GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929752739151872 |
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| author | Chen, Zhaoliang Ding, Cheng Kataria, Saurabh Yan, Runze Wang, Minxiao Lee, Randall Hu, Xiao |
| author_facet | Chen, Zhaoliang Ding, Cheng Kataria, Saurabh Yan, Runze Wang, Minxiao Lee, Randall Hu, Xiao |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_08015 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals Chen, Zhaoliang Ding, Cheng Kataria, Saurabh Yan, Runze Wang, Minxiao Lee, Randall Hu, Xiao Machine Learning Signal Processing 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. |
| title | GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals |
| topic | Machine Learning Signal Processing |
| url | https://arxiv.org/abs/2503.08015 |