Masked self-supervised ECG representation learning via multiview information bottleneck

In recent years, self-supervised learning-based models have been widely used for electrocardiogram (ECG) representation learning. However, most of the models utilize contrastive learning that strongly depend on data augmentation. In this paper, we propose a masked self-supervised learning model based on multiview information bottleneck principle. Our method masks the ECG signal instances in the time and frequency domains at a high ratio and then uses the autoencoder to reconstruct the original input. Not only the intra-view relations within each view but also the inter-view relations between two views are exploited in ECG representation learning. Furthermore, we use the multiview information bottleneck principle to remove redundant information in the time and frequency domains, so that the representations of both views contain more task-relevant information. Our model is pre-trained on three larger ECG datasets at once and fine-tuned on each classification task. Experimental results show that our model not only outperforms state-of-the-art models with self-supervised learning, but also outperforms models with supervised learning.