  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.
