[论文] Physics-Constrained Deep Learning Model for Contactless Blood Pressure...

论文概要

研究领域: ML
作者: Yuanyuan Zhang, Yida Zhang, Jiahui Li
发布时间: 2025-08-26
arXiv: 2508.17628

中文摘要

心冲击描记法（BCG）有望实现无创长期血压监测，但传统BCG信号易受人体-床接触变化的影响，时域或幅值上的基准点漂移以及个体血流动力学变化会导致表征错位，影响模型的泛化能力和鲁棒性。本文提出了基于三轴体震描记法（BSG）的无创血压估计框架Phy-BP。首先设计了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脏模板来选择富含心脏成分的BSG片段。进一步建立了描述人体-床系统中3D波传播的物理模型，并将其嵌入深度学习模型中，以表征由单一心脏激励驱动的三轴BSG信号之间的内在耦合关系。这样，多轴特征在模型训练过程中实现对齐，提高了真实场景中对失真的鲁棒性。在21名受试者162小时医院数据集上的实验表明，Phy-BP能够动态过滤低质量测量，且受物理一致性约束的深度学习模型在训练样本有限时仍能提供可靠的血压监测。

原文摘要

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 a non-invasive BP estimation framework, Phy-BP, based on triaxial bodyseismography (BSG) as an extension of BCG. Firstly, an adaptive quality-control algorithm is designed to select BSG segments enriched with cardiogenic components by jointly considering neighboring beat patterns and universal cardiogenic templates. Furthermore, a physical model is established to describe...

自动采集于 2026-08-26

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