Learning ECG Image Representations via Dual Physiological-Aware Alignments
About
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.
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| ECG Classification | PTBXL Super | Macro AUC90.9 | 84 | |
| ECG Classification | CPSC 2018 | AUC94.6 | 23 | |
| ECG Classification | PTBXL Sub | Macro AUC0.853 | 18 | |
| ECG Classification | PTBXL Form | Macro AUC84.1 | 18 | |
| ECG Classification | PTBXL Rhythm | Macro AUC93.3 | 18 | |
| ECG Classification | CSN | Macro AUC94.2 | 18 | |
| ECG Classification | PTB-XL, CPSC2018, CSN Cross-domain (test) | AUC (PTBXL-Super -> CPSC2018)84.27 | 14 | |
| ECG Interpretation | CODE (test) | AUC94.78 | 9 |