@inproceedings{3050188e71a5473f836d471501a7f60c,
title = "Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning",
abstract = "Blood pressure measurement is a significant part of preventive healthcare and has been widely used in clinical risk and disease management. However, conventional measurement does not provide continuous monitoring and sometimes is inconvenient with a cuff. In addition to the traditional cuff-based blood pressure measurement methods, some researchers have developed various cuff-less and noninvasive blood pressure monitoring methods based on Pulse Transit Time (PTT). Some emerging methods have employed features of either photoplethysmogram (PPG) or electrocardiogram (ECG) signals, although no studies to our knowledge have employed the combined features from both PPG and ECG signals. Therefore this study aims to investigate the performance of a predictive, machine learning blood pressure monitoring system using both PPG and ECG signals. It validates that the employment of the combination of PPG and ECG signals has improved the accuracy of the blood pressure estimation, compared with previously reported results based on PPG signal only.",
keywords = "BLOOD PRESSURE, ELECTROCARDIOGRAM (ECG), FEATURES, PHOTOPLETHYSMOGRAM (PPG)",
author = "Sen Yang and Zaki, {Wan S.W.} and Morgan, {Stephen P.} and Cho, {Siu Yeung} and Ricardo Correia and Long Wen and Yaping Zhang",
note = "Publisher Copyright: {\textcopyright} 2018 Institution of Engineering and Technology. All rights reserved.; IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018, BRAIN 2018 ; Conference date: 04-11-2018",
year = "2018",
doi = "10.1049/cp.2018.1721",
language = "English",
isbn = "9781785617911",
series = "IET Conference Publications",
publisher = "Institution of Engineering and Technology",
number = "CP754",
booktitle = "IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018, BRAIN 2018",
address = "United Kingdom",
edition = "CP754",
}