RAMP: Reliability Adaptive Masking for Pretraining Noisy Multimodal Physiological Signals

Physiological signals from wearable and clinical monitors are often corrupted by motion artifacts, baseline drift, and contact noise, yet most self-supervised pretraining methods apply fixed masking policies regardless of local reliability. This can align reconstruction with artifact structure rather than physiological semantics. We present RAMP, a reliability-adaptive masking framework for noisy multimodal physiological pretraining. RAMP profiles each ECG and PPG patch using quality, corruption type, and spectral degradation, then uses a compact Reliability Policy Network to jointly select masking threshold, temporal granularity, and cross-modal fusion weights through differentiable relaxations. It reconstructs degraded regions from cleaner temporal and cross-modal context. On MIMIC-III and VitalDB, RAMP reduces pretraining RMSE by 16% over the strongest fixed-policy baseline, with the largest gains under asymmetric modality corruption. The pretrained encoder further achieves 93.9/93.9/93.2% mean ACC/macro-F1/ROC-AUC across six downstream physiological benchmarks spanning ECG, PPG, EEG, and accelerometry, outperforming reproduced baselines under a unified evaluation protocol. These results suggest that reliability-aware masking improves physiological representation learning under controlled synthetic corruption.