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

TL;DR

This paper introduces CPR, a causal representation learning method for ECG analysis that enhances robustness against adversarial perturbations by disentangling invariant pathological features from artifacts, achieving high accuracy and efficiency.

Contribution

CPR incorporates a physiological structural prior within a causal framework to improve ECG robustness, offering a practical, interpretable, and computationally efficient defense against adversarial attacks.

Findings

CPR outperforms standard preprocessing methods under attack.

CPR achieves robustness comparable to randomized smoothing.

CPR maintains single-pass inference efficiency.

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.