Why the study?
Deep learning ECG diagnosis algorithms rely on large annotated datasets, but privacy issues and annotation costs lead to data shortages that limit algorithm performance.
CLECG provides a novel contrastive learning framework that leverages unlabeled ECG data to improve arrhythmia classification performance.
May support label-efficient arrhythmia classification; leaves open prospective clinical validation.
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.
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Chen et al. (2021) studied this question.
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