Learning ECG Image Representations via Dual Physiological-Aware Alignments

• URL: http://arxiv.org/abs/2604.01526v1
• Date: Thu, 02 Apr 2026 01:52:14 GMT
• Title: Learning ECG Image Representations via Dual Physiological-Aware Alignments
• Abstract summary: Electrocardiograms (ECGs) are among the most widely used diagnostic tools for cardiovascular diseases.<n>Most existing automated ECG analysis methods rely on access to raw signal recordings.<n>We present ECG-Scan, a self-supervised framework for learning clinically generalized representations from ECG images.
• Score: 33.03253601947226
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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