MTDL-NET: Morphological and Temporal Discriminative Learning for Heartbeat Classification

Heartbeat classification based on Electrocardiogram (ECG) signal is crucial to the clinical diagnosis of heart diseases, which has attracted special interest both industrially and scientifically. However, previous methods on ECG mainly lay emphasis on extracting the optimal hand-crafted or deep features, while ignore to explore the potential of morphological and temporal representation to further boost the performance of heartbeat classification task. To address this challenge, in this work, we propose two main modules: (1) Masked attention embedding for extracting discriminative morphological feature; (2) Temporal feature enhanced mechanism for enhancing temporal representation of heartbeat. We combine two modules with transformer encoder architecture and obtain a simple yet effective signal classification model dubbed as MTDL-Net. Comprehensive experiments on benchmark dataset demonstrate that our method can surpass the previous methods by a clear margin quantitatively. Qualitative analysis also validate that MTDL-Net has strong feature extraction capacity and interpretability in the heartbeat classification task.