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Epileptic seizure prediction has gained considerable interest in thecomputational Epilepsy research community. This paper presents a MachineLearning based method for epileptic seizure prediction which outperformsstate-of-the art methods. We compute a probability for a given epoch, of beingpre-ictal against interictal using the Random Forest classifier and introducenew concepts to enhance the robustness of the algorithm to false alarms. Weassessed our method on 20 patients of the benchmark scalp EEG CHB-MIT datasetfor a seizure prediction horizon (SPH) of 5 minutes and a seizure occurrenceperiod (SOP) of 30 minutes. Our approach achieves a sensitivity of 82.07 % anda low false positive rate (FPR) of 0.0799 /h. We also tested our approach onintracranial EEG recordings.

Random Forest classifier for EEG-based seizure prediction