用GPT模型预训练心率波形，可直接用于检测房颤和去噪。
GPT-PPG: A GPT-based Foundation Model for Photoplethysmography Signals
- 将GPT架构适配连续心率信号，用2亿条30秒数据预训练。
- 在房颤检测等任务上表现达或超越当前最佳水平。
- 无需微调即可生成去噪信号，适合医疗信号处理场景。
本研究提出一种面向光电容积脉搏波（PPG）信号的生成式预训练变压器（GPT）基础模型，适用于多种下游任务。通过调整标准GPT架构以适应PPG信号的连续特性，模型在包含超过2亿条30秒样本的大规模数据集上进行预训练。我们探索了不同的监督微调方法，使模型在房颤检测等任务中表现达到或超越当前最优水平。该GPT模型的显著优势在于其固有的生成能力，能有效执行信号去噪任务，且无需额外微调。这一成功归因于GPT框架的生成特性。
原文摘要 · Abstract (English)
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.
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