Fuzzy optimization with modified Adaboost classifier for epilepsy classification from EEG signals

Abstract

Epilepsy is recognized as a chronic neurological condition with the occurrence of recurrent seizures that alters the normal electrical activities in the neurons of the brain. Epilepsy can occur to any person irrespective of time and age as the exact cause of epilepsy is very difficult to know. For the diagnosis of epilepsy, Electroencephalograph (EEG) signals are widely used. The EEG signals are non-stationary and non-linear sequences of data which can be easily traced and detected when the electrodes are placed on the scalp of the patient. In this work, fuzzy optimization is used as a first level classifier to classify the epilepsy risk levels and then for second level classification, Adaboost Classifier and the Modified Adaboost Classifier are used for classification of epilepsy risk levels from EEG signals. Modified Adaboost Classifier is done through the implementation of Linear Discriminant Analysis (LDA) to Adaboost Classifier thereby enhancing the performance of the classifier. Results show that an average accuracy of 96.68% and an average quality value of 21.77 are obtained when the Modified Adaboost Classifier is used and an average accuracy of 97.20% and an average quality value of 22.51 is obtained when the Adaboost Classifier is used for the classification of epilepsy risk levels from EEG signals.