Research on Arrhythmia Classification by Using Convolutional Neural Network with Mixed Time-Frequency Domain Features

Arrhythmia is one of cardiovascular diseases, and many methods are used to analyze electrocardiogram by computer-aided system to identify arrhythmia. However, most of the data samples of arrhythmia are small, and the computer-aided system is not effective in identifying arrhythmia. In this paper, a mixed time-frequency domain feature extraction method is proposed for arrhythmia classification by using convolution neural network method. The fused features consist of the time domain characteristics from the RR interval, frequency domain characteristics from hilbert-huang transform, and joint time-frequency domain features extracted from continuous wavelet transform. Then the fused features are used as an input to the convolution neural network for training classification model, and the focal loss is used as the loss function of the training model, so as to realize the arrhythmias classification. In addition, the MIT-BIH（Massachusetts Institute of Technology-Boston's Beth Israel Hospital）arrhythmia database is used to verify the performances of the proposed method for arrhythmias classification of four types of ECG (Electrocardiograph) data. Experimental results show that compared with the existing classification algorithms, the proposed method improves the F1 of class obviously. © 2023 Chinese Institute of Electronics. All rights reserved.