DG-ECG: Multi-stream deep graph learning for the recognition of disease-altered patterns in electrocardiogram

Representation learning of electrocardiogram (ECG) has been an active research field for the automated detection of cardiac disease. In addition to extracting time and frequency domain features of ECG, an increasing amount of studies have adapted deep neural networks for the recognition of disease-altered ECG patterns. However, many deep learning models are deployed as blackboxes without fully exploring disease-pertinent information hidden in the signal. This, as a result, diminishes the efficacy and interpretability of the model and impedes applications in clinical practice. To address this problem, we develop a new multi-stream deep graph learning of ECG (DG-ECG) framework, which integrates multi-stream graph neural networks to uncover disease-altered ECG patterns from multifold perspectives (e.g., the morphology and rhythm of ECG signals). In each stream, visibility graphs are modeled to transform signal patterns into graph topological features, which are then mined by graph convolution. In addition, attention mechanisms are integrated into DG-ECG for multi-level information fusion to enhance its detection power and interpretability. Experimental results have demonstrated that the developed DG-ECG is better capable of gleaning disease-pertinent information from multi-channel ECG signals compared to benchmark models. The developed framework is extendable and suited for the pattern recognition of various cardiac disorders.