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Blood Pressure Estimation from Photoplethysmography with Motion Artifacts using Long Short Term Memory Network
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| dc.contributor.author | Welhenge, Anuradhi | |
| dc.contributor.author | Taparugssanagorn, Attaphongse | |
| dc.date.accessioned | 2022-08-12T06:36:59Z | |
| dc.date.available | 2022-08-12T06:36:59Z | |
| dc.date.issued | 2022 | |
| dc.identifier.citation | Welhenge, Anuradhi and Taparugssanagorn, Attaphongse(2022),Blood Pressure Estimation from Photoplethysmography with Motion Artifacts Using Long Short Term Memory Network, Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 54), https://doi.org/10.4028/www.scientific.net/JBBBE.54.31 | en_US |
| dc.identifier.uri | http://repository.kln.ac.lk/handle/123456789/25060 | |
| dc.description.abstract | Continuous measurement of the Blood Pressure (BP) is important in hypertensive patientsand elderly population. Traditional cuff based methods are difficult to use since it is uncomfortable towear a cuff throughout the day. A more suitable method is to estimate the BP using the Photoplethysmography(PPG) signal. However, it is difficult to estimate a BP when the PPG is corrupted withMotion Artifacts (MAs). In this paper, Long Short Term Memory (LSTM) an extension of RecurrentNeural Networks (RNN) is used used to improve the accuracy of the estimation of the BP from thecorrupted PPG. It shows that an accuracy of 97.86 is achieved. | en_US |
| dc.publisher | Journal of Biomimetics, Biomaterials and Biomedical Engineering (Volume 54) | en_US |
| dc.subject | Deep Learning, Blood Pressure, Long Short Term Memory, Recurrent Neural Networks | en_US |
| dc.subject | Blood Pressure, Deep Learning, LSTM, RNN | en_US |
| dc.title | Blood Pressure Estimation from Photoplethysmography with Motion Artifacts using Long Short Term Memory Network | en_US |
