感知变化点的基于光体积描记（PPG）的血压估计模型评估与重新校准
Change Point--Aware Evaluation and Re-Calibration of PPG-Based Blood Pressure Estimation
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- VUNO Inc.（VUNO公司）
- University of California, Irvine（加州大学欧文分校）
- Research Institute for Future Medicine, Samsung Medical Center（三星未来医学研究所）
- Inha University Hospital（仁荷大学医院）
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中文总结 AI 辅助
本研究提出基于时间序列变化点检测的波动感知评估框架，针对PPG血压估计模型在血压变化点处性能下降问题，引入触发式针对性重新校准框架，为连续血压监测提供新评估与校准方案。
中文摘要 AI 辅助
采用光体积描记（PPG）的无创连续血压（BP）监测是袖带式测量的有前景替代方案。然而，现有基于PPG的血压估计研究大多依赖于在整个评估区间计算的聚合性能指标（如平均绝对误差），这会掩盖血压快速波动期间的模型失效情况，限制了临床相关性。本研究中，我们提出了一种基于时间序列变化点检测的、针对PPG血压估计的波动感知评估框架。与启发式血压阈值划分（如ΔBP>10mmHg）不同，我们通过捕捉血压轨迹中的突然分布变化来识别血压变化点，并专门在这些波动期间评估估计性能。我们的分析表明，若干最先进模型在血压变化点周围表现出显著的性能下降，且周期性测试时校准不足以应对此类动态血压变化。为解决这一局限，我们引入了一种由检测到的血压变化点触发的针对性重新校准框架，在不修改模型架构的情况下提升了鲁棒性。据我们所知，这是首次从血压变化点视角对PPG血压估计进行系统评估，凸显了面向真实世界连续血压监测的波动感知评估与校准的重要性。
英文摘要
Non-invasive continuous blood pressure (BP) monitoring using photoplethysmography (PPG) is a promising alternative to cuff-based measurements. However, existing PPG-based BP estimation studies predominantly rely on aggregated performance metrics (e.g., mean absolute error) computed over entire evaluation intervals, which can obscure model failures during rapid BP fluctuations and limit clinical relevance. In this work, we propose a fluctuation-aware evaluation framework for PPG-based BP estimation based on time-series change point detection. Instead of heuristic BP thresholding (e.g., $Δ\mathrm{BP} > 10\mathrm{mmHg}$), we identify BP change points by capturing abrupt distributional shifts in BP trajectories and evaluate estimation performance specifically during these fluctuation periods. Our analysis shows that several state-of-the-art models exhibit substantial performance degradation around BP change points, and that periodic test-time calibration is insufficient to handle such dynamic BP variations. To address this limitation, we introduce a targeted re-calibration framework triggered by detected BP change points, improving robustness without modifying model architectures. To the best of our knowledge, this is the first systematic evaluation of PPG-based BP estimation from a BP change point perspective, highlighting the importance of fluctuation-aware evaluation and calibration for real-world continuous BP monitoring.
