Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/s00521-017-3124-3 http://hdl.handle.net/11449/179040 |
Resumo: | Epilepsy is a class of chronic neurological disorders characterized by transient and unexpected electrical disturbances of the brain. The automated analysis of the electroencephalogram (EEG) signal can be instrumental for the proper diagnosis of this mental condition. This work presents a systematic assessment of the performance of different variants of the binary magnetic optimization algorithm (BMOA), two of which are introduced here, while serving as feature selectors for epileptic EEG signal identification. In this context, the optimum-path forest classifier was adopted as a classification model, whereas different wavelet families were considered for EEG feature extraction. In order to compare the performance of the improved BMOA variants against the traditional one, as well as other metaheuristic techniques, namely particle swarm optimization, binary bat algorithm, and genetic algorithm, we employed a well-known EEG benchmark dataset composed of five classes of EEG signals (two of which comprising normal patients with eyes open or closed, and the remaining comprising ill patients with different levels of epilepsy). Overall, the results evidenced the robustness of the proposed BMOA and its variants. |
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Repositório Institucional da UNESP |
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Automatic identification of epileptic EEG signals through binary magnetic optimization algorithmsEEG signal classificationEpilepsyFeature selectionMagnetic optimization algorithmMetaheuristicsOptimum-path forestEpilepsy is a class of chronic neurological disorders characterized by transient and unexpected electrical disturbances of the brain. The automated analysis of the electroencephalogram (EEG) signal can be instrumental for the proper diagnosis of this mental condition. This work presents a systematic assessment of the performance of different variants of the binary magnetic optimization algorithm (BMOA), two of which are introduced here, while serving as feature selectors for epileptic EEG signal identification. In this context, the optimum-path forest classifier was adopted as a classification model, whereas different wavelet families were considered for EEG feature extraction. In order to compare the performance of the improved BMOA variants against the traditional one, as well as other metaheuristic techniques, namely particle swarm optimization, binary bat algorithm, and genetic algorithm, we employed a well-known EEG benchmark dataset composed of five classes of EEG signals (two of which comprising normal patients with eyes open or closed, and the remaining comprising ill patients with different levels of epilepsy). Overall, the results evidenced the robustness of the proposed BMOA and its variants.Instituto de Computação Universidade Estadual de CampinasDepartamento de Computação UNESP - Univ Estadual PaulistaPrograma de Pós-Graduação em Informática Aplicada Universidade de FortalezaEscola de Artes Ciências e Humanidades Universidade de São PauloDepartamento de Computação UNESP - Univ Estadual PaulistaUniversidade Estadual de Campinas (UNICAMP)Universidade Estadual Paulista (Unesp)Universidade de FortalezaUniversidade de São Paulo (USP)Pereira, Luís A. M.Papa, João P. [UNESP]Coelho, André L. V.Lima, Clodoaldo A. M.Pereira, Danillo R. [UNESP]de Albuquerque, Victor Hugo C.2018-12-11T17:33:16Z2018-12-11T17:33:16Z2017-06-28info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article1-13application/pdfhttp://dx.doi.org/10.1007/s00521-017-3124-3Neural Computing and Applications, p. 1-13.0941-0643http://hdl.handle.net/11449/17904010.1007/s00521-017-3124-32-s2.0-850251469782-s2.0-85025146978.pdfScopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengNeural Computing and Applications0,700info:eu-repo/semantics/openAccess2024-04-23T16:10:45Zoai:repositorio.unesp.br:11449/179040Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T16:10:19.477997Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
title |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
spellingShingle |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms Pereira, Luís A. M. EEG signal classification Epilepsy Feature selection Magnetic optimization algorithm Metaheuristics Optimum-path forest |
title_short |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
title_full |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
title_fullStr |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
title_full_unstemmed |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
title_sort |
Automatic identification of epileptic EEG signals through binary magnetic optimization algorithms |
author |
Pereira, Luís A. M. |
author_facet |
Pereira, Luís A. M. Papa, João P. [UNESP] Coelho, André L. V. Lima, Clodoaldo A. M. Pereira, Danillo R. [UNESP] de Albuquerque, Victor Hugo C. |
author_role |
author |
author2 |
Papa, João P. [UNESP] Coelho, André L. V. Lima, Clodoaldo A. M. Pereira, Danillo R. [UNESP] de Albuquerque, Victor Hugo C. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual de Campinas (UNICAMP) Universidade Estadual Paulista (Unesp) Universidade de Fortaleza Universidade de São Paulo (USP) |
dc.contributor.author.fl_str_mv |
Pereira, Luís A. M. Papa, João P. [UNESP] Coelho, André L. V. Lima, Clodoaldo A. M. Pereira, Danillo R. [UNESP] de Albuquerque, Victor Hugo C. |
dc.subject.por.fl_str_mv |
EEG signal classification Epilepsy Feature selection Magnetic optimization algorithm Metaheuristics Optimum-path forest |
topic |
EEG signal classification Epilepsy Feature selection Magnetic optimization algorithm Metaheuristics Optimum-path forest |
description |
Epilepsy is a class of chronic neurological disorders characterized by transient and unexpected electrical disturbances of the brain. The automated analysis of the electroencephalogram (EEG) signal can be instrumental for the proper diagnosis of this mental condition. This work presents a systematic assessment of the performance of different variants of the binary magnetic optimization algorithm (BMOA), two of which are introduced here, while serving as feature selectors for epileptic EEG signal identification. In this context, the optimum-path forest classifier was adopted as a classification model, whereas different wavelet families were considered for EEG feature extraction. In order to compare the performance of the improved BMOA variants against the traditional one, as well as other metaheuristic techniques, namely particle swarm optimization, binary bat algorithm, and genetic algorithm, we employed a well-known EEG benchmark dataset composed of five classes of EEG signals (two of which comprising normal patients with eyes open or closed, and the remaining comprising ill patients with different levels of epilepsy). Overall, the results evidenced the robustness of the proposed BMOA and its variants. |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-06-28 2018-12-11T17:33:16Z 2018-12-11T17:33:16Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.1007/s00521-017-3124-3 Neural Computing and Applications, p. 1-13. 0941-0643 http://hdl.handle.net/11449/179040 10.1007/s00521-017-3124-3 2-s2.0-85025146978 2-s2.0-85025146978.pdf |
url |
http://dx.doi.org/10.1007/s00521-017-3124-3 http://hdl.handle.net/11449/179040 |
identifier_str_mv |
Neural Computing and Applications, p. 1-13. 0941-0643 10.1007/s00521-017-3124-3 2-s2.0-85025146978 2-s2.0-85025146978.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Neural Computing and Applications 0,700 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
1-13 application/pdf |
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_ |
1808128613448417280 |