  With the development of deep learning-based methods, automated classification
of electrocardiograms (ECGs) has recently gained much attention. Although the
effectiveness of deep neural networks has been encouraging, the lack of
information given by the outputs restricts clinicians' reexamination. If the
uncertainty estimation comes along with the classification results,
cardiologists can pay more attention to "uncertain" cases. Our study aims to
classify ECGs with rejection based on data uncertainty and model uncertainty.
We perform experiments on a real-world 12-lead ECG dataset. First, we estimate
uncertainties using the Monte Carlo dropout for each classification prediction,
based on our Bayesian neural network. Then, we accept predictions with
uncertainty under a given threshold and provide "uncertain" cases for
clinicians. Furthermore, we perform a simulation experiment using varying
thresholds. Finally, with the help of a clinician, we conduct case studies to
explain the results of large uncertainties and incorrect predictions with small
uncertainties. The results show that correct predictions are more likely to
have smaller uncertainties, and the performance on accepted predictions
improves as the accepting ratio decreases (i.e. more rejections). Case studies
also help explain why rejection can improve the performance. Our study helps
neural networks produce more accurate results and provide information on
uncertainties to better assist clinicians in the diagnosis process. It can also
enable deep-learning-based ECG interpretation in clinical implementation.
