Fusion of multi-scale feature extraction and adaptive multi-channel graph neural network for 12-lead ECG classification.
Journal:
Computer methods and programs in biomedicine
PMID:
40184850
Abstract
BACKGROUND AND OBJECTIVE: The 12-lead electrocardiography (ECG) is a widely used diagnostic method in clinical practice for cardiovascular diseases. The potential correlation between interlead signals is an important reference for clinical diagnosis but is often overlooked by most deep learning methods. Although graph neural networks can capture the associations between leads through edge topology, the complex correlations inherent in 12-lead ECG may involve edge topology, node features, or their combination.