Patient-Aware Contrastive Learning Preserves Per-Patient Structure in RR-Interval Representations

Contrastive representation learning struggles on physiological signals when each subject contributes a distinct baseline pattern.If class differences overlap with subject differences, class-level objectives such as supervised contrastive learning tend to merge per-subject structure into a single per-class cluster, removing the individual variation that a model needs to generalize to unseen patients. We study this problem in the setting of Paroxysmal Atrial Fibrillation (PAF) detection from RR-interval (RRI) sequences and propose a patient-aware contrastive objective that forms positive pairs only from same-patient, same-class segments, preserving each patient's own sinus rhythm (SR) baseline while still pushing the two classes apart. Examining the learned embeddings directly, our objective achieves the most consistent per-patient SR structure (cohesion $0.850$ vs. $0.800$ for supervised contrastive loss (SupCon) and $0.772$ for binary cross-entropy (BCE)). We also identify that BCE produces the cleanest global class separation yet the most disordered per patient structure. This is precisely why a linear probe trained on its features breaks down on unseenpatients. On the IRIDIA-AF dataset, the resulting representation reaches a patient-independent Area Under the Receiver Operating Characteristic Curve (AUROC) of $0.989 \pm 0.003$ with $2.6\times$ lower seed variance than supervised contrastive baselines.These results highlight that per-subject geometric consistency, rather than global class separability, is key to robust cross-patient generalization.