LAUSR

Deep learning-based intelligent electrocardiogram (ECG) diagnosis algorithms heavily rely on large annotated datasets. Unfortunately, in the context of ECG diagnosis, privacy issues and the high cost of data annotations lead to a shortage of ECG datasets which severely limits the performance of the state-of-the-art ECG diagnosis algorithms. In this paper, we propose a novel instance-level contrastive learning scheme for ECG signals, namely CLECG, to mine effective information from unlabeled data. During the pre-training, CLECG encourages the representations of different augmented views of the same signal (positive samples) to be similar and increases the distance between representations of augmented views from the different signals (negative samples). The whole pre-training process does not require any form of labeling. Experimental results show that the proposed CLECG strategy outperforms other self-supervised methods and supervised transfer learning strategies.