Computational methods of EEG signals analysis for Alzheimer’s disease classification
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.1038/s41598-023-32664-8 http://hdl.handle.net/11449/248849 |
Resumo: | Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients. |
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Computational methods of EEG signals analysis for Alzheimer’s disease classificationComputational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Department of Biodiversity and Biostatistics Institute of Biosciences São Paulo State UniversityNational Institute for Space Research Earth System Science CenterDepartment of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State UniversityDepartment of Biodiversity and Biostatistics Institute of Biosciences São Paulo State UniversityDepartment of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State UniversityFAPESP: 2018/25358-9CAPES: 88887.602913/2021-00Universidade Estadual Paulista (UNESP)Earth System Science CenterVicchietti, Mário L. [UNESP]Ramos, Fernando M.Betting, Luiz E. [UNESP]Campanharo, Andriana S. L. O. [UNESP]2023-07-29T13:55:27Z2023-07-29T13:55:27Z2023-12-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://dx.doi.org/10.1038/s41598-023-32664-8Scientific Reports, v. 13, n. 1, 2023.2045-2322http://hdl.handle.net/11449/24884910.1038/s41598-023-32664-82-s2.0-85159678189Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengScientific Reportsinfo:eu-repo/semantics/openAccess2024-08-16T15:46:03Zoai:repositorio.unesp.br:11449/248849Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-16T15:46:03Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
title |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
spellingShingle |
Computational methods of EEG signals analysis for Alzheimer’s disease classification Vicchietti, Mário L. [UNESP] |
title_short |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
title_full |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
title_fullStr |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
title_full_unstemmed |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
title_sort |
Computational methods of EEG signals analysis for Alzheimer’s disease classification |
author |
Vicchietti, Mário L. [UNESP] |
author_facet |
Vicchietti, Mário L. [UNESP] Ramos, Fernando M. Betting, Luiz E. [UNESP] Campanharo, Andriana S. L. O. [UNESP] |
author_role |
author |
author2 |
Ramos, Fernando M. Betting, Luiz E. [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 |
dc.contributor.author.fl_str_mv |
Vicchietti, Mário L. [UNESP] Ramos, Fernando M. Betting, Luiz E. [UNESP] Campanharo, Andriana S. L. O. [UNESP] |
description |
Computational analysis of electroencephalographic (EEG) signals have shown promising results in detecting brain disorders, such as Alzheimer’s disease (AD). AD is a progressive neurological illness that causes neuron cells degeneration, resulting in cognitive impairment. While there is no cure for AD, early diagnosis is critical to improving the quality of life of affected individuals. Here, we apply six computational time-series analysis methods (wavelet coherence, fractal dimension, quadratic entropy, wavelet energy, quantile graphs and visibility graphs) to EEG records from 160 AD patients and 24 healthy controls. Results from raw and wavelet-filtered (alpha, beta, theta and delta bands) EEG signals show that some of the time-series analysis methods tested here, such as wavelet coherence and quantile graphs, can robustly discriminate between AD patients from elderly healthy subjects. They represent a promising non-invasive and low-cost approach to the AD detection in elderly patients. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-07-29T13:55:27Z 2023-07-29T13:55:27Z 2023-12-01 |
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/10.1038/s41598-023-32664-8 Scientific Reports, v. 13, n. 1, 2023. 2045-2322 http://hdl.handle.net/11449/248849 10.1038/s41598-023-32664-8 2-s2.0-85159678189 |
url |
http://dx.doi.org/10.1038/s41598-023-32664-8 http://hdl.handle.net/11449/248849 |
identifier_str_mv |
Scientific Reports, v. 13, n. 1, 2023. 2045-2322 10.1038/s41598-023-32664-8 2-s2.0-85159678189 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
Scientific Reports |
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 |
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1808128173239435264 |