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dc.contributor.authorKrálík, M.
dc.date.accessioned2015-08-25T08:42:57Z
dc.date.available2015-08-25T08:42:57Z
dc.date.issued2015cs
dc.identifier.citationProceedings of the 21st Conference STUDENT EEICT 2015. s. 215-217. ISBN 978-80-214-5148-3cs
dc.identifier.isbn978-80-214-5148-3
dc.identifier.urihttp://hdl.handle.net/11012/42980
dc.description.abstractThis work is focused on classification of sleep phases using artificial neural network. The unconventional approach was used for calculation of classification features using polysomnographic data (PSG) of real patients. This approach allows to increase the time resolution of the analysis and, thus, to achieve more accurate results of classification.en
dc.formattextcs
dc.format.extent215-217cs
dc.format.mimetypeapplication/pdfen
dc.language.isocscs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 21st Conference STUDENT EEICT 2015en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectpolysomnographyen
dc.subjectsleep scoringen
dc.subjectclassification featuresen
dc.subjectneural networksen
dc.titlePSG-Based Classification of Sleep Phasesen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date23.04.2015cs
but.event.titleStudent EEICT 2015cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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