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

• URL: http://arxiv.org/abs/2503.08015v1
• Date: Tue, 11 Mar 2025 03:45:31 GMT
• 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,
• Abstract summary: This study introduces a novel application of a Generative Pre-trained Transformer (GPT) model tailored for photoplethysmography ( PPG) signals.<n>Adapting the standard GPT architecture to suit the continuous characteristics of PPG signals, our approach demonstrates promising results.
• Score: 9.441062271747853
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Can We Generate Images with CoT? Let's Verify and Reinforce Image Generation Step by Step [77.86514804787622]
  Chain-of-Thought (CoT) reasoning has been extensively explored in large models to tackle complex understanding tasks.<n>We provide the first comprehensive investigation of the potential of CoT reasoning to enhance autoregressive image generation.<n>We propose the Potential Assessment Reward Model (PARM) and PARM++, specialized for autoregressive image generation.
  arXiv  Detail & Related papers  (2025-01-23T18:59:43Z)
• PaPaGei: Open Foundation Models for Optical Physiological Signals [8.78925327256804]
  Photoplethysmography is the leading non-invasive technique for monitoring biosignals and cardiovascular health.<n>Machine learning models trained on PPG signals tend to be task-specific and struggle with generalization.<n>We present PaPaGei, the first open foundation model for PPG signals.
  arXiv  Detail & Related papers  (2024-10-27T18:18:06Z)
• Steering Masked Discrete Diffusion Models via Discrete Denoising Posterior Prediction [88.65168366064061]
  We introduce Discrete Denoising Posterior Prediction (DDPP), a novel framework that casts the task of steering pre-trained MDMs as a problem of probabilistic inference. Our framework leads to a family of three novel objectives that are all simulation-free, and thus scalable. We substantiate our designs via wet-lab validation, where we observe transient expression of reward-optimized protein sequences.
  arXiv  Detail & Related papers  (2024-10-10T17:18:30Z)
• Continual Learning for Remote Physiological Measurement: Minimize Forgetting and Simplify Inference [4.913049603343811]
  Existing r measurement methods often overlook the incremental learning scenario. Most existing class incremental learning approaches are unsuitable for r measurement. We present a novel method named ADDP to tackle continual learning for r measurement.
  arXiv  Detail & Related papers  (2024-07-19T01:49:09Z)
• Fully Test-Time rPPG Estimation via Synthetic Signal-Guided Feature Learning [8.901227918730562]
  TestTime Adaptation (TTA) enables the model to adaptively estimate r signals in various unseen domains by online adapting to unlabeled target data without referring to any source data. We develop a synthetic signal-guided feature learning method by pseudo r signals as pseudo ground truths to guide a conditional generator in generating latent r features.
  arXiv  Detail & Related papers  (2024-07-18T09:22:40Z)
• GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning [48.71952325015267]
  We apply PEFT methods to a modified Retrieval-Enhanced Transformer (RETRO) and a baseline GPT model across several sizes. We show that RETRO models outperform GPT models in zero-shot settings due to their unique pre-training process. This work presents the first comprehensive comparison of various PEFT methods integrated with RAG, applied to both GPT and RETRO models.
  arXiv  Detail & Related papers  (2024-07-05T14:16:47Z)
• SentimentGPT: Exploiting GPT for Advanced Sentiment Analysis and its Departure from Current Machine Learning [5.177947445379688]
  This study presents a thorough examination of various Generative Pretrained Transformer (GPT) methodologies in sentiment analysis. Three primary strategies are employed: 1) prompt engineering using the advanced GPT-3.5 Turbo, 2) fine-tuning GPT models, and 3) an inventive approach to embedding classification. The research yields detailed comparative insights among these strategies and individual GPT models, revealing their unique strengths and potential limitations.
  arXiv  Detail & Related papers  (2023-07-16T05:33:35Z)
• Efficient GPT Model Pre-training using Tensor Train Matrix Representation [65.96485282393361]
  Large-scale transformer models feature billions of parameters, leading to difficulties in their deployment and prohibitive training costs from scratch. To reduce the number of parameters in the GPT-2 architecture, we replace the matrices of fully-connected layers with the corresponding Train Matrix(TTM) structure. The resulting GPT-based model stores up to 40% fewer parameters, showing the perplexity comparable to the original model.
  arXiv  Detail & Related papers  (2023-06-05T08:38:25Z)
• Universal Domain Adaptation from Foundation Models: A Baseline Study [58.51162198585434]
  We make empirical studies of state-of-the-art UniDA methods using foundation models. We introduce textitCLIP distillation, a parameter-free method specifically designed to distill target knowledge from CLIP models. Although simple, our method outperforms previous approaches in most benchmark tasks.
  arXiv  Detail & Related papers  (2023-05-18T16:28:29Z)
• GOHSP: A Unified Framework of Graph and Optimization-based Heterogeneous Structured Pruning for Vision Transformer [76.2625311630021]
  Vision transformers (ViTs) have shown very impressive empirical performance in various computer vision tasks. To mitigate this challenging problem, structured pruning is a promising solution to compress model size and enable practical efficiency. We propose GOHSP, a unified framework of Graph and Optimization-based Structured Pruning for ViT models.
  arXiv  Detail & Related papers  (2023-01-13T00:40:24Z)
• Kronecker Decomposition for GPT Compression [8.60086973058282]
  GPT is an auto-regressive Transformer-based pre-trained language model which has attracted a lot of attention in the natural language processing (NLP) domain. Despite the superior performance of GPT, GPT can be very prohibitive for deploying this model on devices with limited computational power or memory. In this work, we use Kronecker decomposition to compress the linear mappings of the GPT-22 model.
  arXiv  Detail & Related papers  (2021-10-15T15:28:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.