Learning ECG Image Representations via Dual Physiological-Aware Alignments
This addresses the problem of limited applicability of automated ECG analysis in real-world and resource-constrained settings where only ECG images exist, though it appears incremental as it builds on existing self-supervised and multimodal contrastive methods.
The paper tackles the problem of automated ECG analysis when only image data is available by developing ECG-Scan, a self-supervised framework that learns representations from ECG images using dual physiological-aware alignments. The result is that this image-based model outperforms existing image baselines and significantly reduces the performance gap between ECG image and signal analysis.
Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases, and a large amount of ECG data worldwide appears only in image form. However, most existing automated ECG analysis methods rely on access to raw signal recordings, limiting their applicability in real-world and resource-constrained settings. In this paper, we present ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images through dual physiological-aware alignments: 1) Our approach optimizes image representation learning using multimodal contrastive alignment between image and gold-standard signal-text modalities. 2) We further integrate domain knowledge via soft-lead constraints, regularizing the reconstruction process and improving signal lead inter-consistency. Extensive benchmarking across multiple datasets and downstream tasks demonstrates that our image-based model achieves superior performance compared to existing image baselines and notably narrows the gap between ECG image and signal analysis. These results highlight the potential of self-supervised image modeling to unlock large-scale legacy ECG data and broaden access to automated cardiovascular diagnostics.