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arXiv · 2503.08015

GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals

Zhaoliang Chen·Cheng Ding·Saurabh Kataria·Runze Yan·Minxiao Wang·Randall Lee·Xiao Hu
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Abstract

This study introduces a novel application of a Generative Pre-trained Transformer (GPT) model tailored for photoplethysmography (PPG) signals, serving as a foundation model for various downstream tasks. Adapting the standard GPT architecture to suit the continuous characteristics of PPG signals, our approach demonstrates promising results. Our models are pre-trained on our extensive dataset that contains more than 200 million 30s PPG samples. We explored different supervised fine-tuning techniques to adapt our model to downstream tasks, resulting in performance comparable to or surpassing current state-of-the-art (SOTA) methods in tasks like atrial fibrillation detection. A standout feature of our GPT model is its inherent capability to perform generative tasks such as signal denoising effectively, without the need for further fine-tuning. This success is attributed to the generative nature of the GPT framework.

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Zhaoliang Chen, Cheng Ding, Saurabh Kataria, Runze Yan, Minxiao Wang, Randall Lee, Xiao Hu. 2025-03-11. GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals. https://arxiv.org/abs/2503.08015

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