English(EN) Personalized and Explainable Blood Pressure Estimation from PPG via Hybrid CNN--Morphological Features

新的混合AI框架改进了个性化血压估计

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-15 04:00

研究人员开发了一种使用光电容积脉搏波描记法（PPG）信号估计血压的新混合框架。该方法结合了卷积神经网络（CNN）和先验形态学分支，以同时捕捉波形动态和个体血管特征。该方法旨在提高个性化水平并减少对大型数据集的依赖，在MIMIC-III数据库上实现了更高的准确性，收缩压的平均绝对误差为3.77 mmHg，舒张压为2.36 mmHg。使用SHAP的可解释性分析证实，形态学特征与个体血管特征一致，增强了可解释性。 AI

影响这种混合AI方法有望带来更准确、更个性化的无袖带血压监测设备。

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arXiv cs.CV TIER_1 English(EN) · Myung-Kyu Yi, Jongshill Lee, Jeyeon Lee, In Young Kim · 2026-09-15 04:00

通过混合CNN--形态学特征从PPG进行个性化和可解释的血压估算

arXiv:2609.13190v1 Announce Type: new Abstract: 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 …

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通过混合CNN--形态学特征从PPG进行个性化和可解释的血压估算

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