Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/41479
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dc.contributor.authorMendonça, Fábioen_US
dc.contributor.authorFred, Anaen_US
dc.contributor.authorMostafa, Sheikh Shanawazen_US
dc.contributor.authorMorgado-Dias, Fernandoen_US
dc.contributor.authorRavelo-García, Antonio G.en_US
dc.date.accessioned2018-07-05T11:51:05Z-
dc.date.available2018-07-05T11:51:05Z-
dc.date.issued2018en_US
dc.identifier.issn0941-0643en_US
dc.identifier.urihttp://hdl.handle.net/10553/41479-
dc.description.abstractThe cyclic alternating pattern is a microstructure phasic event, present in the non-rapid eye movement sleep, which has been associated with multiple pathologies, and is a marker of sleep instability that is detected using the electroencephalogram. However, this technique produces a large quantity of information during a full night test, making the task of manually scoring all the cyclic alternating pattern cycles unpractical, with a high probability of miss classification. Therefore, the aim of this work is to develop and test multiple algorithms capable of automatically detecting the cyclic alternating pattern. The employed method first analyses the electroencephalogram signal to extract features that are used as inputs to a classifier that detects the activation (A phase) and quiescent (B phase) phases of this pattern. The output of the classifier was then applied to a finite state machine implementing the cyclic alternating pattern classification. A systematic review was performed to determine the features and classifiers that could be more relevant. Nine classifiers were tested using features selected by a sequential feature selection algorithm and features produced by principal component analysis. The best performance was achieved using a feed-forward neural network, producing, respectively, an average accuracy, sensitivity, specificity and area under the curve of 79, 76, 80% and 0.77 in the A and B phases classification. The cyclic alternating pattern detection accuracy, using the finite state machine, was of 79%.en_US
dc.languageengen_US
dc.relationProjeto Estratégico LA 9—UID/EEA/50009/2013en_US
dc.relation.ispartofNeural Computing and Applicationsen_US
dc.sourceNeural Computing and Applications [ISSN 0941-0643], 4 abril 2018en_US
dc.subject120325 Diseño de sistemas sensoresen_US
dc.subject3307 Tecnología electrónicaen_US
dc.subject.otherAutomatic classificationen_US
dc.subject.otherCAPen_US
dc.subject.otherA phaseen_US
dc.titleAutomatic detection of cyclic alternating patternen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.typeArticleen_US
dc.identifier.doi10.1007/s00521-018-3474-5en_US
dc.identifier.scopus85044941206-
dc.contributor.authorscopusid57195946416-
dc.contributor.authorscopusid6602080284-
dc.contributor.authorscopusid55489640900-
dc.contributor.authorscopusid57200602527-
dc.contributor.authorscopusid9634135600-
dc.description.lastpage11en_US
dc.description.firstpage1en_US
dc.investigacionIngeniería y Arquitecturaen_US
dc.type2Artículoen_US
dc.description.numberofpages11en_US
dc.utils.revisionen_US
dc.date.coverdateAbril 2018en_US
dc.identifier.ulpgcen_US
dc.contributor.buulpgcBU-TELen_US
dc.description.sjr0,637
dc.description.jcr4,664
dc.description.sjrqQ2
dc.description.jcrqQ1
dc.description.scieSCIE
item.grantfulltextopen-
item.fulltextCon texto completo-
crisitem.author.deptGIR IDeTIC: División de Procesado Digital de Señales-
crisitem.author.deptIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.deptDepartamento de Señales y Comunicaciones-
crisitem.author.orcid0000-0002-8512-965X-
crisitem.author.parentorgIU para el Desarrollo Tecnológico y la Innovación-
crisitem.author.fullNameRavelo García, Antonio Gabriel-
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