Sleep spindles recognition system based on time and frequency domain features

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dc.contributor.authorGunes, Salih
dc.contributor.authorDursun, Mehmet
dc.contributor.authorPolat, Kemal
dc.contributor.authorYosunkaya, Sebnem
dc.date.accessioned2020-03-26T18:16:10Z
dc.date.available2020-03-26T18:16:10Z
dc.date.issued2011
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSleep spindle is the one of important components determining N-REM (Non-Rapid Eye Movement) stage 2 in the sleep stages. The symptoms of N-REM stage 2 are sleep spindle and K-complex and here sleep spindles are automatically recognized by using time and frequency domain features belonging to EEG (Electroencephalograph) signals obtained from three patient subjects. In this study, the proposed method consists of two steps. In the first step, six time domain features have been extracted from raw EEG signals. As for the extraction of frequency domain features from raw EEG signals, Welch spectral analysis has been used and applied to raw EEG signals. By this way, 65 frequency domain features have been extracted and reduced from 65 to 4 features by using statistical measures including minimum, maximum, standard deviation, and mean values. Three feature sets including only time domain, only frequency domain, and both time and frequency domain features have been used and the numbers of these feature sets are 6, 4, and 10, respectively. In the second step, artificial neural network (ANN) with LM (Levenberg-Marquardt) has been used to classify the sleep spindles evaluated beforehand by sleep expert physicians. The obtained classification accuracies for three features sets in the classification of sleep spindles are 100%, 56.86%, and 93.84% by using LM-ANN (for ten node in hidden layer). The obtained results have presented that the proposed recognition system could be confidently used in the automatic classification of sleep spindles. (C) 2010 Elsevier Ltd. All rights reserved.en_US
dc.description.sponsorshipTUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [108E033]; Selcuk UniversitySelcuk Universityen_US
dc.description.sponsorshipThis work is supported fully by the TUBITAK Research Project under Grant No. 108E033. And also, this study is supported by the Scientific Research Projects of Selcuk University.en_US
dc.identifier.doi10.1016/j.eswa.2010.08.034en_US
dc.identifier.endpage2461en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issue3en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage2455en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2010.08.034
dc.identifier.urihttps://hdl.handle.net/20.500.12395/26823
dc.identifier.volume38en_US
dc.identifier.wosWOS:000284863200127en_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.selcuk20240510_oaigen_US
dc.subjectSleep spindlesen_US
dc.subjectEEGen_US
dc.subjectTime domain featuresen_US
dc.subjectWelch methoden_US
dc.subjectArtificial neural networken_US
dc.titleSleep spindles recognition system based on time and frequency domain featuresen_US
dc.typeArticleen_US

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