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。

可穿戴生物信号大型语言模型用于无袖血压测量

Large Language Models for Cuffless Blood Pressure Measurement From  Wearable Biosignals

Remote photoplethysmography (rPPG) is a noninvasive technique that aims to
capture subtle variations in facial pixels caused by changes in blood volume
resulting from cardiac activities. Most existing unsupervised methods for rPPG
tasks focus on the contrastive learning between samples while neglecting the
inherent self-similar prior in physiological signals. In this paper, we propose
a Self-Similarity Prior Distillation (SSPD) framework for unsupervised rPPG
estimation, which capitalizes on the intrinsic self-similarity of cardiac
activities. Specifically, we first introduce a physical-prior embedded
augmentation technique to mitigate the effect of various types of noise. Then,
we tailor a self-similarity-aware network to extract more reliable self-similar
physiological features. Finally, we develop a hierarchical self-distillation
paradigm to assist the network in disentangling self-similar physiological
patterns from facial videos. Comprehensive experiments demonstrate that the
unsupervised SSPD framework achieves comparable or even superior performance
compared to the state-of-the-art supervised methods. Meanwhile, SSPD maintains
the lowest inference time and computation cost among end-to-end models. The
source codes are available at this https URL

我们提出了一种基于自相似性先验蒸馏 (SSPD) 框架的无监督远程光电容积描记术 (rPPG) 估计方法，通过利用心脏活动的内在自相似性，采用物理先验嵌入增强技术抑制噪音，利用自相似性感知网络提取更可靠的自相似生理特征，以及开发分层自蒸馏范式来帮助网络从面部视频中解开自相似的生理模式。全面的实验证明，无监督的 SSPD 框架在性能上相当或甚至优于最先进的有监督方法，同时 SSPD 在端到端模型中保持最低的推断时间和计算成本。