  Electrocardiogram (ECG) is an essential signal in monitoring human heart
activities. Researchers have achieved promising results in leveraging ECGs in
clinical applications with deep learning models. However, the mainstream deep
learning approaches usually neglect the periodic and formative attribute of the
ECG heartbeat waveform. In this work, we propose a novel ECG-Segment based
Learning (ECG-SL) framework to explicitly model the periodic nature of ECG
signals. More specifically, ECG signals are first split into heartbeat
segments, and then structural features are extracted from each of the segments.
Based on the structural features, a temporal model is designed to learn the
temporal information for various clinical tasks. Further, due to the fact that
massive ECG signals are available but the labeled data are very limited, we
also explore self-supervised learning strategy to pre-train the models,
resulting significant improvement for downstream tasks. The proposed method
outperforms the baseline model and shows competitive performances compared with
task-specific methods in three clinical applications: cardiac condition
diagnosis, sleep apnea detection, and arrhythmia classification. Further, we
find that the ECG-SL tends to focus more on each heartbeat's peak and ST range
than ResNet by visualizing the saliency maps.
