Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network
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(P...
| Main Authors: | , |
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| Format: | Journal Article |
| Published: |
2022
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| Online Access: | http://hdl.handle.net/20.500.11937/90312 |
| _version_ | 1848765367741054976 |
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| author | Welhenge, Anuradhi Taparugssanagorn, A. |
| author_facet | Welhenge, Anuradhi Taparugssanagorn, A. |
| author_sort | Welhenge, Anuradhi |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:34:08Z |
| format | Journal Article |
| id | curtin-20.500.11937-90312 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:34:08Z |
| publishDate | 2022 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-903122023-02-28T05:30:10Z Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network Welhenge, Anuradhi Taparugssanagorn, A. 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. 2022 Journal Article http://hdl.handle.net/20.500.11937/90312 10.4028/www.scientific.net/JBBBE.54.31 restricted |
| spellingShingle | Welhenge, Anuradhi Taparugssanagorn, A. Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network |
| title | Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network |
| title_full | Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network |
| title_fullStr | Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network |
| title_full_unstemmed | Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network |
| title_short | Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network |
| title_sort | blood pressure estimation from ppg with motion artifacts using long short term memory network |
| url | http://hdl.handle.net/20.500.11937/90312 |