Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6197
Full metadata record
DC FieldValueLanguage
dc.contributor.authorÜbeyli, Elif Derya-
dc.contributor.authorCvetkovic, Dean-
dc.contributor.authorHolland, Gerard-
dc.contributor.authorCosic, Irena-
dc.date.accessioned2021-09-11T15:35:15Z-
dc.date.available2021-09-11T15:35:15Z-
dc.date.issued2010en_US
dc.identifier.issn1051-2004-
dc.identifier.issn1095-4333-
dc.identifier.urihttps://doi.org/10.1016/j.dsp.2009.08.005-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/6197-
dc.description.abstractThe Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neurofuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity clue to hypopnoea episodes. (C) 2009 Elsevier Inc. All rights reserved.en_US
dc.language.isoenen_US
dc.publisherAcademic Press Inc Elsevier Scienceen_US
dc.relation.ispartofDigital Signal Processingen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAdaptive neuro-fuzzy inference system (ANFIS)en_US
dc.subjectWavelet coefficientsen_US
dc.subjectSleep apnoea hypopnoeaen_US
dc.subjectElectroencephalogram (EEG)en_US
dc.titleAdaptive Neuro-Fuzzy Inference System Employing Wavelet Coefficients for Detection of Alterations in Sleep Eeg Activity During Hypopnoea Episodesen_US
dc.typeArticleen_US
dc.departmentFaculties, Faculty of Engineering, Department of Electrical and Electronics Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümütr_TR
dc.identifier.volume20en_US
dc.identifier.issue3en_US
dc.identifier.startpage678en_US
dc.identifier.endpage691en_US
dc.authorid0000-0002-4218-7390-
dc.identifier.wosWOS:000276289800007en_US
dc.identifier.scopus2-s2.0-77949487095en_US
dc.institutionauthorÜbeyli, Elif Derya-
dc.identifier.doi10.1016/j.dsp.2009.08.005-
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.identifier.scopusqualityQ2-
item.openairetypeArticle-
item.languageiso639-1en-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
Show simple item record



CORE Recommender

SCOPUSTM   
Citations

21
checked on Dec 21, 2024

WEB OF SCIENCETM
Citations

24
checked on Aug 31, 2024

Page view(s)

100
checked on Dec 16, 2024

Google ScholarTM

Check




Altmetric


Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.