Ari, A.2024-08-042024-08-0420200013-51941350-911Xhttps://doi.org/10.1049/el.2020.2701https://hdl.handle.net/11616/99700Electroencephalogram (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.eninfo:eu-repo/semantics/closedAccessentropyelectroencephalographywavelet transformssignal classificationmedical signal processingfeature extractionmedical signal detectionpattern classificationsupport vector machinesbrainelectrical activitiesautomated systemcomputer-based expert opinionobjective viewEEG signalwavelet packetWPTdispersion entropyline length methodsextracted feature vectorsupport vector machineSVMneighbour classifiersKNNpublic EEG seizure datasetbinary casesmulticlass casesevaluated classification accuracybinary classificationmulticlass classification evaluated accuracyseizure detectionelectroencephalographydiagnostic methodabnormally functioning partArticle56251381138310.1049/el.2020.27012-s2.0-85098912031Q3WOS:000604957700010Q4