Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/37179
DC FieldValueLanguage
dc.contributor.authorProcházka, A.en_US
dc.contributor.authorKuchyňka, J.en_US
dc.contributor.authorYadollahi, M.en_US
dc.contributor.authorSuárez Araujo, Carmen Pazen_US
dc.contributor.authorVyšata, O.en_US
dc.date.accessioned2018-05-28T08:36:04Z-
dc.date.available2018-05-28T08:36:04Z-
dc.date.issued2017en_US
dc.identifier.issn2165-3577en_US
dc.identifier.otherWoS-
dc.identifier.urihttp://hdl.handle.net/10553/37179-
dc.description.abstractThe paper presents a new algorithm for adaptive classification of sleep stages using multimodal data recorded in the sleep laboratory during overnight polysomnography records. The proposed method includes the learning process applied for the set of individuals with their sleep stages classified by an experienced neurologist. Features evaluated for time windows 30 s long and selected multimodal signals are used for construction and optimization of the proposed two-layer neural network model. Resulting computational system based upon breathing EEG and EOG features is used for analysis of new individuals to detect their sleep stages. Results include classification accuracy higher than 80% and 90% for Wake and REM stages, respectively. The proposed method can adaptively modify model coefficients to detect sleep stages and sleeping disorders using man-machine interaction.en_US
dc.languageengen_US
dc.relation.ispartofInternational Conference on Digital Signal Processing proceedingsen_US
dc.source2017 22Nd International Conference On Digital Signal Processing (Dsp) [ISSN 1546-1874], (2017)en_US
dc.subject120304 Inteligencia artificialen_US
dc.subject.otherDepth sensorsen_US
dc.subject.otherClassificationen_US
dc.subject.otherKinecten_US
dc.subject.otherImageen_US
dc.titleAdaptive segmentation of multimodal polysomnography data for sleep stages detectionen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.typeConferenceObjecten_US
dc.relation.conference2017 22nd International Conference on Digital Signal Processing, DSP 2017en_US
dc.identifier.doi10.1109/ICDSP.2017.8096108en_US
dc.identifier.scopus85040321735-
dc.identifier.isi000426874700074-
dc.contributor.authorscopusid7005747805-
dc.contributor.authorscopusid57189064215-
dc.contributor.authorscopusid56956816300-
dc.contributor.authorscopusid23476354000-
dc.contributor.authorscopusid6602874156-
dc.identifier.eissn2165-3577-
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Actas de congresosen_US
dc.contributor.daisngid581151-
dc.contributor.daisngid3121044-
dc.contributor.daisngid2234050-
dc.contributor.daisngid1776211-
dc.contributor.daisngid442215-
dc.description.numberofpages4en_US
dc.identifier.eisbn978-1-5386-1895-0-
dc.utils.revisionen_US
dc.contributor.wosstandardWOS:Prochazka, A-
dc.contributor.wosstandardWOS:Kuchynka, J-
dc.contributor.wosstandardWOS:Yadollahi, M-
dc.contributor.wosstandardWOS:Araujo, CPS-
dc.contributor.wosstandardWOS:Vysata, O-
dc.date.coverdateNoviembre 2017en_US
dc.identifier.conferenceidevents121090-
dc.identifier.ulpgces
dc.description.ggs3
item.fulltextSin texto completo-
item.grantfulltextnone-
crisitem.event.eventsstartdate23-08-2017-
crisitem.event.eventsenddate25-08-2017-
crisitem.author.deptGIR IUCES: Computación inteligente, percepción y big data-
crisitem.author.deptIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.deptDepartamento de Informática y Sistemas-
crisitem.author.orcid0000-0002-8826-0899-
crisitem.author.parentorgIU de Cibernética, Empresa y Sociedad (IUCES)-
crisitem.author.fullNameSuárez Araujo, Carmen Paz-
Appears in Collections:Actas de congresos
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