Identification Of Sleep/Wake Stages In Actigraphy Data Utilising Gradient Boosting Algorithm

Simple item page
but.event.date27.04.2021cs
but.event.titleSTUDENT EEICT 2021cs
dc.contributor.authorMikulec, Marek
dc.date.accessioned2023-01-06T10:05:41Z
dc.date.available2023-01-06T10:05:41Z
dc.date.issued2021cs
dc.description.abstractSleep disorders are early markers of various serious diseases that can be treated moreeffectively when diagnosed in their prodromal stage. Actigraphy is a noninvasive sleep monitoringmethod for the detection of sleep patterns and determination of sleep parameters that could support thediagnosis of these disorders. This study aims to compare a newly proposed actigraphy-based methodof sleep/wake detection with a conventional one in terms of consistency with a polysomnography(PSG) reference. 55 recordings (acquired in 28 subjects) of actigraphy and PSG were modelled by aheuristics-based method and by a new approach utilising a gradient boosting algorithm. In addition,another database (22 subjects, 150 recordings) was used to compare scores of the new method withdata reported in sleep diaries. The proposed method achieves 89% accuracy and Mathews correlationcoefficient equal to 0.75 when compared to the polysomnography reference. Such results outperformthe ones provided by the heuristic technique. The newly proposed method has good consistency withthe PSG reference, thus being a good alternative to the golden standard in sleep disorders assessment,especially in decentralised clinical trials.en
dc.formattextcs
dc.format.extent270-274cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings II of the 27st Conference STUDENT EEICT 2021: Selected Papers. s. 270-274. ISBN 978-80-214-5943-4cs
dc.identifier.doi10.13164/eeict.2021.270
dc.identifier.isbn978-80-214-5943-4
dc.identifier.urihttp://hdl.handle.net/11012/200855
dc.language.isoencs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings II of the 27st Conference STUDENT EEICT 2021: Selected papersen
dc.relation.urihttps://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazenics
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectactigraphyen
dc.subjectmachine learningen
dc.subjectpolysomnographyen
dc.subjectsleepen
dc.subjectsleep diaryen
dc.titleIdentification Of Sleep/Wake Stages In Actigraphy Data Utilising Gradient Boosting Algorithmen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
270_EEICT_2021_2.pdf
Size:
1.13 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Student EEICT 2021: Selected Papers