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.
View on arXiv@article{chen2025_2503.08015,
title={ GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals },
author={ Zhaoliang Chen and Cheng Ding and Saurabh Kataria and Runze Yan and Minxiao Wang and Randall Lee and Xiao Hu },
journal={arXiv preprint arXiv:2503.08015},
year={ 2025 }
}