发表机构
University of Georgia; School of Electrical and Computer Engineering, University of Georgia; School of Computing, University of Georgia; Stanford University; Yixing People’s Hospital（佐治亚大学; 佐治亚大学电气与计算机工程学院; 佐治亚大学计算学院; 斯坦福大学; 宜兴市人民医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出基于三轴身体震波的Phy-BP框架，通过自适应质量控制算法筛选有效信号、嵌入三维波传播物理模型对齐多轴特征，在21人162小时数据集上实现可靠血压监测，训练样本有限时表现优异。
AI 中文摘要
心冲击图（BCG）有望在实验室环境中实现无扰式长期血压（BP）监测，但传统BCG信号易受身体-床相互作用变化的影响， fiducial点在时间或振幅轴上发生偏移，且血压会随个体血流动力学变化而改变，导致表示不对齐，影响模型的泛化能力和鲁棒性。本研究提出一种基于三轴身体震波（BSG，BCG的延伸）的无创血压估计框架Phy-BP。首先，设计自适应质量控制算法，通过联合考虑相邻心跳模式和通用心源性模板，筛选富含心源性成分的BSG片段；此外，建立描述身体-床系统中三维波传播的物理模型，并将其嵌入深度学习模型，以表征由单一心源性激励驱动的三轴BSG信号之间的内在耦合关系，从而在模型训练过程中对齐多轴特征，提升对真实场景中失真的鲁棒性。对来自21名受试者的162小时医院数据集开展的实验表明，所提Phy-BP可动态过滤低质量测量值，且深度学习模型训练受不同轴间物理一致性的约束，能提供可靠的血压监测，尤其在训练样本有限时表现突出。
英文摘要
Ballistocardiography (BCG) is promising for unobtrusive long-term blood pressure (BP) monitoring in laboratory settings, but traditional BCG signals are vulnerable to the variations in body-bed interaction with shifted fiducial points in temporal or amplitude axis, and BP varies with personal hemodynamic changes, causing misaligned representations that affect model generalizability and robustness. In this work, we propose Phy-BP, a physics-constrained BP estimation framework based on triaxial bodyseismography (BSG) acquired using bed-mounted sensors as an extension of single-axis BCG. Firstly, we design an adaptive quality-control algorithm combining neighboring beat patterns and universal cardiogenic templates to retain reliable cardiac components. Secondly, we propose a physical model of wave propagation through the body-bed system to constrain latent feature evolution, aligning triaxial representations generated by a shared cardiogenic excitation. These mechanical constraints connect multi-axis feature learning to body-bed dynamics and complement data-driven regression from vibration signals. Evaluation on a 162-hour hospital dataset from 21 subjects against invasive arterial BP yields a mean absolute error of 5.07 mmHg, a prediction-error standard deviation of 7.24 mmHg, and a Pearson correlation coefficient of 0.86 for mean arterial pressure. This framework is intended for unobtrusive monitoring during rest and sleep, with potential applications in overnight hospital and home monitoring.
