Cuffless blood pressure screening based on easily acquired photoplethysmography (PPG) signals offers a practical pathway toward scalable cardiovascular health assessment. Despite rapid progress, existing PPG-based blood pressure estimation models have not consistently achieved the established clinical numerical limits such as AAMI/ISO 81060-2, and prior evaluations often lack the rigorous experimental controls necessary for valid clinical assessment. Moreover, the publicly available datasets commonly used are heterogeneous and lack physiologically controlled conditions for fair benchmarking. To enable fair benchmarking under physiologically controlled conditions, we created a standardized benchmarking subset NBPDB comprising 101,453 high-quality PPG segments from 1,103 healthy adults, derived from MIMIC-III and VitalDB. Using this dataset, we systematically benchmarked several state-of-the-art PPG-based models. The results showed that none of the evaluated models met the AAMI/ISO 81060-2 accuracy requirements (mean error $<$ 5 mmHg and standard deviation $<$ 8 mmHg). To improve model accuracy, we modified these models and added patient demographic data such as age, sex, and body mass index as additional inputs. Our modifications consistently improved performance across all models. In particular, the MInception model reduced error by 23\% after adding the demographic data and yielded mean absolute errors of 4.75 mmHg (SBP) and 2.90 mmHg (DBP), achieves accuracy comparable to the numerical limits defined by AAMI/ISO accuracy standards. Our results show that existing PPG-based BP estimation models lack clinical practicality under standardized conditions, while incorporating demographic information markedly improves their accuracy and physiological validity.

翻译：基于易于获取的光电容积脉搏波（PPG）信号的无袖带血压筛查，为可扩展的心血管健康评估提供了一条实用途径。尽管进展迅速，现有的基于PPG的血压估计模型尚未持续达到既定的临床数值标准（如AAMI/ISO 81060-2），且先前的评估往往缺乏有效临床评估所需的严格实验控制。此外，常用的公开数据集通常存在异质性，缺乏用于公平基准测试的生理学受控条件。为了在生理学受控条件下实现公平的基准测试，我们创建了一个标准化的基准测试子集NBPDB，该子集包含来自1,103名健康成年人的101,453个高质量PPG片段，数据源自MIMIC-III和VitalDB。利用该数据集，我们系统地对几种最先进的基于PPG的模型进行了基准测试。结果表明，所有被评估模型均未满足AAMI/ISO 81060-2的精度要求（平均误差 $<$ 5 mmHg，标准差 $<$ 8 mmHg）。为了提高模型精度，我们修改了这些模型，并加入了患者人口统计学数据（如年龄、性别和体重指数）作为额外输入。我们的修改一致地提升了所有模型的性能。特别是，MInception模型在加入人口统计学数据后误差降低了23%，其平均绝对误差达到4.75 mmHg（收缩压）和2.90 mmHg（舒张压），实现了与AAMI/ISO精度标准定义的数值限值相当的准确性。我们的结果表明，现有基于PPG的血压估计模型在标准化条件下缺乏临床实用性，而纳入人口统计学信息能显著提高其精度和生理学有效性。