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arXiv · 2509.19215

PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation

Juntong Ni·Saurabh Kataria·Shengpu Tang·Carl Yang·Xiao Hu·Wei Jin
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Abstract

Photoplethysmography (PPG) is widely used in wearable health monitoring, yet large PPG foundation models remain difficult to deploy on resource-limited devices. We present PPG-Distill, a knowledge distillation framework that transfers both global and local knowledge through prediction-, feature-, and patch-level distillation. PPG-Distill incorporates morphology distillation to preserve local waveform patterns and rhythm distillation to capture inter-patch temporal structures. On heart rate estimation and atrial fibrillation detection, PPG-Distill improves student performance by up to 21.8% while achieving 7X faster inference and reducing memory usage by 19X, enabling efficient PPG analysis on wearables.

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Juntong Ni, Saurabh Kataria, Shengpu Tang, Carl Yang, Xiao Hu, Wei Jin. 2025-11-06. PPG-Distill: Efficient Photoplethysmography Signals Analysis via Foundation Model Distillation. https://arxiv.org/abs/2509.19215

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