Learning ECG Image Representations via Dual Physiological-Aware Alignments

TL;DR

ECG-Scan is a self-supervised framework that learns generalized ECG image representations by aligning images with signals and incorporating domain knowledge, improving diagnostic performance without raw signal access.

Contribution

The paper introduces ECG-Scan, a novel self-supervised method that leverages multimodal contrastive alignment and domain knowledge to enhance ECG image analysis.

Findings

Outperforms existing image-based ECG analysis methods.

Narrows the performance gap between image and signal-based ECG analysis.

Demonstrates effectiveness across multiple datasets and tasks.

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.