Detecting Epileptic Seizures: Optimal Feature Extraction from EEG for Support Vector Machines
Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Epilepsy is a chronic neurological brain disorder causing the affected to have seizures.Looking at EEG recordings, an expert is able to identify epileptic activity and diagnosepatients with epilepsy. This process is time consuming and calls for automatization. Theautomation process is done through feature extraction and classification. The featureextraction finds features of the signal and the classification uses the features to classify thesignal as epileptic or not. The accuracy of the classification varies depending on both whichfeatures is chosen to represent each signal and which classification method is used. Onepopular method for classification of data is the SVM. This report tests and analyses six featureextraction methods with a linear SVM to see which method resulted in best classificationperformance when classifying epileptic EEG data. The results showed that two differentmethods resulted in classification accuracies significantly higher than the rest. The waveletbased method for feature extraction got a classification accuracy of 98.83% and the Hjorthfeatures method got a classification accuracy of 97.42%. However the results of these twomethods was too similar to be considered significantly different and therefore no conclusioncould be drawn of which was the best.
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IdentifiersURN: urn:nbn:se:kth:diva-166702OAI: oai:DiVA.org:kth-166702DiVA: diva2:811913