Application of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction

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dc.contributor.authorYildiz, Abdulnasir
dc.contributor.authorAkin, Mehmet
dc.contributor.authorPoyraz, Mustafa
dc.contributor.authorKirbas, Gokhan
dc.date.accessioned2024-04-24T16:11:24Z
dc.date.available2024-04-24T16:11:24Z
dc.date.issued2009
dc.departmentDicle Üniversitesien_US
dc.description.abstractThis paper presents the application of adaptive neuro-fuzzy inference system (ANFIS) model for estimation of vigilance level by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. The developed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. This study comprises of three stages. In the first stage, three types of EEG signals (alert signal, drowsy signal and sleep signal) were obtained from 30 healthy subjects. In the second stage, for feature extraction, obtained EEG signals were separated to its sub-bands using discrete wavelet transform (DWT). Then, entropy of each sub-band was calculated using Shannon entropy algorithm. In the third stage, the ANFIS was trained with the back-propagation gradient descent method in combination with least squares method. The extracted features of three types of EEG signals were used as input patterns of the three ANFIS classifiers. In order to improve estimation accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The performance of the ANFIS model was tested using the EEG data obtained from 12 healthy subjects that have not been used for the training. The results confirmed that the developed ANFIS classifier has potential for estimation of vigilance level by using EEG signals. (C) 2008 Elsevier Ltd. All rights reserved.en_US
dc.identifier.doi10.1016/j.eswa.2008.09.003
dc.identifier.endpage7399en_US
dc.identifier.issn0957-4174
dc.identifier.issn1873-6793
dc.identifier.issue4en_US
dc.identifier.scopus2-s2.0-60249085301
dc.identifier.scopusqualityQ1
dc.identifier.startpage7390en_US
dc.identifier.urihttps://doi.org/10.1016/j.eswa.2008.09.003
dc.identifier.urihttps://hdl.handle.net/11468/15382
dc.identifier.volume36en_US
dc.identifier.wosWOS:000264528600007
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoenen_US
dc.publisherPergamon-Elsevier Science Ltden_US
dc.relation.ispartofExpert Systems With Applications
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectAlerten_US
dc.subjectDrowsyen_US
dc.subjectSleepen_US
dc.subjectEntropyen_US
dc.subjectElectroencephalogram (Eeg) Signalsen_US
dc.subjectDiscrete Wavelet Transform (Dwt)en_US
dc.subjectAdaptive Neuro-Fuzzy Inference System (Anfis)en_US
dc.titleApplication of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extractionen_US
dc.titleApplication of adaptive neuro-fuzzy inference system for vigilance level estimation by using wavelet-entropy feature extraction
dc.typeArticleen_US

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