Automated EEG Signals Analysis Using Quantile Graphs
Autor(a) principal: | |
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Data de Publicação: | 2017 |
Outros Autores: | , , , , |
Tipo de documento: | Artigo de conferência |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1007/978-3-319-59147-6_9 http://hdl.handle.net/11449/164585 |
Resumo: | Recently, a map from time series to networks has been proposed [7,6], allowing the use of network statistics to characterize time series. In this approach, time series quantiles are naturally mapped into nodes of a graph. Networks generated by this method, called Quantile Graphs (QGs), are able to capture and quantify features such as long-range correlations or randomness present in the underlying dynamics of the original signal. Here we apply the QG method to the problem of detecting the differences between electroencephalographic time series (EEG) of healthy and unhealthy subjects. Our main goal is to illustrate how the differences in dynamics are reflected in the topology of the corresponding QGs. Results show that the QG method cannot only differentiate epileptic from normal data, but also distinguish the different abnormal stages/patterns of a seizure, such as preictal (EEG changes preceding a seizure) and ictal (EEG changes during a seizure). |
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Automated EEG Signals Analysis Using Quantile GraphsElectroencephalographic time seriesEpilepsyComplex networksQuantile graphsRecently, a map from time series to networks has been proposed [7,6], allowing the use of network statistics to characterize time series. In this approach, time series quantiles are naturally mapped into nodes of a graph. Networks generated by this method, called Quantile Graphs (QGs), are able to capture and quantify features such as long-range correlations or randomness present in the underlying dynamics of the original signal. Here we apply the QG method to the problem of detecting the differences between electroencephalographic time series (EEG) of healthy and unhealthy subjects. Our main goal is to illustrate how the differences in dynamics are reflected in the topology of the corresponding QGs. Results show that the QG method cannot only differentiate epileptic from normal data, but also distinguish the different abnormal stages/patterns of a seizure, such as preictal (EEG changes preceding a seizure) and ictal (EEG changes during a seizure).Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)Univ Estadual Paulista, Dept Bioestat, Inst Biociencias, Botucatu, SP, BrazilUniv Fed Sao Paulo, Dept Ciencia & Tecnol, Campus Sao Jose dos Campos, Sao Paulo, BrazilInst Nacl Pesquisas Espaciais, Lab Comp & Matemat Aplicada, Sao Jose Dos Campos, SP, BrazilUniv Estadual Paulista, Dept Bioestat, Inst Biociencias, Botucatu, SP, BrazilFAPESP: 2013/19905-3SpringerUniversidade Estadual Paulista (Unesp)Universidade Federal de São Paulo (UNIFESP)Inst Nacl Pesquisas EspaciaisCampanharo, Andriana S. L. O. [UNESP]Doescher, ErwinRamos, Fernando M.Rojas, IJoya, G.Catala, A.2018-11-26T17:55:12Z2018-11-26T17:55:12Z2017-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject95-103application/pdfhttp://dx.doi.org/10.1007/978-3-319-59147-6_9Advances In Computational Intelligence, Iwann 2017, Pt Ii. Cham: Springer International Publishing Ag, v. 10306, p. 95-103, 2017.0302-9743http://hdl.handle.net/11449/16458510.1007/978-3-319-59147-6_9WOS:000443108700009WOS000443108700009.pdfWeb of Sciencereponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengAdvances In Computational Intelligence, Iwann 2017, Pt Ii0,295info:eu-repo/semantics/openAccess2023-11-10T06:13:40Zoai:repositorio.unesp.br:11449/164585Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T17:20:12.523326Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Automated EEG Signals Analysis Using Quantile Graphs |
title |
Automated EEG Signals Analysis Using Quantile Graphs |
spellingShingle |
Automated EEG Signals Analysis Using Quantile Graphs Campanharo, Andriana S. L. O. [UNESP] Electroencephalographic time series Epilepsy Complex networks Quantile graphs |
title_short |
Automated EEG Signals Analysis Using Quantile Graphs |
title_full |
Automated EEG Signals Analysis Using Quantile Graphs |
title_fullStr |
Automated EEG Signals Analysis Using Quantile Graphs |
title_full_unstemmed |
Automated EEG Signals Analysis Using Quantile Graphs |
title_sort |
Automated EEG Signals Analysis Using Quantile Graphs |
author |
Campanharo, Andriana S. L. O. [UNESP] |
author_facet |
Campanharo, Andriana S. L. O. [UNESP] Doescher, Erwin Ramos, Fernando M. Rojas, I Joya, G. Catala, A. |
author_role |
author |
author2 |
Doescher, Erwin Ramos, Fernando M. Rojas, I Joya, G. Catala, A. |
author2_role |
author author author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Universidade Federal de São Paulo (UNIFESP) Inst Nacl Pesquisas Espaciais |
dc.contributor.author.fl_str_mv |
Campanharo, Andriana S. L. O. [UNESP] Doescher, Erwin Ramos, Fernando M. Rojas, I Joya, G. Catala, A. |
dc.subject.por.fl_str_mv |
Electroencephalographic time series Epilepsy Complex networks Quantile graphs |
topic |
Electroencephalographic time series Epilepsy Complex networks Quantile graphs |
description |
Recently, a map from time series to networks has been proposed [7,6], allowing the use of network statistics to characterize time series. In this approach, time series quantiles are naturally mapped into nodes of a graph. Networks generated by this method, called Quantile Graphs (QGs), are able to capture and quantify features such as long-range correlations or randomness present in the underlying dynamics of the original signal. Here we apply the QG method to the problem of detecting the differences between electroencephalographic time series (EEG) of healthy and unhealthy subjects. Our main goal is to illustrate how the differences in dynamics are reflected in the topology of the corresponding QGs. Results show that the QG method cannot only differentiate epileptic from normal data, but also distinguish the different abnormal stages/patterns of a seizure, such as preictal (EEG changes preceding a seizure) and ictal (EEG changes during a seizure). |
publishDate |
2017 |
dc.date.none.fl_str_mv |
2017-01-01 2018-11-26T17:55:12Z 2018-11-26T17:55:12Z |
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/10.1007/978-3-319-59147-6_9 Advances In Computational Intelligence, Iwann 2017, Pt Ii. Cham: Springer International Publishing Ag, v. 10306, p. 95-103, 2017. 0302-9743 http://hdl.handle.net/11449/164585 10.1007/978-3-319-59147-6_9 WOS:000443108700009 WOS000443108700009.pdf |
url |
http://dx.doi.org/10.1007/978-3-319-59147-6_9 http://hdl.handle.net/11449/164585 |
identifier_str_mv |
Advances In Computational Intelligence, Iwann 2017, Pt Ii. Cham: Springer International Publishing Ag, v. 10306, p. 95-103, 2017. 0302-9743 10.1007/978-3-319-59147-6_9 WOS:000443108700009 WOS000443108700009.pdf |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Advances In Computational Intelligence, Iwann 2017, Pt Ii 0,295 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
95-103 application/pdf |
dc.publisher.none.fl_str_mv |
Springer |
publisher.none.fl_str_mv |
Springer |
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 |
|
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1808128792681512960 |