@inproceedings{00c0acbf8b1a44e28f3468575554c3ac,
title = "The influence of physiological characteristics on blood pressure estimation using only PPG signals",
abstract = "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.",
keywords = "blood pressure estimation, photoplethysmography (PPG) signal, physiological characteristics",
author = "Yang Sen and Morgan, {Stephen P.} and Cho, {Siu Yeung} and Zhang Yaping",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 14th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2019 ; Conference date: 01-11-2019 Through 03-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICEMI46757.2019.9101868",
language = "English",
series = "2019 14th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1694--1700",
editor = "Juan Wu and Jiali Yin and Zhang Qi",
booktitle = "2019 14th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2019",
address = "United States",
}