Multiscale Residual Network with Dynamic Depthwise Convolution for Multi-label 12-Lead ECG Classification

Accurate and efficient classification of electrocardiogram (ECG) signals is an important task in clinical diagnosis and patient care. Existing approaches of ECG signal classification suffer from problems such as feature redundancy, overfitting, increased computational complexity, and decreased real-time performance. The objective of this study is to propose an effective and efficient solution for ECG signal classification. We introduce a novel end-to-end multi-label classification model named MSR-D2CNN that combines a multi-scale dynamic depthwise convolution module (MSD2C-Blocks) with residual skip connections. MSD2C-Blocks integrate the channel attention mechanism Squeeze-and-Excitation(SE) with the ECG dynamic convolution (EDC) module. The model can capture features of different sizes and shapes of ECG signals while maintaining a lightweight structure. We evaluate the proposed model on the PTB-XL dataset, which consists of 12-lead ECG signals and covers 6 different label settings. Our proposed model achieves excellent classification performance while maintaining a low parameter count and computational complexity. Specifically, the parameter count is 0.827M, FLOPs is 134.563M, and Macro-AUC scores are 0.9350, 0.9360, 0.9339, 0.9341, 0.8765, and 0.9668 across the six different label settings. Our proposed solution provides an effective and efficient approach to ECG signal classification, overcoming the limitations of existing methods. The MSR-D2CNN model can accurately classify ECG signals while maintaining a low parameter count and computational complexity, making it practical for real-world scenarios.