Classification of sleep apnea by using wavelet transform and artificial neural networks

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.contributor.authorTagluk, M. Emin
dc.contributor.authorAkin, Mehmet
dc.contributor.authorSezgin, Nemettin
dc.date.accessioned2024-08-04T20:32:13Z
dc.date.available2024-08-04T20:32:13Z
dc.date.issued2010
dc.departmentİnönü Üniversitesien_US
dc.description.abstractThis paper describes a new method to classify sleep apnea syndrome (SAS) by using wavelet transforms and an artificial neural network (ANN) The network was trained and tested for different momentum coefficients. The abdominal respiration signals are separated into spectral components by using multi-resolution wavelet transforms. These spectral components are applied to the inputs of the artificial neural network. Then the neural network was configured to give three outputs to classify the SAS situation of the patient. The apnea can be broadly 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. (C) 2009 Elsevier Ltd. Ail rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2009.06.049
dc.identifier.endpage1607en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue2en_US
dc.identifier.scopus2-s2.0-71749120142en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1600en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2009.06.049
dc.identifier.urihttps://hdl.handle.net/11616/94926
dc.identifier.volume37en_US
dc.identifier.wosWOS:000272432300082en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applicationsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSleep apnea syndromeen_US
dc.subjectWavelet transformen_US
dc.subjectArtificial neural networksen_US
dc.subjectAbdominal effort signalen_US
dc.titleClassification of sleep apnea by using wavelet transform and artificial neural networksen_US
dc.typeArticleen_US

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