Analysis of EEG signal for seizure detection based on WPT

Basit Öğe Kaydı
dc.contributor.authorAri, A.
dc.date.accessioned2024-08-04T20:49:13Z
dc.date.available2024-08-04T20:49:13Z
dc.date.issued2020
dc.departmentİnönü Üniversitesien_US
dc.description.abstractElectroencephalogram (EEG) is a diagnostic method that provides information about the functioning of the brain. EEG can be used to diagnose the abnormally functioning part of the brain by monitoring electrical activities. An automated system has been proposed to create a computer-based expert opinion needed in the detection of epilepsy and to capture a more objective view. To this end, the approximation and detail coefficients of EEG signals are calculated by using the wavelet packet transform (WPT). The coefficients were subjected to feature extraction using dispersion entropy and line length methods. The extracted feature vector has been applied as input to the support vector machine (SVM) and k-nearest neighbour (KNN) classifiers. The proposed method was tested using the public EEG seizure dataset created by the University of Bonn. In this study, the dataset was evaluated in two different ways as binary cases and multiclass cases. Evaluated classification accuracy was 100% for binary classification with SVM. For multiclass classification evaluated accuracy was 99.85% with KNN. The proposed method was compared with other methods in the literature using the same dataset. The comparison results provide the superiority of the proposed method.en_US
dc.identifier.doi10.1049/el.2020.2701
dc.identifier.endpage1383en_US
dc.identifier.issn0013-5194
dc.identifier.issn1350-911X
dc.identifier.issue25en_US
dc.identifier.scopus2-s2.0-85098912031en_US
dc.identifier.scopusqualityQ3en_US
dc.identifier.startpage1381en_US
dc.identifier.urihttps://doi.org/10.1049/el.2020.2701
dc.identifier.urihttps://hdl.handle.net/11616/99700
dc.identifier.volume56en_US
dc.identifier.wosWOS:000604957700010en_US
dc.identifier.wosqualityQ4en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherInst Engineering Technology-Ieten_US
dc.relation.ispartofElectronics Lettersen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectentropyen_US
dc.subjectelectroencephalographyen_US
dc.subjectwavelet transformsen_US
dc.subjectsignal classificationen_US
dc.subjectmedical signal processingen_US
dc.subjectfeature extractionen_US
dc.subjectmedical signal detectionen_US
dc.subjectpattern classificationen_US
dc.subjectsupport vector machinesen_US
dc.subjectbrainen_US
dc.subjectelectrical activitiesen_US
dc.subjectautomated systemen_US
dc.subjectcomputer-based expert opinionen_US
dc.subjectobjective viewen_US
dc.subjectEEG signalen_US
dc.subjectwavelet packeten_US
dc.subjectWPTen_US
dc.subjectdispersion entropyen_US
dc.subjectline length methodsen_US
dc.subjectextracted feature vectoren_US
dc.subjectsupport vector machineen_US
dc.subjectSVMen_US
dc.subjectneighbour classifiersen_US
dc.subjectKNNen_US
dc.subjectpublic EEG seizure dataseten_US
dc.subjectbinary casesen_US
dc.subjectmulticlass casesen_US
dc.subjectevaluated classification accuracyen_US
dc.subjectbinary classificationen_US
dc.subjectmulticlass classification evaluated accuracyen_US
dc.subjectseizure detectionen_US
dc.subjectelectroencephalographyen_US
dc.subjectdiagnostic methoden_US
dc.subjectabnormally functioning parten_US
dc.titleAnalysis of EEG signal for seizure detection based on WPTen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu