Quantile graphs for EEG-based diagnosis of Alzheimer’s disease
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1371/journal.pone.0231169 http://hdl.handle.net/11449/200576 |
Resumo: | Known as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here—clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index—showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients’ scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs. |
id |
UNSP_001dd0ecdfc2622a1f086c3bedd8fc81 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/200576 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
Quantile graphs for EEG-based diagnosis of Alzheimer’s diseaseKnown as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here—clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index—showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients’ scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs.Florida State UniversityCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Department of Biostatistics Institute of Biosciences São Paulo State University (UNESP)National Institute for Space Research (INPE) Earth System Science Center (CCST)Department of Neurology Psychology and Psychiatry Institute of Biosciences Botucatu Medical School São Paulo State University (UNESP)Department of Biostatistics Institute of Biosciences São Paulo State University (UNESP)Department of Neurology Psychology and Psychiatry Institute of Biosciences Botucatu Medical School São Paulo State University (UNESP)CAPES: 2016/ 17914-3Universidade Estadual Paulista (Unesp)Earth System Science Center (CCST)Pineda, Aruane M. [UNESP]Ramos, Fernando M.Betting, Luiz Eduardo [UNESP]Campanharo, Andriana S.L.O. [UNESP]2020-12-12T02:10:14Z2020-12-12T02:10:14Z2020-06-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1371/journal.pone.0231169PLoS ONE, v. 15, n. 6, 2020.1932-6203http://hdl.handle.net/11449/20057610.1371/journal.pone.02311692-s2.0-85086051337Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengPLoS ONEinfo:eu-repo/semantics/openAccess2021-10-23T14:48:05Zoai:repositorio.unesp.br:11449/200576Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462021-10-23T14:48:05Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
title |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
spellingShingle |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease Pineda, Aruane M. [UNESP] |
title_short |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
title_full |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
title_fullStr |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
title_full_unstemmed |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
title_sort |
Quantile graphs for EEG-based diagnosis of Alzheimer’s disease |
author |
Pineda, Aruane M. [UNESP] |
author_facet |
Pineda, Aruane M. [UNESP] Ramos, Fernando M. Betting, Luiz Eduardo [UNESP] Campanharo, Andriana S.L.O. [UNESP] |
author_role |
author |
author2 |
Ramos, Fernando M. Betting, Luiz Eduardo [UNESP] Campanharo, Andriana S.L.O. [UNESP] |
author2_role |
author author author |
dc.contributor.none.fl_str_mv |
Universidade Estadual Paulista (Unesp) Earth System Science Center (CCST) |
dc.contributor.author.fl_str_mv |
Pineda, Aruane M. [UNESP] Ramos, Fernando M. Betting, Luiz Eduardo [UNESP] Campanharo, Andriana S.L.O. [UNESP] |
description |
Known as a degenerative and progressive dementia, Alzheimer’s disease (AD) affects about 25 million elderly people around the world. This illness results in a decrease in the productivity of people and places limits on their daily lives. Electroencephalography (EEG), in which the electrical brain activity is recorded in the form of time series and analyzed using signal processing techniques, is a well-known neurophysiological AD biomarker. EEG is noninvasive, low-cost, has a high temporal resolution, and provides valuable information about brain dynamics in AD. Here, we present an original approach based on the use of quantile graphs (QGs) for classifying EEG data. QGs map frequency, amplitude, and correlation characteristics of a time series (such as the EEG data of an AD patient) into the topological features of a network. The five topological network metrics used here—clustering coefficient, mean jump length, betweenness centrality, modularity, and Laplacian Estrada index—showed that the QG model can distinguish healthy subjects from AD patients, with open or closed eyes. The QG method also indicates which channels (corresponding to 19 different locations on the patients’ scalp) provide the best discriminating power. Furthermore, the joint analysis of delta, theta, alpha, and beta wave results indicate that all AD patients under study display clear symptoms of the disease and may have it in its late stage, a diagnosis known a priori and supported by our study. Results presented here attest to the usefulness of the QG method in analyzing complex, nonlinear signals such as those generated from AD patients by EEGs. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-12-12T02:10:14Z 2020-12-12T02:10:14Z 2020-06-01 |
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/10.1371/journal.pone.0231169 PLoS ONE, v. 15, n. 6, 2020. 1932-6203 http://hdl.handle.net/11449/200576 10.1371/journal.pone.0231169 2-s2.0-85086051337 |
url |
http://dx.doi.org/10.1371/journal.pone.0231169 http://hdl.handle.net/11449/200576 |
identifier_str_mv |
PLoS ONE, v. 15, n. 6, 2020. 1932-6203 10.1371/journal.pone.0231169 2-s2.0-85086051337 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
PLoS ONE |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
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_ |
1803650109297131520 |