基于混合CNN与形态学特征的个性化可解释光电容积脉搏波血压估计
Personalized and Explainable Blood Pressure Estimation from PPG via Hybrid CNN--Morphological Features
- Hanyang University（汉阳大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对PPG血压估计中手工特征依赖精确检测和深度学习缺乏可解释性的问题，提出融合CNN与形态学先验的混合框架，在MIMIC-III上MAE达3.77/2.36 mmHg，较CNN基线提升43.7%/32.4%，并增强了个体可解释性。
AI中文摘要：
使用光电容积脉搏波（PPG）进行连续无袖带血压（BP）监测为个性化医疗提供了一种有前景的解决方案。然而，现有方法存在两大局限性。基于手工特征的方法依赖于精确的基准点检测，且仅限于短期分析，而深度学习模型尽管准确，但往往作为黑箱运行，生理可解释性有限。为应对这些挑战，我们提出了一种生理引导的混合框架用于个性化血压估计，该框架将捕捉全局和局部波形动态的卷积神经网络（CNN）分支与显式编码个体血管特征的形态学先验分支相结合。通过嵌入显式编码个体血管特征的基于形态学的特征集，所提出的框架增强了个性化能力，并减少了对大规模训练数据集的依赖。在MIMIC-III数据库的子集上，采用受试者特定（个性化）测试协议进行评估，所提出的个性化生理引导混合方法实现了收缩压平均绝对误差（MAEs）为3.77 mmHg，舒张压为2.36 mmHg，相对于受试者特定（个性化）的纯CNN基线分别相对提高了43.7%和32.4%。基于SHAP的分析证实，引入的形态学先验特征与个体血管特征一致，增强了每个受试者的可解释性。这些发现凸显了个性化、生理引导的混合学习结合新型形态学描述符在现实环境中实现准确且可解释的血压监测的潜力。
英文摘要：
Continuous cuffless blood pressure (BP) monitoring using photoplethysmography (PPG) offers a promising solution for personalized healthcare. However, existing methods have two major limitations. Handcrafted feature-based approaches rely on precise fiducial point detection and are limited to short-term analysis, while deep learning models, despite their accuracy, often operate as black boxes with limited physiological interpretability. To address these challenges, we propose a physiology-guided hybrid framework for personalized BP estimation that couples a convolutional neural network (CNN) branch capturing global and local waveform dynamics with a morphology-prior branch that explicitly encodes person-specific vascular characteristics. By embedding a morphology-based feature set that explicitly encodes individual vascular characteristics, the proposed framework enhances personalization and reduces dependence on large-scale training datasets. Evaluated on a subset of the MIMIC-III database under a subject-specific (personalized) testing protocol, the proposed personalized physiology-guided hybrid approach achieved mean absolute errors (MAEs) of 3.77 mmHg for systolic BP and 2.36 mmHg for diastolic BP, corresponding to relative improvements of 43.7% and 32.4% over a subject-specific (personalized) CNN-only baseline. SHAP-based analysis confirmed that the introduced morphology-prior features align with individual vascular characteristics, reinforcing per-subject interpretability. These findings highlight the potential of personalized, physiology-guided hybrid learning with novel morphological descriptors for accurate and explainable BP monitoring in real-world settings.
