Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , , , , , , , , , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://hdl.handle.net/11449/185428 |
Resumo: | Predicting epileptic seizure occurrence has long been a goal of the community surrounding it. Accurate prediction, however, is still elusive. This work presents a modified pipeline for the training of seizure prediction systems which aims to attenuate the effects of current data labeling strategies - and consequent data mislabeling of samples that heavily affect classifiers that are trained on it. This paper also presents a seizure prediction system trained following the proposed pipeline, which improved our system's performance by reducing its time-in-warning (TiW) by over 14%, while improving its prediction sensitivity to 72.4%, bringing its performance closer to the state-of-the-art performance (83.1% prediction sensitivity) for systems with similar TiW (41%) [1], while only requiring input from two scalp EEG electrodes - without making use of any variables external to the single EEG channels. |
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Repositório Institucional da UNESP |
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Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG ChannelPredicting epileptic seizure occurrence has long been a goal of the community surrounding it. Accurate prediction, however, is still elusive. This work presents a modified pipeline for the training of seizure prediction systems which aims to attenuate the effects of current data labeling strategies - and consequent data mislabeling of samples that heavily affect classifiers that are trained on it. This paper also presents a seizure prediction system trained following the proposed pipeline, which improved our system's performance by reducing its time-in-warning (TiW) by over 14%, while improving its prediction sensitivity to 72.4%, bringing its performance closer to the state-of-the-art performance (83.1% prediction sensitivity) for systems with similar TiW (41%) [1], while only requiring input from two scalp EEG electrodes - without making use of any variables external to the single EEG channels.Epistem Gomez & Gomez Ltda ME, Cietec, Ave Prof Lineu Prestes 2242,Sala 244, BR-05508000 Sao Paulo, BrazilUniv Sao Paulo, Escola Politecn, Rua Prof Luciano Gualberto Travessa 3,380, BR-05508010 Sao Paulo, BrazilUniv Estadual Paulista, Inst Fis Teor UNESP, Rua Dr Bento Teobaldo Ferraz 271,Bloco 2, BR-01140070 Sao Paulo, BrazilUniv Estadual Paulista, Inst Fis Teor UNESP, Rua Dr Bento Teobaldo Ferraz 271,Bloco 2, BR-01140070 Sao Paulo, BrazilIeeeEpistem Gomez & Gomez Ltda MEUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Marques, Joao M. C.Cerdeira, Hilda A. [UNESP]Tanaka, EdgarVitor, Conrado deGomez, PaulaZheng, H.Callejas, Z.Griol, D.Wang, H.Hu, XSchmidt, H.Baumbach, J.Dickerson, J.Zhang, L.2019-10-04T12:35:19Z2019-10-04T12:35:19Z2018-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2628-2634Proceedings 2018 Ieee International Conference On Bioinformatics And Biomedicine (bibm). New York: Ieee, p. 2628-2634, 2018.2156-1125http://hdl.handle.net/11449/185428WOS:000458654000450Web of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings 2018 Ieee International Conference On Bioinformatics And Biomedicine (bibm)info:eu-repo/semantics/openAccess2021-10-23T19:49:55Zoai:repositorio.unesp.br:11449/185428Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T19:14:37.988533Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
title |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
spellingShingle |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel Marques, Joao M. C. |
title_short |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
title_full |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
title_fullStr |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
title_full_unstemmed |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
title_sort |
Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel |
author |
Marques, Joao M. C. |
author_facet |
Marques, Joao M. C. Cerdeira, Hilda A. [UNESP] Tanaka, Edgar Vitor, Conrado de Gomez, Paula Zheng, H. Callejas, Z. Griol, D. Wang, H. Hu, X Schmidt, H. Baumbach, J. Dickerson, J. Zhang, L. |
author_role |
author |
author2 |
Cerdeira, Hilda A. [UNESP] Tanaka, Edgar Vitor, Conrado de Gomez, Paula Zheng, H. Callejas, Z. Griol, D. Wang, H. Hu, X Schmidt, H. Baumbach, J. Dickerson, J. Zhang, L. |
author2_role |
author author author author author author author author author author author author author |
dc.contributor.none.fl_str_mv |
Epistem Gomez & Gomez Ltda ME Universidade de São Paulo (USP) Universidade Estadual Paulista (Unesp) |
dc.contributor.author.fl_str_mv |
Marques, Joao M. C. Cerdeira, Hilda A. [UNESP] Tanaka, Edgar Vitor, Conrado de Gomez, Paula Zheng, H. Callejas, Z. Griol, D. Wang, H. Hu, X Schmidt, H. Baumbach, J. Dickerson, J. Zhang, L. |
description |
Predicting epileptic seizure occurrence has long been a goal of the community surrounding it. Accurate prediction, however, is still elusive. This work presents a modified pipeline for the training of seizure prediction systems which aims to attenuate the effects of current data labeling strategies - and consequent data mislabeling of samples that heavily affect classifiers that are trained on it. This paper also presents a seizure prediction system trained following the proposed pipeline, which improved our system's performance by reducing its time-in-warning (TiW) by over 14%, while improving its prediction sensitivity to 72.4%, bringing its performance closer to the state-of-the-art performance (83.1% prediction sensitivity) for systems with similar TiW (41%) [1], while only requiring input from two scalp EEG electrodes - without making use of any variables external to the single EEG channels. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-01-01 2019-10-04T12:35:19Z 2019-10-04T12:35:19Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/conferenceObject |
format |
conferenceObject |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
Proceedings 2018 Ieee International Conference On Bioinformatics And Biomedicine (bibm). New York: Ieee, p. 2628-2634, 2018. 2156-1125 http://hdl.handle.net/11449/185428 WOS:000458654000450 |
identifier_str_mv |
Proceedings 2018 Ieee International Conference On Bioinformatics And Biomedicine (bibm). New York: Ieee, p. 2628-2634, 2018. 2156-1125 WOS:000458654000450 |
url |
http://hdl.handle.net/11449/185428 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Proceedings 2018 Ieee International Conference On Bioinformatics And Biomedicine (bibm) |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
2628-2634 |
dc.publisher.none.fl_str_mv |
Ieee |
publisher.none.fl_str_mv |
Ieee |
dc.source.none.fl_str_mv |
Web of Science reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1808129039506866176 |