Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/44842
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dc.contributor.authorVermunicht, P.-
dc.contributor.authorBuyck, C.-
dc.contributor.authorMakayed, K.-
dc.contributor.authorHerman, S.-
dc.contributor.authorGiraldo, J. S. Piedrahita-
dc.contributor.authorLaukens, K.-
dc.contributor.authorKNAEPEN, Lieselotte-
dc.contributor.authorNaessens, S.-
dc.contributor.authorHens, W.-
dc.contributor.authorVan Craenenbroeck, E.-
dc.contributor.authorDESTEGHE, Lien-
dc.contributor.authorHEIDBUCHEL, Hein-
dc.date.accessioned2024-12-16T07:07:39Z-
dc.date.available2024-12-16T07:07:39Z-
dc.date.issued2024-
dc.date.submitted2024-12-12T15:40:43Z-
dc.identifier.citationEuropean heart journal, 45 (S1) (Art N° ehae6663533)-
dc.identifier.urihttp://hdl.handle.net/1942/44842-
dc.description.abstractBackground: Heart rate (HR) tracking by wrist-worn devices using photoplethysmography (PPG) could assist in continuously following up physical activity. However, the accuracy can be impacted by (motion) artefacts. Machine learning models could help to recognise artefacts in PPG-based HR data. The choice of classifier in these machine learning models is a determing factor for task performance of the model.-
dc.description.sponsorshipType of funding sources: Public grant(s) – National budget only. Main funding source(s): This project is supported by the Fund for Scientific Research, Flanders (G084023N)-
dc.language.isoen-
dc.publisherOXFORD UNIV PRESS-
dc.titleOptimisation of artefact detection in photoplethysmography heart rate data: influence of different classifiers in machine learning models-
dc.typeJournal Contribution-
dc.identifier.issueS1-
dc.identifier.volume45-
local.format.pages2-
local.bibliographicCitation.jcatM-
dc.description.notesVermunicht, P (corresponding author), Univ Antwerp, Res Grp Cardiovasc Dis, Antwerp, Belgium.-
dc.description.notespaulien.vermunicht@uantwerpen.be-
local.publisher.placeGREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND-
local.type.refereedRefereed-
local.type.specifiedMeeting Abstract-
local.bibliographicCitation.artnrehae6663533-
dc.identifier.doi10.1093/eurheartj/ehae666.3533-
dc.identifier.isi001359718000023-
local.provider.typewosris-
local.description.affiliation[Vermunicht, P.; Buyck, C.] Univ Antwerp, Res Grp Cardiovasc Dis, Antwerp, Belgium.-
local.description.affiliation[Makayed, K.; Herman, S.] Univ Antwerp, Dept Comp Sci, Antwerp, Belgium.-
local.description.affiliation[Giraldo, J. S. Piedrahita; Laukens, K.] Univ Antwerp, Biomed Informat Res Network Antwerp Biomina, Antwerp, Belgium.-
local.description.affiliation[Knaepen, L.] Hasselt Univ, Fac Med & Life Sci, Hasselt, Belgium.-
local.description.affiliation[Naessens, S.; Hens, W.; Van Craenenbroeck, E.; Heidbuchel, H.] Univ Hosp Antwerp, Dept Cardiol, Antwerp, Belgium.-
local.uhasselt.internationalno-
item.fulltextWith Fulltext-
item.accessRightsOpen Access-
item.fullcitationVermunicht, P.; Buyck, C.; Makayed, K.; Herman, S.; Giraldo, J. S. Piedrahita; Laukens, K.; KNAEPEN, Lieselotte; Naessens, S.; Hens, W.; Van Craenenbroeck, E.; DESTEGHE, Lien & HEIDBUCHEL, Hein (2024) Optimisation of artefact detection in photoplethysmography heart rate data: influence of different classifiers in machine learning models. In: European heart journal, 45 (S1) (Art N° ehae6663533).-
item.contributorVermunicht, P.-
item.contributorBuyck, C.-
item.contributorMakayed, K.-
item.contributorHerman, S.-
item.contributorGiraldo, J. S. Piedrahita-
item.contributorLaukens, K.-
item.contributorKNAEPEN, Lieselotte-
item.contributorNaessens, S.-
item.contributorHens, W.-
item.contributorVan Craenenbroeck, E.-
item.contributorDESTEGHE, Lien-
item.contributorHEIDBUCHEL, Hein-
crisitem.journal.issn0195-668X-
crisitem.journal.eissn1522-9645-
Appears in Collections:Research publications
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