Research On ECG Recognition Method Based On Deep Learning

Electrocardiogram(ECG)is essential for various heart disease diagnosis.The traditional method is to analyze the ECG signal manually and make a diagnosis based on clinical experience.However,artificial diagnosis is usually affected by subjectivity in the face of vast and complex ECG data,which leads to misdiagnosis,and lack of real-time then delays the best opportunity for treatment.The intelligent recognition of ECG can minimize the workload of medical personnel and promote the accuracy and efficiency of diagnosis.Therefore,the study for automatic ECG recognition has essential theoretical and applied significance.This thesis integrates the current popular deep learning models and researches the automatic recognition of ECG.The main work and contributions are depicted below:(1)This thesis proposed a method of ECG recognition based on the Convolutional Neural Network(CNN).The sparse connectivity,weight sharing,and pooling of CNN can simplify the network model and make it more stable.However,to address the issue that a single convolutional layer cannot capture the deep features in the image,a method of ECG recognition based on CNN is suggested in this thesis.By tuning the parameters of the multilayer CNN,extract more in-depth ECG features;the Mean Absolute Error is selected as the loss function to optimize the gradient of the model and improve its effectiveness.(2)This thesis proposed a method of ECG recognition based on CNN and Long Short-Term Memory(LSTM).Considering the time sequence of ECG data,this thesis adds the LSTM layer based on the CNN model and proposes an ECG recognition method based on CNN-LSTM.Use LSTM to capture the position-correlation between signals,and simultaneously,select multilayer CNN to capture the deep features.This method can combine the advantages of CNN and LSTM remarkably,and improve the accuracy of the five classification tasks of ECG.(3)This thesis proposed a method of ECG recognition based on ensemble learning.To further study the ECG recognition method,an ensemble deep learning model named ENDNN is put forward in this thesis.Through the ensemble learning method,combined and compared method with the two models above for experimental analysis.Finally,it is proved that the ensemble can give full play to each classifier's advantages to achieve better recognition effect.