RAE-PPG: Duration-Grounded Retain-and-Extend Pretraining for PPG Foundation Models

Signal features derived from photoplethysmography (PPG) require different signal durations to characterize. Existing PPG foundation models treat duration as a pretraining or evaluation condition rather than using the different durations required by PPG features to organize self-supervision. We hypothesize that self-supervision should expand with signal duration, allowing one encoder to progressively acquire additional features while preserving and reusing earlier learning. We introduce Retain-and-Extend PPG (RAE-PPG), which trains a single Transformer encoder through successive 10 s, 30 s, and 240 s input-duration stages, introducing supervision for additional signal features as longer observations become available. The encoder is partitioned into duration-specific parameter groups, allowing later stages to access groups learned earlier while updating only the group assigned to the current stage. Retained-target supervision continues supervision for selected earlier features at longer durations, encouraging them to remain decodable. Direct decoding from the final encoder shows that earlier features remain recoverable from longer-input representations, while later-stage features show higher mean decoding performance at their introduction durations. Controlled comparisons further show that prior-stage learning provides a better basis for learning newly introduced features at both transitions. Across 18 tasks from eight datasets, the final frozen encoder achieves the best observed score on 12 tasks compared with five existing PPG foundation models.