# Teaching PPG How not Who: Fixed-Effects Distillation from ECG

arXiv: https://arxiv.org/abs/2610.10662 · HTML: https://mlsanity.com/paper/2610.10662/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 7 Oct 2026.

## Builds on (most cited first, 3 of 3)

- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [Conditional Contrastive Learning for Improving Fairness in Self-Supervised Learning](https://mlsanity.com/paper/2106.02866/) (2021)
- [P2E-VQ: ECG-linked representation augmentation for PPG via discrete patch retrieval](https://mlsanity.com/paper/2608.14656/) (2026)

## Abstract (arXiv)

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.