Heuristic Active Learning for the Prediction of Epileptic Seizures Using Single EEG Channel
Autor(a) principal: | |
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Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1109/BIBM.2018.8621506 http://hdl.handle.net/11449/188806 |
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.Epistemic Gomez Gomez Ltda. ME Cidade Universitria, Av. Professor Lineu PrestesEscola Politecnica - USP Universidade de Sao Paulo, Rua Professor Luciano Gualberto travessa 3, no 380Instituto de Fisica Teorica - UNESP Universidade Estadual Paulista, Rua Dr. Bento Teobaldo Ferraz 271Instituto de Fisica Teorica - UNESP Universidade Estadual Paulista, Rua Dr. Bento Teobaldo Ferraz 271Cidade UniversitriaUniversidade de São Paulo (USP)Universidade Estadual Paulista (Unesp)Marques, Joao M.C.Cerdeira, Hilda A. [UNESP]Tanaka, EdgarVitor, Conrado DeGomez, Paula2019-10-06T16:19:51Z2019-10-06T16:19:51Z2019-01-21info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject2628-2634http://dx.doi.org/10.1109/BIBM.2018.8621506Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, p. 2628-2634.http://hdl.handle.net/11449/18880610.1109/BIBM.2018.86215062-s2.0-85062494953Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018info:eu-repo/semantics/openAccess2021-10-22T21:54:13Zoai:repositorio.unesp.br:11449/188806Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T18:25:19.884491Repositório 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 |
author_role |
author |
author2 |
Cerdeira, Hilda A. [UNESP] Tanaka, Edgar Vitor, Conrado De Gomez, Paula |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Cidade Universitria 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 |
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 |
2019 |
dc.date.none.fl_str_mv |
2019-10-06T16:19:51Z 2019-10-06T16:19:51Z 2019-01-21 |
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 |
http://dx.doi.org/10.1109/BIBM.2018.8621506 Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, p. 2628-2634. http://hdl.handle.net/11449/188806 10.1109/BIBM.2018.8621506 2-s2.0-85062494953 |
url |
http://dx.doi.org/10.1109/BIBM.2018.8621506 http://hdl.handle.net/11449/188806 |
identifier_str_mv |
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, p. 2628-2634. 10.1109/BIBM.2018.8621506 2-s2.0-85062494953 |
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 2018 |
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.source.none.fl_str_mv |
Scopus 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_ |
1808128929760804864 |