Data Mining in EEG: Application to Epileptic Brain Disorders

Epilepsy is one of the most common brain disorders. At least 40 million people or about 1% of the population worldwide currently suffer from epilepsy Despite advances in neurology and neuroscience, approximately 25–30% of epileptic patients remain unresponsive to anti-epileptic drug treatment, which is the standard therapy for epilepsy There is a growing body of evidence and interest in predicting epileptic seizures using intracranial electroencephalogram (EEG), which is a tool for evaluating the physiological state of the brain. Although recent studies in the EEG dynamics have been used to demonstrate seizure predictability, the question of whether the brain’s normal and pre-seizure epileptic activities are distinctive or differentiable remains unanswered. In this study, we apply data mining techniques to EEG data in order to verify the classifiability of the brain dynamics. We herein propose a quantitative analysis derived from the chaos theory to investigate the brain dynamics. We employ measures of chaoticity and complexity of EEG signals, including Short-Term Maximum Lyapunov Exponents, Angular Frequency, and Entropy, which were previously shown capable of contemplating dynamical mechanisms of the brain network. Each of these measures can be used to display the state transition toward seizures, in which different states of patients can be classified (normal, pre-seizure, and post-seizure states). In addition, optimization and data mining techniques are herein proposed for the extraction of classifiable features of the brain’s normal and pre-seizure epileptic states from spontaneous EEG. We use these features in study of classification of the brain’s normal and epileptic activities. A statistical cross validation is implemented to estimate the accuracy of the brain state classification. The results of this study indicate that it may be possible to design and develop efficient seizure warning algorithms for diagnostic and therapeutic purposes.