A novel unsupervised domain adaptation framework based on graph convolutional network and multi-level feature alignment for inter-subject ECG classification

Electrocardiogram (ECG) is an effective non-invasive tool that can detect arrhythmias. Recently, deep learning (DL) has been widely used in ECG classification algorithms. However, differences between subjects lead to data shifts, hindering the further extension of DL algorithms. To solve this problem, we propose a novel multi-level unsupervised domain adaptation framework (MLUDAF) to diagnose arrhythmias. During feature extraction, we use the atrous spatial pyramid pooling residual (ASPP-R) module to extract spatio-temporal features of the samples. Then the graph convolutional network (GCN) module is used to extract the data structure features. During domain adaptation, we design three alignment mechanisms: domain alignment, semantic alignment, and structure alignment. The three alignment strategies are integrated into a unified deep network to guide the feature extractor to extract domain sharing and distinguishable semantic representations, which can reduce the differences between the source and target domains. Experimental results based on the MIT-BIH database show that the proposed method achieves an overall accuracy of 96.8% for arrhythmia detection. Compared to other methods, the proposed method achieves competitive performance. Cross-domain experiments between databases also demonstrate its strong generalizability. Therefore, the proposed method is promising for application in medical diagnosis systems.