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
instruction 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.

该论文探讨了使用大型语言模型分析可穿戴生物信号实现无袖血压测量的潜力，提出了从心电图和光电波传感图中提取生理特征，并结合血压领域知识和用户信息设计增强上下文的提示信息，通过优化调整大型语言模型，在全面的可穿戴生物信号数据集上取得了令人瞩目的结果。