ECG-based multi-class arrhythmia detection using spatio-temporal attention-based convolutional recurrent neural network

Automatic arrhythmia detection based on electrocardiogram (ECG) is of great significance for early prevention and diagnosis of cardiac diseases. Recently, deep learning methods have been applied to arrhythmia detection and obtained great success. Among them, convolutional neural network (CNN) is an effective method for extracting features due to its local connectivity and parameter sharing. In addition, recurrent neural network (RNN) is another commonly used method, which is applied to process time-series signal. The stacking of both CNN and RNN has been proved to be more effective in mull-class arrhythmia detection. However, these networks ignored the fact that different channels and temporal segments of a feature map extracted from the 12-lead ECG signal contribute differently to cardiac arrhythmia detection, and thus, the classification performance could be greatly improved. To address this issue, spatio-temporal attention-based convolutional recurrent neural network (STA-CRNN) is proposed to focus on representative features along both spatial and temporal axes. STA-CRNN consists of CNN subnetwork, spatio-temporal attention modules and RNN subnetwork. The experiment result shows that, STA-CRNN reaches an average F-1 score of 0.835 in classifying 8 types of arrhythmias and normal rhythm. Compared with the state-of-the-art methods based on the same public dataset, STA-CRNN achieves an obvious improvement on identifying most of arrhythmias. Also, it is demonstrated by visualization that the learned features through STA-CRNN are in line with clinical judgement. STA-CRNN provides a promising method for automatic arrhythmia detection, which has a potential to assist cardiologists in the diagnosis of arrhythmias.