Teaching PPG How not Who: Fixed-Effects Distillation from ECG
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Computer Science > Machine Learning
Title:Teaching PPG How not Who: Fixed-Effects Distillation from ECG
Abstract:ECG is widely used to teach PPG-only models, yet what it teaches is unexamined. Wearables are valued for tracking how a person's cardiovascular state changes, but ECG-to-PPG distillation mostly learns who the person is. A per-recording mean, the trait, holds 40-59% of a frozen ECG teacher's target, and pooled students memorise it without carrying it to new recordings. The raw alignment cosine misses this, since a constant predictor scores 0.793. Across 34 runs, the more identity a student memorises, the less state it learns. Fixed-effects distillation subtracts each recording's mean from prediction and target, so the trait cancels exactly, while a pooled anchor keeps it. State agreement more than doubles, within-person labels improve while age and sex do not, and the gain holds on two backbones and two further databases. Conditioning on the recording turns distillation toward the within-person changes that wearables monitor.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.10662 [cs.LG] |
| (or arXiv:2610.10662v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.10662
arXiv-issued DOI via DataCite (pending registration)
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