Epileptic seizure detection using EEG signals and extreme gradient boosting
Paul Vanabelle1
Pierre De Handschutter2
Ri(e)m El Tahry3
Mohammed Benjelloun2
Mohamed Boukhebouze1
1.Data Science Department, Centre of Excellence in Information and Communication Technologies, Charleroi 6041, Belgium2.Computer Science Unit, Faculty of Engineering, University of Mons, Mons 7000, Belgium3.Refractory Epilepsy Centre, University Hospital of Saint-Luc, Brussels 1200, Belgium;Institute of Neuroscience, Catholic University of Louvain, Brussels 1200, Belgium
摘要：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.
机标关键词：
分类号：R742.1(神经病学与精神病学)
论文发表日期：2020-05-30
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：12( 228-239 )
英文信息