ST-ReGE: A Novel Spatial-Temporal Residual Graph Convolutional Network for CVD

Recently, deep learning (DL) has enabled rapid advancements in electrocardiogram (ECG)-based automatic cardiovascular disease (CVD) diagnosis. Multi-lead ECG signals have lead systems based on the potential differences between electrodes placed on the limbs and the chest. When applying DL models, ECG signals are usually treated as synchronized signals arranged in Euclidean space, which is the abstraction and generalization of real space. However, conventional DL models typically merely focus on temporal features when analyzing Euclidean data. These approaches ignore the spatial relationships of different leads, which are physiologically significant and useful for CVD diagnosis because different leads represent activities of specific heart regions. These relationships derived from spatial distributions of electrodes can be conveniently created in non-Euclidean data, making multi-lead ECGs better conform to their nature. Considering graph convolutional network (GCN) adept at analyzing non-Euclidean data, a novel spatial-temporal residual GCN for CVD diagnosis is proposed in this work. ECG signals are firstly divided into single-channel patches and transferred into nodes, which will be connected by spatial-temporal connections. The proposed model employs residual GCN blocks and feed-forward networks to alleviate over-smoothing and over-fitting. Moreover, residual connections and patch dividing enable the capture of global and detailed spatial-temporal features. Experimental results reveal that the proposed model achieves at least a 5.85% and 6.80% increase in
F₁
over other state-of-the-art algorithms with similar parameters and computations in both PTB-XL and Chapman databases. It indicates that the proposed model provides a promising avenue for intelligent diagnosis with limited computing resources.