Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning

Yang, Sen and Zaki, Wan S.W. and Morgan, Stephen P. and Chow, David H.C. and Correia, Ricardo and Wen, Long and Zhang, Yaping (2018) Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning. In: IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018 (BRAIN 2018), 4 November 2018, Ningbo, China.

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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. © 2018 Institution of Engineering and Technology. All rights reserved.

Item Type: Conference or Workshop Item (Lecture)
Additional Information: This paper is under Global University Publication Licence.
Keywords: BLOOD PRESSURE; PHOTOPLETHYSMOGRAM (PPG); ELECTROCARDIOGRAM (ECG); FEATURES
Schools/Departments: University of Nottingham Ningbo China > Faculty of Science and Engineering > Department of Electrical and Electronic Engineering
University of Nottingham Ningbo China > Faculty of Humanities and Social Sciences > School of Economics
University of Nottingham, UK > Faculty of Engineering
Identification Number: https://doi.org/10.1049/cp.2018.1721
Depositing User: QIU, Lulu
Date Deposited: 29 Aug 2019 14:00
Last Modified: 13 Sep 2019 10:21
URI: http://eprints.nottingham.ac.uk/id/eprint/57352

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