Abstracts

At least 2 million people in the U.S. (1% of population) currently suffer from epilepsy. The most disabling aspect of epilepsy is recurrent seizures, in which the majority of epileptic patients (at least 1 million) feel that they get inadequate treatments or seizures cannot be controlled by available treatments. It is very clear that improved treatments are desperately needed. In this study, we herein propose novel quantitative approaches that can detect abnormalities in the brain[apos]s electrical activity. The results of this study will pave our way to the development of an which is a prerequisite of seizure prediction process. Continuous 26-channel long-term intracranial EEG recordings previously obtained in 3 patients with medically intractable partial seizures were used to test the automated brain activity classifier. Patient 1 had 15 seizures in a 10-day recording; patient 2 had 8 seizures in 6 days; patient 3 had 7 seizures in 12 days. The automated brain activity classifier involved the following steps: (1) quantify the chaoticity properties (i.e., Lyapunov exponents, angular frequency, entropy) of the brain dynamics, (2a) use the statistical cross validation technique to estimate statistical distances between an EEG epoch and the brain activity from different physiological states (normal, pre-seizure, post-seizure), (2b) use optimization techniques to find support vector machines to separate different brain physiological states, (3) classify the EEG epoch to the physiological state of the most brain activity (smallest statistical distance) . The sensitivities of the [italic]statistical cross validation approach[/italic] in classifying [italic]pre-seizure EEG[apos]s[/italic] in patients 1, 2, and 3 were 89.39%, 85.71%, and 84.44%, respectively and the sensitivities in classifying [italic]normal EEG[apos]s[/italic] in patients 1, 2, and 3 were 93.50%, 78.00%, and 75.00%, respectively. The sensitivities of the [italic]support vector machines approach[/italic] in classifying [italic]pre-seizure EEG[apos]s[/italic] in patients 1, 2, and 3 were 81.21%, 71.18%, and 74.13%, respectively and the sensitivities in classifying [italic]normal EEG[apos]s[/italic] in patients 1, 2, and 3 were 87.46%, 76.85%, and 70.60%, respectively. Based on the proposed statistical classification approaches, this automated brain activity classifier can classify EEG epochs into the accurate brain physiological state with performance characteristics that could have practical clinical utility. The classifier could be incorporated into clinical EEG monitoring system or, incorporated into a seizure warning system used to activate timed physiological or pharmacological interventions.