Localizing Epileptogenic Zone from High Density EEG Data Using Machine Learning

Abstract

Drug-resistant focal epilepsy is the failure of antiepileptic drugs scheduled to obtain epileptic free brain activities. In human brain, cerebral hemispheres are the most commonly involved brain regions in epilepsy. In case of antiepileptic drugs failure, surgical treatment is the best cure possible. However, correct localization of epileptogenic region is a challenging task for neurologists, while for computer scientists, automatic localization is. This research work’s aim is to explore the functional activities of all brain regions in drug-resistant focal epileptic patients and achieve high accuracy for the classification of epileptogenic region (ER) with the high-density electroencephalographic (hdEEG) data. The proposed system includes frequency analysis for feature extractions followed by individual subject’s registration of hdEEG signals with anatomical brain images for most precise localization of ER possible. The datasets attained from feature extraction process are then preprocessed for class imbalanced and then evaluated using different machine learning algorithms including the techniques under Bayesian networks, Lazy networks, Meta techniques, Rule based systems and Tree structured algorithms. Considering human brain as stationary object as well as dynamic object, frequency-based and time-frequency based features were considered in 12 subjects respectively. Through this novel approach, 99.70% accuracy is achieved to classify ER from healthy regions using KSTAR and using IBK algorithm, 91.60% accuracy has been achieved to classify generator from propagator.