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Currently, there is no standard approach for evaluating the intracranial encephalographic signals for seizure prediction. This study evaluates the IEEG signals by applying a systematic approach to feature selection, classification and validation to predict seizures. After preprocessing and processing, a genetic algorithm selects reasonable features off-line from a preselected group of features to serve as inputs to the classifier based feature selection process. A probabilistic neural network is used to select the optimal feature vector using a reed forward sequential approach on the training data followed by classification. A study of four patients resulted in a 62.5% average probability of prediction and a block false positive rate of 0.2775 false positive predictions per hour.