CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts

• URL: http://arxiv.org/abs/2512.24564v1
• Date: Wed, 31 Dec 2025 02:08:34 GMT
• Title: CPR: Causal Physiological Representation Learning for Robust ECG Analysis under Distribution Shifts
• Authors: Shunbo Jia, Caizhi Liao,
• Abstract summary: Deep learning models for Electrocardiogram (ECG) diagnosis exhibit fragility against adversarial perturbations.<n>We propose Causal Physiological Representation Learning (CPR)<n>CPR incorporates a Physiological Structural Prior within a causal disentanglement framework.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning models for Electrocardiogram (ECG) diagnosis have achieved remarkable accuracy but exhibit fragility against adversarial perturbations, particularly Smooth Adversarial Perturbations (SAP) that mimic biological morphology. Existing defenses face a critical dilemma: Adversarial Training (AT) provides robustness but incurs a prohibitive computational burden, while certified methods like Randomized Smoothing (RS) introduce significant inference latency, rendering them impractical for real-time clinical monitoring. We posit that this vulnerability stems from the models' reliance on non-robust spurious correlations rather than invariant pathological features. To address this, we propose Causal Physiological Representation Learning (CPR). Unlike standard denoising approaches that operate without semantic constraints, CPR incorporates a Physiological Structural Prior within a causal disentanglement framework. By modeling ECG generation via a Structural Causal Model (SCM), CPR enforces a structural intervention that strictly separates invariant pathological morphology (P-QRS-T complex) from non-causal artifacts. Empirical results on PTB-XL demonstrate that CPR significantly outperforms standard clinical preprocessing methods. Specifically, under SAP attacks, CPR achieves an F1 score of 0.632, surpassing Median Smoothing (0.541 F1) by 9.1%. Crucially, CPR matches the certified robustness of Randomized Smoothing while maintaining single-pass inference efficiency, offering a superior trade-off between robustness, efficiency, and clinical interpretability.

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