Teaching PPG How not Who: Fixed-Effects Distillation from ECG
This research addresses a flaw in teaching PPG-only models using ECG data: distillation methods primarily learn who a person is rather than how their cardiovascular state changes. The authors introduce fixed-effects distillation, which conditions the loss on each recording's mean to cancel out identity effects. This technique significantly improves state learning, doubling agreement and lowering prediction errors on within-recording labels.