XGboost-based Method for Seizure Detection in Mouse Models of Epilepsy

DC FieldValueLanguage
dc.contributor.authorWei, Lan-
dc.contributor.authorGerbatin, R.-
dc.contributor.authorMamad, O.-
dc.contributor.authorLowery, Madeleine M.-
dc.contributor.authorMooney, Catherine-
dc.date.accessioned2022-09-20T15:44:50Z-
dc.date.available2022-09-20T15:44:50Z-
dc.date.copyright2020 IEEEen_US
dc.date.issued2020-12-05-
dc.identifier.isbn9781728188201-
dc.identifier.urihttp://hdl.handle.net/10197/13125-
dc.descriptionThe 2020 IEEE Signal Processing in Medicine and Biology Symposium, Virtual Conference, 5 December 2020en_US
dc.description.abstractEpilepsy is a chronic neurological disease which affects over 50 million people worldwide [1], caused by the disruption of the finely tuned inhibitory and excitatory balance in brain networks, manifesting clinically as seizures. Electroencephalographic (EEG) monitoring in rodent disease models of epilepsy is critical in the understanding of disease mechanisms and the development of anti-seizure drugs. However, the visual annotation of EEG traces is time-consuming, and is complicated by different models and seizure types. Automated annotation systems can help to solve these problems by reducing expert annotation time and increasing the throughput and reliability of seizure quantification. As machine learning is becoming increasingly popular for modelling sequential signals such as EEG, several researchers have tried machine learning to detect seizures in EEG traces from mouse models of epilepsy. Most existing work [2], [3] can only detect seizures in single mouse models of epilepsy and research on multiple mouse models has been limited to-date.en_US
dc.description.sponsorshipEuropean Commission - Seventh Framework Programme (FP7)en_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.subjectBiological system modelingen_US
dc.subjectEpilepsyen_US
dc.subjectMachine learningen_US
dc.subjectBrain modelingen_US
dc.subjectMiceen_US
dc.subjectElectroencephalographyen_US
dc.titleXGboost-based Method for Seizure Detection in Mouse Models of Epilepsyen_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/SPMB50085.2020.9353632-
dc.neeo.contributorWei|Lan|aut|-
dc.neeo.contributorGerbatin|R.|aut|-
dc.neeo.contributorMamad|O.|aut|-
dc.neeo.contributorLowery|Madeleine M.|aut|-
dc.neeo.contributorMooney|Catherine|aut|-
dc.description.othersponsorshipFutureNeuro industry partnersen_US
dc.date.updated2021-07-27T12:40:28Z-
dc.identifier.grantid16/RC/3948-
dc.identifier.grantidH2020-MSCA-IF-2018 840262-
dc.identifier.grantid602130-
dc.rights.licensehttps://creativecommons.org/licenses/by-nc-nd/3.0/ie/en_US
item.grantfulltextopen-
item.fulltextWith Fulltext-
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