IM-ECG: An interpretable framework for arrhythmia detection using multi-lead ECG

Abstract

Multi-lead electrocardiogram (ECG) is a fundamental and reliable diagnostic tool for the detection of heart arrhythmias. An increasing number of deep neural network models have been proposed for automatic arrhythmia detection using multi-lead ECGs. However, most of the existing models focus on intra-lead features while neglecting the inter-lead ones and fail to explain the reasons behind decisions due to their black-box nature. To address these challenges, we propose an interpretable multi-lead ECG detection framework that tackles these limitations in two key areas. First, a flexible double-kernel residual block (DKR-block) is proposed to effectively extract inter-lead and intra-lead features. In addition, a new two-dimensional ECG classification model is built based on the DKR-block for accurate arrhythmia detection. Second, a visualization method based on gradient-weighted class activation mapping is used to visualize the indicative features of the model’s decisions. The proposed model achieves an AUCmacro of 0.929 on the PTB-XL data, a F1micro of 0.932 on the HFECGIC dataset, and an F1macro of 0.989 on the LUDB dataset. The results show that the proposed model outperforms several state-of-the-art methods, and the visualization features of its classification decision are consistent with the medical background knowledge.