The problem of automated seizure detection is treated using clinical electroencephalograms (EEG) and machine learning algorithms on the Temple University Hospital EEG Seizure Corpus (TUSZ). Performances on this complex data set are still not encountering expectations. The purpose of this work is to determine to what extent the use of larger amount of data can help to improve the performances. Two methods are explored: a standard partitioning on a recent and larger version of the TUSZ, and a leave-one-out approach used to increase the amount of data for the training set. XGBoost, a fast implementation of the gradient boosting classifier, is the ideal algorithm for these tasks. The performances obtained are in the range of what is reported until now in the literature with deep learning models. We give interpretation to our results by identifying the most relevant features and analyzing performances by seizure types. We show that generalized seizures tend to be far better predicted than focal ones. We also notice that some EEG channels and features are more important than others to distinguish seizure from background.
Keywords: Temple University Hospital EEG Seizure Corpus; XGBoost; electroencephalograms; epileptic seizure; machine learning.