least two cardiac cycles;

The intercepted electrocardiogram signal is input into a feature extraction model in the form of a picture, and the features of the electrocardiogram signal are extracted; the feature extraction model is obtained by training based on a ResNet model, or an Inception model, or an Inception-ResNet model.
The ECG feature extraction method based on deep learning algorithm according to claim 1, wherein the feature extraction model comprises a convolution layer, a pooling layer, a dropout layer, and a fully connected layer, wherein the convolution layer, the pool The chemical layer and the dropout layer both use the Relu activation function or a variant thereof, and the fully connected layer uses the softmax function.
The ECG feature extraction method based on deep learning algorithm according to claim 2, wherein the first convolution layer in the convolution layer includes 6 convolution channels, and the convolution kernel of each convolution channel is For {{1,1,2}, {1,2,1}, {2,1,1}}.
The method for extracting ECG features based on a deep learning algorithm according to claim 2, wherein the size of the pooling window of the pooling layer is (2, 2).
The method for extracting ECG features based on a deep learning algorithm according to claim 2, wherein the fully connected layer comprises three layers, the number of neurons in the first layer is 448, and the number of neurons in the second layer Is 112, and the number of neurons in the third layer is 28.
The method for extracting ECG features based on a deep learning algorithm according to claim 2, wherein the sleep ratio of the neurons in the dropout layer is 0.5.
A device for extracting ECG features based on a deep learning algorithm, comprising: a pre-processing module for randomly intercepting a continuous ECG signal from a 12-lead ECG to be processed, the ECG signal including at least two Cardiac cycle An extraction module is used to input the intercepted electrocardiogram signal into a feature extraction model in the form of a picture to extract the characteristics of the electrocardiogram signal; the feature extraction model is obtained by training based on a ResNet model, an Inception model, or an Inception-ResNet model.
A computer device includes a memory and a processor, and the memory stores a computer program, wherein the processor implements the deep learning algorithm according to any one of claims 1 to 6 when executing the computer program. ECG feature extraction method.
An ECG feature extraction system based on a deep learning algorithm includes a terminal and a server. The server includes a memory and a processor. The memory stores a computer program. It is characterized in that the server obtains an electrocardiogram from a terminal; when the processor executes the computer program, the method for extracting an electrocardiogram feature based on a deep learning algorithm according to any one of claims 1 to 6 is implemented.
An ECG signal classification method based on a deep learning algorithm is characterized in that it includes the following steps:

Randomly intercept a continuous ECG signal from the 12-lead ECG to be processed, and the ECG signal includes at least two cardiac cycles;

The intercepted electrocardiogram signals are input into a classification model in the form of pictures to obtain a classification result; the classification model is obtained by training based on a ResNet model, an Inception model, or an Inception-ResNet model.