A simple self-supervised ECG representation learning method via manipulated temporal–spatial reverse detection

• Published in: Biomedical signal processing and control Vol. 79; p. 104194
• Main Authors: Zhang, Wenrui, Geng, Shijia, Hong, Shenda
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2023
• Subjects: Deep learning; ECG; Representation learning; Self-supervised learning; Representation learning; Deep learning; ECG; Self-supervised learning
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2022.104194

Learning representations from electrocardiogram (ECG) signals can serve as a fundamental step for different machine learning-based ECG tasks. In order to extract general ECG representations that can be adapted to various downstream tasks, the learning process needs to be based on a general ECG-related task which can be achieved through self-supervised learning (SSL). However, existing SSL approaches either fail to provide satisfactory ECG representations or require too much effort to construct the learning data. In this paper, we propose the T-S reverse detection, a simple yet effective self-supervised approach to learn ECG representations. Inspired by the temporal and spatial characteristics of ECG signals, we flip the original signals horizontally (temporal reverse), vertically (spatial reverse), and both horizontally and vertically (temporal–spatial reverse). Learning is then done by classifying four types of signals including the original one. To verify the effectiveness of the proposed method, we perform a downstream task to detect atrial fibrillation (AF) which is one of the most common ECG tasks. The results show that the ECG representations learned with our method achieve remarkable performance. Furthermore, after exploring the representation feature space and investigating salient ECG locations, we conclude that the temporal reverse is more effective for learning ECG representations than the spatial reverse. •We propose a simple method to learn ECG representations in a self-supervised way.•We learn temporal and spatial representations via manipulated reverse detection.•We employ model interpretation to visualize the relationship between ECGs and their representations.