Development of a personalized multiclass classification model to detect blood pressure variations associated with physical or cognitive workload

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dc.contributor.authorValerio, Andreaen
dc.contributor.authorDemarchi, Daniloen
dc.contributor.authorO'Flynn, Brendanen
dc.contributor.authorMotto Ros, Paoloen
dc.contributor.authorTedesco, Salvatoreen
dc.contributor.funderEnterprise Irelanden
dc.contributor.funderScience Foundation Irelanden
dc.contributor.funderEuropean Regional Development Funden
dc.date.accessioned2024-06-12T11:31:33Z
dc.date.available2024-06-12T11:31:33Z
dc.date.issued2024-06-06en
dc.description.abstractComprehending the regulatory mechanisms influencing blood pressure control is pivotal for continuous monitoring of this parameter. Implementing a personalized machine learning model, utilizing data-driven features, presents an opportunity to facilitate tracking blood pressure fluctuations in various conditions. In this work, data-driven photoplethysmograph features extracted from the brachial and digital arteries of 28 healthy subjects were used to feed a random forest classifier in an attempt to develop a system capable of tracking blood pressure. We evaluated the behavior of this latter classifier according to the different sizes of the training set and degrees of personalization used. Aggregated accuracy, precision, recall, and F1-score were equal to 95.1%, 95.2%, 95%, and 95.4% when 30% of a target subject’s pulse waveforms were combined with five randomly selected source subjects available in the dataset. Experimental findings illustrated that incorporating a pre-training stage with data from different subjects made it viable to discern morphological distinctions in beat-to-beat pulse waveforms under conditions of cognitive or physical workload.en
dc.description.sponsorshipEnterprise Ireland (Disruptive Technologies Innovation Fund (DTIF) project HOLISTICS)en
dc.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleid3697en
dc.identifier.citationValerio, A., Demarchi, D., O’Flynn, B., Motto Ros, P. and Tedesco, S. (2024) ‘Development of a personalized multiclass classification model to detect blood pressure variations associated with physical or cognitive workload’, Sensors, 24(11), p. 3697. Available at: https://doi.org/10.3390/s24113697en
dc.identifier.doihttps://doi.org/10.3390/s24113697en
dc.identifier.endpage22en
dc.identifier.issn1424-8220en
dc.identifier.issued11en
dc.identifier.journaltitleSensorsen
dc.identifier.startpage1en
dc.identifier.urihttps://hdl.handle.net/10468/16002
dc.identifier.volume24en
dc.language.isoenen
dc.publisherMDPIen
dc.relation.ispartofSensorsen
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 2/12/RC/2289-P2s/IE/INSIGHT Phase 2/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres/12/RC/2289/IE/INSIGHT - Irelands Big Data and Analytics Research Centre/en
dc.relation.projectinfo:eu-repo/grantAgreement/SFI/SFI Research Centres Programme::Phase 1/16/RC/3918/IE/Confirm Centre for Smart Manufacturing/en
dc.rights© 2024 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 (https://creativecommons.org/licenses/by/4.0/).en
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en
dc.subjectCuffless blood pressureen
dc.subjectPersonalized healthen
dc.subjectPhotoplethysmogramen
dc.subjectPulse transit timeen
dc.subjectPulse wave analysisen
dc.titleDevelopment of a personalized multiclass classification model to detect blood pressure variations associated with physical or cognitive workloaden
dc.typeArticle (peer-reviewed)en
oaire.citation.issue11en
oaire.citation.volume24en
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