A Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithms

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dc.authoridSEN, BAHA/0000-0003-3577-2548
dc.contributor.authorSen, Baha
dc.contributor.authorPeker, Musa
dc.contributor.authorCavusoglu, Abdullah
dc.contributor.authorCelebi, Fatih V.
dc.date.accessioned2024-09-29T15:51:18Z
dc.date.available2024-09-29T15:51:18Z
dc.date.issued2014
dc.departmentKarabük Üniversitesien_US
dc.description.abstractSleep scoring is one of the most important diagnostic methods in psychiatry and neurology. Sleep staging is a time consuming and difficult task undertaken by sleep experts. This study aims to identify a method which would classify sleep stages automatically and with a high degree of accuracy and, in this manner, will assist sleep experts. This study consists of three stages: feature extraction, feature selection from EEG signals, and classification of these signals. In the feature extraction stage, it is used 20 attribute algorithms in four categories. 41 feature parameters were obtained from these algorithms. Feature selection is important in the elimination of irrelevant and redundant features and in this manner prediction accuracy is improved and computational overhead in classification is reduced. Effective feature selection algorithms such as minimum redundancy maximum relevance (mRMR); fast correlation based feature selection (FCBF); ReliefF; t-test; and Fisher score algorithms are preferred at the feature selection stage in selecting a set of features which best represent EEG signals. The features obtained are used as input parameters for the classification algorithms. At the classification stage, five different classification algorithms (random forest (RF); feed-forward neural network (FFNN); decision tree (DT); support vector machine (SVM); and radial basis function neural network (RBF)) classify the problem. The results, obtained from different classification algorithms, are provided so that a comparison can be made between computation times and accuracy rates. Finally, it is obtained 97.03 % classification accuracy using the proposed method. The results show that the proposed method indicate the ability to design a new intelligent assistance sleep scoring system.en_US
dc.identifier.doi10.1007/s10916-014-0018-0
dc.identifier.issn0148-5598
dc.identifier.issn1573-689X
dc.identifier.issue3en_US
dc.identifier.pmid24609509en_US
dc.identifier.scopus2-s2.0-84895092631en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1007/s10916-014-0018-0
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4002
dc.identifier.volume38en_US
dc.identifier.wosWOS:000334074900002en_US
dc.identifier.wosqualityQ2en_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.subjectEEG signalsen_US
dc.subjectClassification algorithmsen_US
dc.subjectFeature selection algorithmsen_US
dc.subjectClassification of sleep stageen_US
dc.titleA Comparative Study on Classification of Sleep Stage Based on EEG Signals Using Feature Selection and Classification Algorithmsen_US
dc.typeArticleen_US

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