Integrating ECG and PCG Signals through a Dual-Modal ViT for Coronary Artery Disease Detection.

Abstract

Cardiovascular disease (CVD) remains the leading cause of mortality worldwide, with coronary artery disease (CAD) being the most prevalent form. To improve screening efficiency, there is a critical need for accurate, non-invasive, and cost-effective CAD detection methods. This study presents Co-Attention Dual-Modal ViT (CAD-ViT), a novel classification framework based on the Vision Transformer that integrates both electrocardiogram (ECG) and phonocardiogram (PCG) signals. Unlike prior approaches that process ECG and PCG features independently or fuse them through simple concatenation, the proposed model introduces two key modules: a Co-Attention mechanism that enables bidirectional cross-modal interaction to effectively capture complementary features between ECG and PCG signals, and a Dynamic Weighted Fusion (DWF) module that adaptively adjusts the contribution of each modality for robust feature fusion. CAD-ViT is evaluated on a private clinical dataset comprising 132 CAD and 101 non-CAD subjects, achieving an accuracy of 97.08%, precision of 97.18%, specificity of 98.52%, F1-score of 97.04, and recall of 96.94%. Additional validation on two public datasets confirms the model's robustness and generalization capability. These results demonstrate the effectiveness of the proposed approach and its potential for practical deployment in CAD screening using multimodal biosignals.