{"title":"GPT-PPG: a GPT-based foundation model for photoplethysmography signals.","authors":"Zhaoliang Chen, Cheng Ding, Saurabh Kataria, Runze Yan, Minxiao Wang, Randall Lee, Xiao Hu","doi":"10.1088/1361-6579/add988","DOIUrl":null,"url":null,"abstract":"<p><p><i>Objective</i>. This study aims to introduce a novel generative pre-trained transformer (GPT)-based foundation model specifically tailored to photoplethysmography (PPG) signals, enabling effective adaptation to various downstream biomedical tasks.<i>Approach</i>. We adapted the standard GPT architecture to handle the continuous characteristics of PPG signals, leveraging extensive pre-training on a large dataset comprising over 200 million 30 s PPG samples, followed by supervised fine-tuning strategies for task-specific optimization.<i>Main results</i>. Our approach achieves performance comparable to or exceeding current state-of-the-art methods on various downstream tasks, notably atrial fibrillation detection, and demonstrates a unique generative capability, such as effective signal denoising, inherently available without additional fine-tuning.<i>Significance</i>. The successful adaptation of GPT to PPG signals underscores the potential of generative transformer frameworks as versatile foundation models in biomedical signal processing, highlighting their dual role in predictive and generative tasks.</p>","PeriodicalId":20047,"journal":{"name":"Physiological measurement","volume":" ","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physiological measurement","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1088/1361-6579/add988","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOPHYSICS","Score":null,"Total":0}
引用次数: 0
Abstract
Objective. This study aims to introduce a novel generative pre-trained transformer (GPT)-based foundation model specifically tailored to photoplethysmography (PPG) signals, enabling effective adaptation to various downstream biomedical tasks.Approach. We adapted the standard GPT architecture to handle the continuous characteristics of PPG signals, leveraging extensive pre-training on a large dataset comprising over 200 million 30 s PPG samples, followed by supervised fine-tuning strategies for task-specific optimization.Main results. Our approach achieves performance comparable to or exceeding current state-of-the-art methods on various downstream tasks, notably atrial fibrillation detection, and demonstrates a unique generative capability, such as effective signal denoising, inherently available without additional fine-tuning.Significance. The successful adaptation of GPT to PPG signals underscores the potential of generative transformer frameworks as versatile foundation models in biomedical signal processing, highlighting their dual role in predictive and generative tasks.
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.