ArticleJournal of biomedical research2019
Epileptic seizure detection using EEG signals and extreme gradient boosting.
Article in Journal of biomedical research, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Retraining and evaluation of machine learning and deep learning models for seizure classification from EEG data.Scientific reports · 2025Article
- Combining meta and ensemble learning to classify EEG for seizure detection.Scientific reports · 2025Article
- Automatic Detection and Classification of Epileptic Seizures from EEG Data: Finding Optimal Acquisition Settings and Testing Interpretable Machine Learning Approach.Biomedicines · 2023Article
- Data Augmentation Effects on Highly Imbalanced EEG Datasets for Automatic Detection of Photoparoxysmal Responses.Sensors (Basel, Switzerland) · 2023Article
- Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data.Journal of healthcare engineering · 2022Article
- Editorial commentary on special issue of Advances in EEG Signal Processing and Machine Learning for Epileptic Seizure Detection and Prediction.Journal of biomedical research · 2020Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
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.
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Registered trials
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