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dc.contributor.authorWelhenge, Anuradhi
dc.contributor.authorTaparugssanagorn, A.
dc.identifier.citationWelhenge, A. and Taparugssanagorn, A. 2022. Blood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network. Journal of Biomimetics, Biomaterials and Biomedical Engineering. 54: pp. 31-39.

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.

dc.titleBlood Pressure Estimation from PPG with Motion Artifacts using Long Short Term Memory Network
dc.typeJournal Article
dcterms.source.titleJournal of Biomimetics, Biomaterials and Biomedical Engineering
curtin.departmentSchool of Elec Eng, Comp and Math Sci (EECMS)
curtin.accessStatusFulltext not available
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidWelhenge, Anuradhi [0000-0001-9219-2246]
curtin.contributor.scopusauthoridWelhenge, Anuradhi [56604130200]

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