Paper ECG recognition is challenging because real-world ECG images vary in layout, physical artifacts, and label availability.
We introduce RobECG-CL, a rank-aware contrastive learning framework for robust paper ECG representation learning.
Starting from standard 12-lead ECG recordings, we construct progressively degraded paper ECG views with heterogeneous layouts and train the model to balance same-recording invariance with degradation-aware ordering.
Across synthetic stress tests on CODE-II and EchoNext, RobECG-CL improves robustness under severe degradation and few-shot transfer, outperforming contrastive learning baselines and surpassing the waveform-based foundation model, ECG-FM, in the 1% labeled setting.
On 312 samples of hospital data with 37 labels, RobECG-CL achieves the best macro AUROC.