Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning

Yang, Sen; Zaki, W.S.W.; Morgan, S.P.; Cho, Siu-Yeung; Correia, R.; Wen, Long; Zhang, Yaping

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.

Citation

Yang, S., Zaki, W., Morgan, S., Cho, S.-Y., Correia, R., Wen, L., & Zhang, Y. (2018, November). Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning. Presented at IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018 (BRAIN 2018), Ningbo, China

• Presentation Conference Type: Edited Proceedings
• Conference Name: IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018 (BRAIN 2018)
• Start Date: Nov 4, 2018
• Acceptance Date: Oct 1, 2018
• Online Publication Date: Nov 4, 2018
• Publication Date: 2018
• Deposit Date: Aug 29, 2019
• Publicly Available Date: Aug 29, 2019
• Publisher: Institution of Engineering and Technology (IET)
• Book Title: IET Doctoral Forum on Biomedical Engineering, Healthcare, Robotics and Artificial Intelligence 2018 (BRAIN 2018)
• ISBN: 9781839530838
• DOI: https://doi.org/10.1049/cp.2018.1721
• Keywords: diseases; medical signal processing; photoplethysmography; patient monitoring; learning (artificial intelligence); blood pressure measurement; blood; electrocardiography
• Public URL: https://nottingham-repository.worktribe.com/output/2517934
• Publisher URL: https://digital-library.theiet.org/content/conferences/10.1049/cp.2018.1721
• Contract Date: Aug 29, 2019