  Remote patient monitoring based on wearable single-lead electrocardiogram
(ECG) devices has significant potential for enabling the early detection of
heart disease, especially in combination with artificial intelligence (AI)
approaches for automated heart disease detection. There have been prior studies
applying AI approaches based on deep learning for heart disease detection.
However, these models are yet to be widely accepted as a reliable aid for
clinical diagnostics, in part due to the current black-box perception
surrounding many AI algorithms. In particular, there is a need to identify the
key features of the ECG signal that contribute toward making an accurate
diagnosis, thereby enhancing the interpretability of the model. In the present
study, we develop a vision transformer approach to identify atrial fibrillation
based on single-lead ECG data. A residual network (ResNet) approach is also
developed for comparison with the vision transformer approach. These models are
applied to the Chapman-Shaoxing dataset to classify atrial fibrillation, as
well as another common arrhythmia, sinus bradycardia, and normal sinus rhythm
heartbeats. The models enable the identification of the key regions of the
heartbeat that determine the resulting classification, and highlight the
importance of P-waves and T-waves, as well as heartbeat duration and signal
amplitude, in distinguishing normal sinus rhythm from atrial fibrillation and
sinus bradycardia.
