Classification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets plus 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.authorSezgin, Necmettin
dc.date.accessioned2024-08-04T20:32:36Z
dc.date.available2024-08-04T20:32:36Z
dc.date.issued2010
dc.departmentİnönü Üniversitesien_US
dc.description.abstractDetection and classification of sleep apnea syndrome (SAS) is a critical problem. In this study an efficient method for classification sleep apnea through sub-band energy of abdominal effort using a particularly designed hybrid classifier as Wavelets + Neural Network is proposed. The Abdominal respiration signals were separated into spectral sub-band energy components with multi-resolution Discrete Wavelet Transform (DWT). The energy content of these spectral components was applied to the input of the artificial neural network (ANN). The ANN was configured to give three outputs dedicated to SAS cases; obstructive sleep apnea (OSA), central sleep apnea (CSA) and mixed sleep apnea (MSA). Through the network, satisfactory results that rewarding 85.62% mean accuracy in classifying SAS were obtained.en_US
dc.identifier.doi10.1007/s10916-009-9330-5
dc.identifier.endpage1119en_US
dc.identifier.issn0148-5598
dc.identifier.issn1573-689X
dc.identifier.issue6en_US
dc.identifier.pmid20703596en_US
dc.identifier.scopus2-s2.0-78649326656en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1111en_US
dc.identifier.urihttps://doi.org/10.1007/s10916-009-9330-5
dc.identifier.urihttps://hdl.handle.net/11616/95187
dc.identifier.volume34en_US
dc.identifier.wosWOS:000283258000014en_US
dc.identifier.wosqualityQ3en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofJournal of Medical Systemsen_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.subjectSub-band energyen_US
dc.subjectArtificial neural networksen_US
dc.subjectAbdominal effort signalen_US
dc.titleClassification of Sleep Apnea through Sub-band Energy of Abdominal Effort Signal Using Wavelets plus Neural Networksen_US
dc.typeArticleen_US

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