GPT-PPG: A GPT-based foundation model for photoplethysmography signals.

IF 2.3 4区 医学 Q3 BIOPHYSICS
Zhaoliang Chen, Cheng Ding, Saurabh Kataria, Runze Yan, Minxiao Wang, Randall J Lee, Xiao Hu
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引用次数: 0

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.

GPT-PPG:基于gpt的光容积脉搏波信号基础模型。
本研究介绍了一种为光容积脉搏波(PPG)信号量身定制的生成式预训练变压器(GPT)模型的新应用,作为各种下游任务的基础模型。通过调整标准GPT架构以适应PPG信号的连续特性,我们的方法显示了有希望的结果。我们的模型是在包含超过2亿个30ppg样本的广泛数据集上进行预训练的。我们探索了不同的监督微调技术,以使我们的模型适应下游任务,从而在房颤检测等任务中获得与当前最先进(SOTA)方法相当或超过的性能。我们的GPT模型的一个突出特征是其固有的能力,可以有效地执行生成任务,如信号去噪,而不需要进一步的微调。这一成功归功于GPT框架的生成性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Physiological measurement
Physiological measurement 生物-工程:生物医学
CiteScore
5.50
自引率
9.40%
发文量
124
审稿时长
3 months
期刊介绍: Physiological Measurement publishes papers about the quantitative assessment and visualization of physiological function in clinical research and practice, with an emphasis on the development of new methods of measurement and their validation. Papers are published on topics including: applied physiology in illness and health electrical bioimpedance, optical and acoustic measurement techniques advanced methods of time series and other data analysis biomedical and clinical engineering in-patient and ambulatory monitoring point-of-care technologies novel clinical measurements of cardiovascular, neurological, and musculoskeletal systems. measurements in molecular, cellular and organ physiology and electrophysiology physiological modeling and simulation novel biomedical sensors, instruments, devices and systems measurement standards and guidelines.
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