Learning What to Trust: Reliability-Conditioned Masked Modeling for Physiological Time Series

Masked time-series models usually choose reconstruction targets without considering whether the observations are trustworthy. This is a poor fit for wearable physiological signals, where motion and contact artifacts create short, source-specific failures inside otherwise usable ECG and PPG recordings. We introduce QRAFT, a self-supervised learner in which fine-grained reliability determines which patches remain visible and which become reconstruction targets. Visible tokens are processed by a shared temporal encoder, allowing reliable evidence from either source to support local recovery in the other. A temporal, spectral, and contrastive objective trains the representation. To distinguish reliability-aware localization from generic hard-example mining, we introduce difficulty-matched random masking. At the same 38.5% mask ratio, QRAFT reduces PPG reconstruction RMSE by 23.2% relative to random masking and by 10.0% relative to the difficulty-matched control. The representation improves blood-pressure transfer across three datasets. On synchronized ECG–PPG data, fusion gains rise from 5.5% when both streams are clean to 18.6% when both are degraded. A factorial analysis further separates gains due to pretraining from those due to the fusion rule. These results show that local reliability can serve as supervision for masked representation learning, not merely as a filter applied before training