Research On Automatic Diagnosis Of Arrhythmia Based On Deep Learning

Cardiovascular disease ranks first among the causes of death among residents,threatening the lives of patients at all times,and there is a trend of younger patients,which poses severe challenges to the social medical system.In the diagnosis of cardiovascular diseases,doctors can obtain useful information about the patient’s cardiac function and structure by analyzing the ECG signals contained in the ECG.The single-lead ECG is suitable for preliminary screening,and the multi-lead ECG is suitable for detailed Comprehensive assessment.However,misdiagnosis often occurs during the diagnosis of cardiovascular diseases,which will delay the optimal treatment time for patients.This paper draws on the advantages of deep learning in automatic feature extraction and classification accuracy,starting from the problem of low accuracy of single-lead ECG and the problem of low classification efficiency of multi-lead ECG due to data mixing,and aims at the detection and classification of arrhythmia.Diagnosis conducts research with the aim of proposing efficient arrhythmia classification algorithms and applying them to the arrhythmia classification platform.The research content of this paper mainly includes:(1)For single-lead ECG,this paper designs a 1DC-RES convolutional neural network.The research on the preprocessing method of ECG data is carried out,and the corresponding filter is proposed to filter out noise interference and other problems,and improve the quality of ECG signal.The 1DC-RES model is based on the residual network,using residual nesting to mine the characteristics of ECG data,avoiding the problem of gradient disappearance,and increasing the depth and width of the model to achieve effective recognition and analysis of ECG signals.Compared with the existing models,the experimental results show that the recognition accuracy of 1DC-RES model can reach 96.89%.(2)For multi-lead ECG,this paper designs a 2DC-RES convolutional neural network.The2DC-RES model extracts the ECG data of each lead on the multi-lead ECG through the combination of two-dimensional convolution and residual nesting,which ensures the independence of the data between the leads and learns the data of each lead.shared features.Then,the key information in the ECG signal is extracted through the one-dimensional residual nested block to improve the classification accuracy.Model training uses stochastic gradient descent as the optimizer and cross-entropy loss function as the optimization objective.Finally,experiments demonstrate that 2DC-RES has good classification performance.(3)Developed arrhythmia classification software.The information collection module is composed of STM32 module and AD8232 ECG module,which is used to collect user’s ECG data.The arrhythmia classification software has a software user information management module,an electrocardiographic signal management module and an electrocardiographic signal classification diagnosis management module.The system is easy to operate and the diagnostic effect is good.