Classification of Arrhythmia Based on Convolutional Neural Networks
Arrhythmia is a significant cause of serious conditions such as myocardial infarction and sudden cardiac death,and is of-ten diagnosed early using electrocardiogram.However,the traditional ECG signal classification method has complicated task of fea-ture extraction,which is time-consuming and laborious.To address this,we design an improved Convolutional Neural Network(CNN)model by incorporating Dropout layers into the classic CNN framework.This enhancement aims to improve the model's gen-eralization ability and increase accuracy.By using CNN to automatically extract features,the preprocessed ECG signal is directly used as the input of the model to automatically identify 5 different types of heart beats.Experiments conducted on the MIT-BIH Ar-rhythmia Database achieve the accuracy of 99.68%,the specificity of 98.94%,and the sensitivity of 99.76%.The experimental re-sults show that compared with the classic CNN,the proposed method can accurately and efficiently identify different types of ar-rhythmia diseases.
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