Self-Supervised Pulse-Aware Interpretable Disentangled ECG Representation Learning

Abstract

Electrocardiography (ECG) is a widely used cardiac measurement for detecting cardiovascular conditions, while self-supervised learning leverages unlabeled data for model pre-training. However, current self-supervised frameworks for ECG signals generally lack a comprehensive understanding of intra-heartbeat and inter-heartbeat representation, which are crucial in the clinical interpretation of ECG data. In this work, a novel self-supervised pulse-aware interpretable disentangled ECG representation learning framework named SPIDER is proposed. The branch structure in SPIDER disentangles the general-purpose representation to encode specific information. The SPIDER framework notably enhances the performance in terms of area under the precision-recall curve (AUPRC), accompanied by a twofold improvement in training efficiency compared to alternative methods. Moreover, the design of the heartbeat branch provides interpretable heartbeat-level representations. The proposed SPIDER framework not only improves the performances on downstream tasks but also enhances training efficiency and interpretability, and these benefits are particularly valuable in real-world medical applications.