Efficient Sleep Stage Recognition System Based on EEG Signal Using K-Means Clustering Based Feature Weighting

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dc.contributor.authorGüneş, Salih
dc.contributor.authorPolat, Kemal
dc.contributor.authorYosunkaya, Şebnem
dc.date.accessioned2020-03-26T17:48:25Z
dc.date.available2020-03-26T17:48:25Z
dc.date.issued2010
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractSleep scoring is one of the most important methods for diagnosis in psychiatry and neurology. Sleep staging is a time consuming and difficult task conducted by sleep specialists. The purposes of this work are to automatic score the sleep stages and to help to sleep physicians on sleep stage scoring. In this work, a novel data preprocessing method called k-means clustering based feature weighting (KMCFW) has been proposed and combined with k-NN (k-nearest neighbor) and decision tree classifiers to classify the EEG (electroencephalogram) sleep into six sleep stages including awake, N-REM (non-rapid eye movement) stage 1, N-REM stage 2, N-REM stage 3, REM, and non-sleep (movement time). First of all, frequency domain features belonging to sleep EEG signal have been extracted using Welch spectral analysis method and composed 129 features from EEG signal relating each sleep stages. In order to decrease the features, the statistical features comprising minimum value, maximum value, standard deviation, and mean value have been used and then reduced from 129 to 4 features. In the second phase, the sleep stages dataset with four features has been weighted by means of k-means clustering based feature weighting. Finally, the weighted sleep stages have been automatically classified into six sleep stages using k-NN and C4.5 decision tree classifier. In the classification of sleep stages, the k values of 10, 20, 30, 40, 50, and 60 in k-NN classifier have been used and compared with each other. In the experimental results, while sleep stages has been classified with 55.88% success rate using k-NN classifier (for k value of 40), the weighted sleep stages with KMCFW has been recognized with 82.15% success rate k-NN classifier (for k value of 40). And also, we have investigated the relevance between sleep stages and frequency domain features belonging to EEG signal. These results have demonstrated that proposed weighting method have a considerable impact on automatic determining of sleep stages. This system could be used as an online system in the automatic scoring of sleep stages and helps to sleep physicians in the sleep scoring process.en_US
dc.description.sponsorshipTUBITAK Research ProjectTurkiye 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.citationGüneş, S., Polat, K., Yosunkaya, Ş., (2010). Efficient Sleep Stage Recognition System Based on EEG Signal Using K-Means Clustering Based Feature Weighting. Expert Systems with Applications, (37), 7922-7928. Doi: 10.1016/j.eswa.2010.04.043
dc.identifier.doi10.1016/j.eswa.2010.04.043en_US
dc.identifier.endpage7928en_US
dc.identifier.issn0957-4174en_US
dc.identifier.issn1873-6793en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage7922en_US
dc.identifier.urihttps://dx.doi.org/10.1016/j.eswa.2010.04.043
dc.identifier.urihttps://hdl.handle.net/20.500.12395/24879
dc.identifier.volume37en_US
dc.identifier.wosWOS:000281339900061en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGüneş, Salih
dc.institutionauthorPolat, Kemal
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/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectK-means clustering based feature weightingen_US
dc.subjectSleep scoringen_US
dc.subjectEEG signalen_US
dc.subjectK-nearest neighbor classifieren_US
dc.subjectDecision treeen_US
dc.subjectPolysomnographyen_US
dc.titleEfficient Sleep Stage Recognition System Based on EEG Signal Using K-Means Clustering Based Feature Weightingen_US
dc.typeArticleen_US

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