Advances in Non-Invasive Blood Pressure Monitoring
| dc.contributor.author | Quan, Xina | |
| dc.contributor.author | Liu, Junjun | |
| dc.contributor.author | Roxlo, Thomas | |
| dc.contributor.author | Siddharth, Siddharth | |
| dc.contributor.author | Leong, Weyland | |
| dc.contributor.author | Muir, Arthur | |
| dc.contributor.author | Cheong, So-Min | |
| dc.contributor.author | Rao, Anoop | |
| dc.date.accessioned | 2021-12-08T21:40:51Z | |
| dc.date.available | 2021-12-08T21:40:51Z | |
| dc.date.issued | 2021-06-22 | |
| dc.identifier.citation | Quan, 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/s21134273 | en_US |
| dc.identifier.uri | http://hdl.handle.net/1808/32273 | |
| dc.description.abstract | This 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.publisher | MDPI | en_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.uri | http://creativecommons.org/licenses/by/4.0/ | en_US |
| dc.subject | cNIBP | en_US |
| dc.subject | Neonate | en_US |
| dc.subject | NICU | en_US |
| dc.subject | Hypertension | en_US |
| dc.subject | Hypotension | en_US |
| dc.subject | Non-invasive blood pressure monitoring | en_US |
| dc.title | Advances in Non-Invasive Blood Pressure Monitoring | en_US |
| dc.type | Article | en_US |
| kusw.kuauthor | Cheong, So-Min | |
| kusw.kudepartment | Geography & Atmospheric Science | en_US |
| kusw.oanotes | Per Sherpa Romeo 12/08/2021:Sensors [Open panel below]Publication Information TitleSensors [English] ISSNsElectronic: 1424-8220 URLhttp://www.mdpi.com/journal/sensors PublishersMDPI [Commercial Publisher] DOAJ Listinghttps://doaj.org/toc/1424-8220 Requires APCYes [Data provided by DOAJ] [Open panel below]Publisher Policy Open Access pathways permitted by this journal's policy are listed below by article version. Click on a pathway for a more detailed view.Published Version NoneCC BYPMC Any Repository, Journal Website, +1 OA PublishingThis pathway includes Open Access publishing EmbargoNo Embargo LicenceCC BY 4.0 Copyright OwnerAuthors Publisher DepositPubMed Central Location Any Repository Named Repository (PubMed Central) Journal Website ConditionsPublished source must be acknowledged with citation NotesAuthors are encouraged to submit their published articles to institutional repositories | en_US |
| dc.identifier.doi | 10.3390/s21134273 | en_US |
| kusw.oaversion | Scholarly/refereed, publisher version | en_US |
| kusw.oapolicy | This item meets KU Open Access policy criteria. | en_US |
| dc.identifier.pmid | PMC8271585 | en_US |
| dc.rights.accessrights | openAccess | en_US |
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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.
