  Epileptic seizure prediction has gained considerable interest in the
computational Epilepsy research community. This paper presents a Machine
Learning based method for epileptic seizure prediction which outperforms
state-of-the art methods. We compute a probability for a given epoch, of being
pre-ictal against interictal using the Random Forest classifier and introduce
new concepts to enhance the robustness of the algorithm to false alarms. We
assessed our method on 20 patients of the benchmark scalp EEG CHB-MIT dataset
for a seizure prediction horizon (SPH) of 5 minutes and a seizure occurrence
period (SOP) of 30 minutes. Our approach achieves a sensitivity of 82.07 % and
a low false positive rate (FPR) of 0.0799 /h. We also tested our approach on
intracranial EEG recordings.
