用智能手表无创测血压,无需校准,精度更高。
Cuffless, calibration-free hemodynamic monitoring with physics-informed machine learning models
- 结合生物物理模型与神经网络,实现无校准血压估算。
- 在健康人和高血压患者中均准确监测血压与血流速度。
- 适合长期居家或医院使用,提升心血管管理效率。
可穿戴设备有望通过连续评估心血管健康指标,改变院外和家庭的血流动力学监测方式。然而,现有无袖带可穿戴设备多依赖缺乏理论基础的方法(如脉搏波分析或脉搏抵达时间),易受生理和实验干扰,影响准确性和临床应用。本文开发了一款具备实时电生物阻抗(BioZ)传感功能的智能手表,通过多尺度分析与计算建模框架阐明了BioZ与血压之间的生物物理关系,并识别出影响手腕处脉动生物阻抗信号的生理、解剖及实验参数。采用融合流体动力学原理的信号标记物理信息神经网络,实现了无校准的血压及桡动脉和轴向血流速度估计。我们在静息状态、运动后以及自主神经挑战下对健康人群进行了测试,并在门诊和重症监护病房对高血压及心血管疾病患者进行了验证。结果表明,该方法可行且有效,解决了现有无袖带技术的关键局限。
原文摘要 · Abstract (English)
Wearable technologies have the potential to transform ambulatory and at-home hemodynamic monitoring by providing continuous assessments of cardiovascular health metrics and guiding clinical management. However, existing cuffless wearable devices for blood pressure (BP) monitoring often rely on methods lacking theoretical foundations, such as pulse wave analysis or pulse arrival time, making them vulnerable to physiological and experimental confounders that undermine their accuracy and clinical utility. Here, we developed a smartwatch device with real-time electrical bioimpedance (BioZ) sensing for cuffless hemodynamic monitoring. We elucidate the biophysical relationship between BioZ and BP via a multiscale analytical and computational modeling framework, and identify physiological, anatomical, and experimental parameters that influence the pulsatile BioZ signal at the wrist. A signal-tagged physics-informed neural network incorporating fluid dynamics principles enables calibration-free estimation of BP and radial and axial blood velocity. We successfully tested our approach with healthy individuals at rest and after physical activity including physical and autonomic challenges, and with patients with hypertension and cardiovascular disease in outpatient and intensive care settings. Our findings demonstrate the feasibility of BioZ technology for cuffless BP and blood velocity monitoring, addressing critical limitations of existing cuffless technologies.
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