Classification of Focal and Non-focal EEG Signal for Epileptic Seizure Detection with Entropy Features Using KNN Classifier

Electroencephalography is the recording of the brain activity. It is used to diagnose the diverse disease conditions in the brain. In case of epilepsy, a specific part of the brain is affected. The EEG recorded from the affected part of the brain is called as focal EEG (FEEG), and the EEG recorded from the other portion is called as non-focal EEG (NFEEG). In this paper, an automatic method to classify the EEG signal has been proposed. Bern Barcelona database has been used in this method. Entropy features like approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn), and Reyni entropy (ReEn) features are analyzed. In this method, KNN classifier has been used to get the highest accuracy.

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