A self-supervised framework for computer-aided arrhythmia diagnosis

Cardiovascular diseases, including all types of arrhythmias, are the leading cause of death. Deep learning (DL)based electrocardiography (ECG) diagnosis systems have attracted considerable attention in recent years. However, training DL-based models requires much high-quality labeled data, and labeling ECG records is timeconsuming and expensive. In this paper, a high-precision deep network obtained via low-cost annotation, that is, a self-supervised residual convolutional neural network (SSRCNN), was designed. First, a convolutional neural network and residual blocks were designed to construct a deep feature extractor to automatically obtain the feature expression of ECG signals. Next, we developed time- and lead-dimension-based data augmentation methods and designed a pretraining framework based on unlabeled datasets such that the feature extractor could update the weights in the unlabeled samples. Furthermore, through interactions with clinicians, we used a few labeled data to fine-tune the pretrained model and feature classifier to automatically extract deep features and perform effective classification. Finally, we used different open-source datasets to validate the superiority of the SSRCNN. With the support of unlabeled datasets, SSRCNN can effectively reduce clinicians' workload. Compared with existing methods, the SSRCNN achieves a better diagnostic performance in terms of average accuracy (99.0 %) and average F1-macro (86.83 %) using approximately 25% of labeled data. Therefore, the SSRCNN has a potential for practical applications in clinical settings.