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Advances in Non-Invasive Blood Pressure Monitoring

dc.contributor.authorQuan, Xina
dc.contributor.authorLiu, Junjun
dc.contributor.authorRoxlo, Thomas
dc.contributor.authorSiddharth, Siddharth
dc.contributor.authorLeong, Weyland
dc.contributor.authorMuir, Arthur
dc.contributor.authorCheong, So-Min
dc.contributor.authorRao, Anoop
dc.date.accessioned2021-12-08T21:40:51Z
dc.date.available2021-12-08T21:40:51Z
dc.date.issued2021-06-22
dc.identifier.citationQuan, X.; Liu, J.; Roxlo, T.; Siddharth, S.; Leong, W.; Muir, A.; Cheong, S.-M.; Rao, A. Advances in Non-Invasive Blood Pressure Monitoring. Sensors 2021, 21, 4273. https://doi.org/10.3390/s21134273en_US
dc.identifier.urihttp://hdl.handle.net/1808/32273
dc.description.abstractThis paper reviews recent advances in non-invasive blood pressure monitoring and highlights the added value of a novel algorithm-based blood pressure sensor which uses machine-learning techniques to extract blood pressure values from the shape of the pulse waveform. We report results from preliminary studies on a range of patient populations and discuss the accuracy and limitations of this capacitive-based technology and its potential application in hospitals and communities.en_US
dc.publisherMDPIen_US
dc.rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en_US
dc.subjectcNIBPen_US
dc.subjectNeonateen_US
dc.subjectNICUen_US
dc.subjectHypertensionen_US
dc.subjectHypotensionen_US
dc.subjectNon-invasive blood pressure monitoringen_US
dc.titleAdvances in Non-Invasive Blood Pressure Monitoringen_US
dc.typeArticleen_US
kusw.kuauthorCheong, So-Min
kusw.kudepartmentGeography & Atmospheric Scienceen_US
dc.identifier.doi10.3390/s21134273en_US
kusw.oaversionScholarly/refereed, publisher versionen_US
kusw.oapolicyThis item meets KU Open Access policy criteria.en_US
dc.identifier.pmidPMC8271585en_US
dc.rights.accessrightsopenAccessen_US


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© 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.