An Interpretable Graph Neural Network for Multi-lead ECG in Coronary Artery Disease Detection

Coronary artery disease (CAD) is a leading cause of death worldwide, and electrocardiography (ECG) serves as a crucial diagnostic tool. However, current deep learning methods for automated CAD diagnosis often focus on intra-lead features, overlooking inter-lead relationships. Additionally, their black-box nature limits clinical applicability. To address these issues, we propose a graph neural network (GNN)-based interpretable model for CAD diagnosis. We propose a feature extractor that integrates attention mechanisms with one-dimensional convolutional neural networks (1D CNNs) to effectively capture temporal features within leads over multiple time steps. To model inter-lead dependencies, we construct graph structures based on lead similarity and leverage a graph convolutional network (GCN) to aggregate spatial information across the leads. Furthermore, an interpretability analysis utilizing attention weights highlights critical ECG segments that influence model decisions, offering insights consistent with established medical knowledge. Experimental results on two public datasets demonstrate state-of-the-art performance, achieving an accuracy of 0.995 and an AUC of 1.0 on the INCART dataset, and an accuracy of 0.833 and an AUC of 0.901 on the STC dataset, significantly outperforming existing approaches on both benchmarks. This method provides an effective and interpretable diagnostic tool to support cardiologists in CAD detection and prevention.