Energy based feature extraction for classification of sleep apnea syndrome

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.contributor.authorSezgin, Necmettin
dc.contributor.authorTagluk, M. Emin
dc.date.accessioned2024-08-04T20:31:20Z
dc.date.available2024-08-04T20:31:20Z
dc.date.issued2009
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this paper it is aimed to classify sleep apnea syndrome (SAS) by using discrete wavelet transforms (DWT) and an artificial neural network (ANN). The abdominal and thoracic respiration signals are separated into spectral components by using multi-resolution DWT. Then the energy of these spectral components are applied to the inputs of the ANN. The neural network was configured to give three outputs to classify the SAS situation of the subject. The apnea can be mainly classified into three types: obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). During OSA, the airway is blocked while respiratory efforts continue. During CSA the airway is open, however, there are no respiratory efforts. In this paper we aim to classify sleep apnea in one of three basic types: obstructive, central and mixed. A significant result was obtained. (C) 2009 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.compbiomed.2009.08.005
dc.identifier.endpage1050en_US
dc.identifier.issn0010-4825
dc.identifier.issn1879-0534
dc.identifier.issue11en_US
dc.identifier.pmid19762012en_US
dc.identifier.scopus2-s2.0-70349783747en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1043en_US
dc.identifier.urihttps://doi.org/10.1016/j.compbiomed.2009.08.005
dc.identifier.urihttps://hdl.handle.net/11616/94887
dc.identifier.volume39en_US
dc.identifier.wosWOS:000271690500013en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofComputers in Biology and Medicineen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSASen_US
dc.subjectDiscrete wavelet transformen_US
dc.subjectEnergyen_US
dc.subjectArtificial neural networksen_US
dc.subjectAbdominal effort signalen_US
dc.subjectThoracic effort signalen_US
dc.titleEnergy based feature extraction for classification of sleep apnea syndromeen_US
dc.typeArticleen_US

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