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.
Cite
@article{arxiv.2106.04510,
title = {Random Forest classifier for EEG-based seizure prediction},
author = {Remy Ben Messaoud and Mario Chavez},
journal= {arXiv preprint arXiv:2106.04510},
year = {2021}
}
Comments
all the python code used for this work can be found in this repository: https://github.com/rbm1996/seizure_prediction