A genetic approach to selecting the optimal feature for epileptic seizure prediction

The objective of this study is to (1) develop and apply efficient algorithms to simultaneous intracranial electroencephalographic signals recorded from multiple implanted electrode sites to evaluate the spatial and temporal behavior of seizure precursors and (2) to demonstrate the utility of multiple feature and channel synergy for predicting epileptic seizures in patients with mesial temporal lobe epilepsy. Short-term seizure precursors within a 10-minute time period are investigated. The method consists of preprocessing, processing, feature selection, classification, and validation steps. The preprocessing step removes extraneous data and captures the salient signal attributes while maintaining the integrity of the signal. Processing is a three-step approach that includes first-level features extracted from the raw data, second-level features extracted from first level features, and third-level features extracted from second-level features. A genetic algorithm selects the optimal features off-line from a preselected group of features to serve as the input to the classifier.