Dense lead contrast for self-supervised representation learning of multilead electrocardiograms.

Usually, manual labeling of large-scale electrocardiograms (ECGs) for deep learning is always expensive, as it requires considerable effort and time from cardiologists. Currently, contrastive learning can utilize unlabeled ECGs to improve deep learning using insufficient labels. However, this field may still lack instructive literature. In this paper, a dense lead contrast (DLC) is proposed for effective contrastive learning on multilead ECGs. It develops contrastive learning between any two leads from different views to explore intralead and interlead invariance. A joint loss combining intralead and interlead contrastive loss guides the DLC pretraining. Moreover, DLC introduces a multibranch network (MBN) for contrastive learning, generates a representation for each lead, and fuses all the leads for a global representation. In the downstream tasks, DLC outperforms the standard contrastive learning paradigm of multilead ECGs by 2.78%∼6.59% in AUROC (linear probe). Using only 10% of the labeled training data, it still outperforms standard contrastive learning by a significant margin in AUROC. Compared with existing methods, DLC shows obvious advantages in all the experiments. Therefore, DLC may be more suitable for the contrastive learning of multilead ECGs. Its good performance based on insufficient labels can alleviate the cardiologists' burden from data labeling. • Dense lead contrast (DLC) conducts an effective self-supervised pre-training. • DLC jointly explores intra- and inter-lead invariance of electrocardiograms (ECGs). • Multiple branch network (MBN) architecture organizes all the leads. • Raw ECGs from a real-world hospital are used to validate algorithm robustness. [ABSTRACT FROM AUTHOR]