Large language models (LLMs) have captured significant interest from both academia and industry due to their impressive performance across various textual tasks. However, the potential of LLMs to analyze physiological time-series data remains an emerging research field. Particularly, there is a notable gap in the utilization of LLMs for analyzing wearable biosignals to achieve cuffless blood pressure (BP) measurement, which is critical for the management of cardiovascular diseases. This paper presents the first work to explore the capacity of LLMs to perform cuffless BP estimation based on wearable biosignals. We extracted physiological features from electrocardiogram (ECG) and photoplethysmogram (PPG) signals and designed context-enhanced prompts by combining these features with BP domain knowledge and user information. Subsequently, we adapted LLMs to BP estimation tasks through fine-tuning. To evaluate the proposed approach, we conducted assessments of ten advanced LLMs using a comprehensive public dataset of wearable biosignals from 1,272 participants. The experimental results demonstrate that the optimally fine-tuned LLM significantly surpasses conventional task-specific baselines, achieving an estimation error of 0.00 $\pm$ 9.25 mmHg for systolic BP and 1.29 $\pm$ 6.37 mmHg for diastolic BP. Notably, the ablation studies highlight the benefits of our context enhancement strategy, leading to an 8.9% reduction in mean absolute error for systolic BP estimation. This paper pioneers the exploration of LLMs for cuffless BP measurement, providing a potential solution to enhance the accuracy of cuffless BP measurement.

翻译：大语言模型（LLMs）因其在各种文本任务中的卓越表现，已引起学术界和工业界的广泛关注。然而，LLMs在分析生理时间序列数据方面的潜力仍是一个新兴的研究领域。特别是在利用LLMs分析可穿戴生物信号以实现无袖带血压测量方面，存在显著的研究空白，而该技术对于心血管疾病的管理至关重要。本文首次探索了LLMs基于可穿戴生物信号进行无袖带血压估计的能力。我们从心电图（ECG）和光电容积脉搏波（PPG）信号中提取生理特征，并通过将这些特征与血压领域知识及用户信息相结合，设计了上下文增强提示。随后，我们通过微调使LLMs适应血压估计任务。为评估所提出的方法，我们使用一个包含1,272名参与者的综合性可穿戴生物信号公共数据集，对十种先进的LLMs进行了评估。实验结果表明，经过最优微调的LLM显著超越了传统的任务专用基线模型，其收缩压估计误差为0.00 $\pm$ 9.25 mmHg，舒张压估计误差为1.29 $\pm$ 6.37 mmHg。值得注意的是，消融研究凸显了我们上下文增强策略的优势，使收缩压估计的平均绝对误差降低了8.9%。本文开创了将LLMs应用于无袖带血压测量的探索，为提高无袖带血压测量的准确性提供了一种潜在的解决方案。