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The influence of physiological characteristics on blood pressure estimation using only PPG signals

机译：生理特性对仅使用PPG信号进行血压估算的影响

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摘要

This paper proposed a novel non-invasive, cuff-less and continuous blood pressure monitoring method to investigate the influence of physiological characteristics. The proposed method, based solely on a photoplethysmography (PPG) signal and machine learning models, has been implemented to investigate a database of 191 subjects. Each subject has PPG signals and 5 physiological characteristics recorded. Therefore, there were 32 types of combinations of physiological characteristics that could serve as inputs to the machine learning models, along with features extracted from PPG signals. The mean absolute error and standard deviation were calculated to test the performance of the machine learning models. Simulation results indicated that the more the physiological characteristics were included, the more accurate the blood pressure estimation of the models.
机译：本文提出了一种新颖的无创，无袖带和连续血压监测方法，以研究生理特性的影响。所提出的方法仅基于光电容积描记（PPG）信号和机器学习模型，已被实施以调查191名受试者的数据库。每个受试者都有记录的PPG信号和5个生理特征。因此，共有32种生理特征组合，可以用作机器学习模型的输入，以及从PPG信号中提取的特征。计算平均绝对误差和标准偏差以测试机器学习模型的性能。仿真结果表明，包含的生理特征越多，模型的血压估计值越准确。

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