ELECTROENCEPHALOGRAM WAVE SIGNAL ANALYSIS AND EPILEPTIC SEIZURE PREDICTION USING SUPERVISED CLASSIFICATION APPROACH

Abstract

The transient and unexpected electrical disturbances of the brain results in an acute disease called Epileptic seizures. A significant way for identifying and analyzing epileptic seizure activity in human is by using electroencephalogram (EEG) signal. Manually reviewing and analyzing lengthy data of EEG recordings, for detection and classification of electro graphical patterns at present requires trained personnel and time consuming. Hence, there is a need for an efficient automated system based on pattern classification for analysis and classification of seizure-related EEG signals to assist the expert in the diagnosis. This paper presents the modeling of epileptic seizure prediction as binary classification problem and provides a suitable solution by implementing supervised classification algorithms, namely Decision table, Naive Baye's Tree, k-NN and support vector machine. The classification models are trained using the EEG data sets and the prediction accuracy of the classifier has been evaluated using 10-fold cross validation. It has been observed that the model produce about 86% of prediction accuracy in predicting the presence of epileptic seizure in human brain.