Research On ECG Arrhythmia Classification Method Based On Deep Learning

Arrhythmia is one of the common diseases in cardiovascular diseases.The diagnosis of arrhythmia often needs ECG examination.However,the visual examination of ECG is not only time-consuming,but also may lead to misdiagnosis and affect the prevention and treatment of diseases.Therefore,automatic analysis technology is needed to assist doctors in the diagnosis of arrhythmia,so as to improve the accuracy and efficiency of diagnosis.At present,the automatic classification methods of arrhythmias mainly include traditional machine learning methods and deep learning methods.The performance of traditional machine learning classifier largely depends on the quality of manual feature extraction,and the generalization ability of model is weak.The classification algorithm based on deep learning can automatically learn features and achieve better classification performance than the traditional machine learning algorithm.Therefore,according to the characteristics of ECG signal,this paper studies the R-peak detection and arrhythmia classification method of ECG signal based on deep learning:(1)Aiming at the problems of high preprocessing time cost and noise sensitivity of existing methods,an automatic classification method of arrhythmia based on original onedimensional ECG signal is proposed.This method first uses the convolutional neural network(CNN)to learn and extract the morphological features of ECG signals,then obtains the time-dependent relationship in the features through bidirectional long short-term memory network(BLSTM),and finally completes the automatic classification of arrhythmias with the help of softmax function.The mish function is used as the activation function to make the model more stable in training.The five-fold cross validation is carried out on the open database MIT-BIH arrhythmia database,and the evaluation index reaches an average accuracy of 99.11%,which shows that the model can effectively extract the important features of the original ECG signal.(2)Aiming at the classification of single beat in most of the existing methods,an ECG R-peak detection and arrhythmia classification method based on modified high-resolution network(HRNet)and effective channel attention mechanism(ECA)is proposed.This method can recognize and classify ECG segments containing multiple ECG beats.Methods first divides the original ECG signal into 5-second ECG segments with a total of 1800 sampling points,and then input these segments into the improved HRNet model for automatic learning and classification.By introducing the effective channel attention mechanism module,the ability of feature extraction and selection of the model is further strengthened.Relevant experiments are carried out on the MIT-BIH arrhythmia database.The average accuracy of the proposed method in arrhythmia classification task is 99.86%,which verifies the effectiveness of the proposed model.