Multi-Lead ECG Arrhythmia Detection with Hybrid Dynamic Graph Convolutional Network

Cardiovascular disease remains a leading cause of global mortality, underscoring the critical need for accurate and efficient diagnosis of cardiac conditions. Although the electrocardiogram (ECG) is a widely used diagnostic tool, its utility is often limited by signal artifacts and phenotypic similarities among distinct pathologies. To address these limitations, we propose a novel Hybrid Dynamic Graph Convolutional Network (HDGCN) to detect multi-lead ECG arrhythmia. In our HDGCN, ECG signals are represented as graph structures, where nodes correspond to sampling points and edges encode spatiotemporal relationships. Local spatiotemporal features are extracted using a pre-trained ResNet module to capture subtle morphological variations, while an adaptive graph convolutional module with a learnable adjacency matrix dynamically models deeper inter-lead dependencies. Additionally, wavelet-based denoising is applied during preprocessing to preserve clinically relevant features, and depthwise separable convolutions are incorporated to substantially reduce computational complexity. Experimental results demonstrate that HDGCN achieves an average accuracy of 99.57%. The model exhibits robust performance in detecting complex arrhythmias, notably attaining an improvement exceeding 6% for categories such as atrial premature beats.