Integrating learnable expert knowledge into deep learning-based multi-label ECG classification

Abstract Deep learning (DL) has shown great promise in electrocardiogram (ECG) analysis, revolutionizing cardiovascular medicine by enabling precise and efficient diagnosis. This study explores the integration of expert knowledge into DL models, creating a flexible structure that adapts during training to refine this knowledge and guides feature separation in higher-dimensional space, thereby improving multi-label ECG classification. By leveraging domain expertise on the relationships between specific ECG abnormalities and their corresponding changes across lead dimensions, a lead-wise prior knowledge framework (LPKF) was introduced to enhance the learning efficiency of DL models. The effectiveness of this framework was validated by the higher classification performance of LPKF-enhanced models compared to their original versions. In addition, the LPKF-enhanced Inception model outperforms recent state-of-the-art DL methods, underscoring the benefits of integrating learnable expert knowledge. The study also demonstrated the interpretability of the LPKF-enhanced Inception model using gradient-weighted class activation mapping, revealing its capability to identify crucial diagnostic symptoms from ECG signals that align with clinical criteria.