  The electrocardiogram (ECG) is one of the most commonly-used tools to
diagnose cardiovascular disease in clinical practice. Although deep learning
models have achieved very impressive success in the field of automatic ECG
analysis, they often lack model interpretability that is significantly
important in the healthcare applications. To this end, many schemes such as
general-purpose attention mechanism, Grad-CAM technique and ECG knowledge graph
were proposed to be integrated with deep learning models. However, they either
result in decreased classification performance or do not consist with the one
in cardiologists' mind when interpreting ECG. In this study, we propose a novel
disease-specific attention-based deep learning model (DANet) for arrhythmia
detection from short ECG recordings. The novel idea is to introduce a
soft-coding or hard-coding waveform enhanced module into existing deep neural
networks, which amends original ECG signals with the guidance of the rule for
diagnosis of a given disease type before being fed into the classification
module. For the soft-coding DANet, we also develop a learning framework
combining self-supervised pre-training with two-stage supervised training. To
verify the effectiveness of our proposed DANet, we applied it to the problem of
atrial premature contraction detection and the experimental results shows that
it demonstrates superior performance compared to the benchmark model. Moreover,
it also provides the waveform regions that deserve special attention in the
model's decision-making process, allowing it to be a medical diagnostic
assistant for physicians.
