ECG Signal Ｒecognition Based on Deep Stacked Network - ZHANG Ｒiu; WANG Ｒu; HUANG Jun; ZENG Xin (Journal of Harbin University of Science and Technology)
The traditional electrocardiogram ( ECG) signal recognition algorithms rely on ECG experts to participate in feature recognition，which is time-consuming and laborious with high diagnostic cost. Complex and diverse ECG signal patterns result in low recognition accuracy and poor adaptability. To solve the above problems， the stack Sparse Autoencoder was combined with the Softmax classifier to form a Deep stack Network to realize automatic recognition of ECG signals. The feature extraction of ECG signals was completed by stacking three sparse autoencoders，and the high-dimensional features of ECG signals were depicted layer by layer，and the ECG signals were identified by Softmax classifier. Detailed assessment of the model characteristic of Deep stacked Network， determine the super parameter of the network model，sample training set and test set samples from MIT /BIH database. The experimental results show that the total recognition rate of the proposed method is 99. 69%，which verifies the effectiveness of the proposed method.