Design And Optimization Of ECG Analysis Model Based On Deep Learning

Arrhythmia is a set of common diseases of cardiovascular diseases,and its related research has been a subject of great concern in the medical community.Electrocardiogram(ECG)is mainly used for the screening and diagnosis of arrhythmia.Through the analysis of ECG data by computer and related algorithms,the physiological and pathological status of patients can be obtained by using its waveform morphological characteristics and rhythm information,which can effectively improve the screening efficiency of cardiovascular diseases,assist medical workers,save medical costs,and make up for the phenomenon of missed diagnosis or misdiagnosis caused by doctors’different experience or knowledge level.At present,the traditional algorithm of ECG data analysis is knowledge reasoning or structural pattern recognition,but these algorithms have limitations in application because the pattern they can express can not describe the diverse and complex ECG waveform.Aiming at the problem of arrhythmia automatic diagnosis based on ECG,this paper studies the ECG data and deep learning related literature.Based on deep learning technology and data balance strategy,a series of data preprocessing scheme,data balance scheme,model constructing scheme and performance evaluation scheme are proposed.An one-dimensional convolutional neural network based on ResNet structure is designed and implemented for ECG automatic diagnosis.The main work and achievements of this paper are as follows:1.This paper studies the generation of ECG data,ECG waveform composition and morphological characteristics of arrhythmia waveform,and completes the construction of ECG dataset through the data extraction of ECG records and professional doctors’ diagnosis results.Aiming at the baseline drift,EMG interference and other noise problems existing in ECG dataset,a reasonable filtering method is used to eliminate the noise,which lays a good foundation for the subsequent model training.2.There is a serious problem of data imbalance in ECG datasets.In most arrhythmia categories,the number of positive and negative samples is very different.In order to solve this problem,this paper studies and designs a data balance strategy,which includes the strategies of data level,algorithm level and evaluation metrics level.Data balance strategy can reduce the quantity difference of categories and improve the learning efficiency of the model.3.This paper analyzes the periodic characteristics and waveform shape of ECG data,designs deep one-dimensional convolutional neural network based on ResNet module of full pre activation structure,and uses the processed dataset and reasonable strategy to train the model.According to a variety of deep learning network models,the experimental group of models is built,and the accuracy,sensitivity,F1 score and other metrics are used to verify the effectiveness of the model structure design and complete the performance evaluation.4.This paper studies the model visualization method,realizes the visualization of the model,and completes the deployment of the model in the mobile terminal.The visualization is based on the feature maps of network layers and GradCam visualization technology.The feature morphology and classification basis extracted from the network are displayed,which provides a certain interpretability for the model in the process of arrhythmia classification.The model is deployed to the mobile terminal to realize offline and portable ECG automatic diagnosis,which can improve the convenience of diagnosis and effectively guarantee the security and privacy of medical data.