arXiv cs.LG论文
Teaching PPG How not Who: Fixed-Effects Distillation from ECG
该研究指出，利用心电图（ECG）向仅光电容积脉搏波（PPG）模型进行知识蒸馏时，主要学习的是受试者的个体特征（即“谁”），而非心血管状态变化（即“如何”）。研究发现，单次记录的均值这一固定效应占据了冻结ECG教师模型目标的40-59%，导致学生模型在未见数据上泛化能力差。原始对齐余弦相似度无法捕捉此问题，因为常数预测器也能获得高分。
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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.