Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning
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 bl...
| Main Authors: | , , , , , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://eprints.nottingham.ac.uk/57352/ |
| _version_ | 1848799470913847296 |
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| author | Yang, Sen Zaki, Wan S.W. Morgan, Stephen P. Chow, David H.C. Correia, Ricardo Wen, Long Zhang, Yaping |
| author_facet | Yang, Sen Zaki, Wan S.W. Morgan, Stephen P. Chow, David H.C. Correia, Ricardo Wen, Long Zhang, Yaping |
| author_sort | Yang, Sen |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T20:36:11Z |
| format | Conference or Workshop Item |
| id | nottingham-57352 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T20:36:11Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-573522019-09-13T10:21:52Z https://eprints.nottingham.ac.uk/57352/ Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning Yang, Sen Zaki, Wan S.W. Morgan, Stephen P. Chow, David H.C. Correia, Ricardo Wen, Long Zhang, Yaping 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. 2018-11-04 Conference or Workshop Item PeerReviewed application/pdf en https://eprints.nottingham.ac.uk/57352/2/combinepdf.pdf Yang, Sen, Zaki, Wan S.W., Morgan, Stephen P., Chow, David H.C., Correia, Ricardo, 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. BLOOD PRESSURE; PHOTOPLETHYSMOGRAM (PPG); ELECTROCARDIOGRAM (ECG); FEATURES https://digital-library.theiet.org/content/conferences/10.1049/cp.2018.1721 10.1049/cp.2018.1721 10.1049/cp.2018.1721 10.1049/cp.2018.1721 |
| spellingShingle | BLOOD PRESSURE; PHOTOPLETHYSMOGRAM (PPG); ELECTROCARDIOGRAM (ECG); FEATURES Yang, Sen Zaki, Wan S.W. Morgan, Stephen P. Chow, David H.C. Correia, Ricardo Wen, Long Zhang, Yaping Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| title | Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| title_full | Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| title_fullStr | Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| title_full_unstemmed | Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| title_short | Blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| title_sort | blood pressure estimation from photoplethysmogram and electrocardiogram signals using machine learning |
| topic | BLOOD PRESSURE; PHOTOPLETHYSMOGRAM (PPG); ELECTROCARDIOGRAM (ECG); FEATURES |
| url | https://eprints.nottingham.ac.uk/57352/ https://eprints.nottingham.ac.uk/57352/ https://eprints.nottingham.ac.uk/57352/ |