PRIOR FORECASTING OF EPILEPTIC SEIZURE AND LOCALIZATION OF EPILEPTOGENIC REGION

Abstract

Forecasting of an epileptic seizure and localization of the epileptogenic region is a challenging task. Scalp electroencephalogram (EEG) is the most commonly used signal for studying various brain disorders. This paper presents an algorithm for seizure forecast and detection of epileptogenic region by analyzing EEG signals from frontal, temporal, central and parietal region of the brain. Eight features have been extracted from each EEG signal. Average of features extracted from different regions of brain is computed for each region. An artificial neural network is trained to predict an epileptic seizure by identifying the pre-ictal duration. The trained neural network is tested and found to have an accuracy of 92.3%, sensitivity of 100% and specificity, of 83.3%. Two prominent features, accumulated energy and power in beta band, have been identified to identify the epileptogenic region. The result shows that the region corresponding to temporal lobe has maximum variation in these two features for pre-ictal and inter-ictal duration. The result validates the proposed algorithm to identify the pre-ictal state and predict the seizure in advance and identification of the epileptogenic region.