Robust Transfer Learning for Paper ECG Recognition

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2609.39581 [cs.LG]

(or arXiv:2609.39581v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.39581 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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