Epileptic Seizure Detection in Clinical EEGs Using an XGboost-based Method

DC FieldValueLanguage
dc.contributor.authorWei, Lan-
dc.contributor.authorMooney, Catherine-
dc.date.accessioned2022-09-20T15:34:45Z-
dc.date.available2022-09-20T15:34:45Z-
dc.date.copyright2020 IEEEen_US
dc.date.issued2020-12-05-
dc.identifier.isbn9781728188201-
dc.identifier.urihttp://hdl.handle.net/10197/13124-
dc.descriptionThe 2020 IEEE Signal Processing in Medicine and Biology Symposium, Virtual Conference, 5 December 2020en_US
dc.description.abstractEpilepsy is one of the most common serious disorders of the brain, affecting about 50 million people worldwide. Electroencephalography (EEG) is an electrophysiological monitoring method which is used to measure tiny electrical changes of the brain, and it is frequently used to diagnose epilepsy. However, the visual annotation of EEG traces is time-consuming and typically requires experienced experts. Therefore, automatic seizure detection can help to reduce the time required to annotate EEGs. Automatic detection of seizures in clinical EEGs has been limited-to date. In this study, we present an XGBoost-based method to detect seizures in EEGs from the TUH-EEG Corpus. 4,597 EEG files were used to train the method, 1,013 EEGs were used as a validation set, and 1,026 EEG files were used to test the method. Sixty-four features were selected as the input to the training set, and Synthetic Minority Over-sampling Technique was used to balance the dataset. Our XGBoost-based method achieved sensitivity and false alarm/24 hours of 20.00% and 15.59, respectively, in the test set. The proposed XGBoost-based method has the potential to help researchers automatically analyse seizures in clinical EEG recordings.en_US
dc.description.sponsorshipEuropean Commission - European Regional Development Funden_US
dc.description.sponsorshipScience Foundation Irelanden_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB 2020)en_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.subjectTUH-EEGen_US
dc.subjectCorpusen_US
dc.subjectSeizure detectionen_US
dc.subjectEpilepsyen_US
dc.subjectMachine learningen_US
dc.titleEpileptic Seizure Detection in Clinical EEGs Using an XGboost-based Methoden_US
dc.typeConference Publicationen_US
dc.internal.authorcontactothercatherine.mooney@ucd.ieen_US
dc.internal.webversionshttps://www.ieeespmb.org/2020/-
dc.statusPeer revieweden_US
dc.identifier.doi10.1109/SPMB50085.2020.9353625-
dc.neeo.contributorWei|Lan|aut|-
dc.neeo.contributorMooney|Catherine|aut|-
dc.description.othersponsorshipFutureNeuro industry partnersen_US
dc.date.updated2021-07-27T12:39:35Z-
dc.identifier.grantid16/RC/3948-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.fulltextWith Fulltext-
item.grantfulltextopen-
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