Computational methods of EEG signals analysis for Alzheimer’s disease classification

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorEarth System Science Center-
Autor(es): dc.creatorVicchietti, Mário L.-
Autor(es): dc.creatorRamos, Fernando M.-
Autor(es): dc.creatorBetting, Luiz E.-
Autor(es): dc.creatorCampanharo, Andriana S. L. O.-
Data de aceite: dc.date.accessioned2025-08-21T20:25:59Z-
Data de disponibilização: dc.date.available2025-08-21T20:25:59Z-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-07-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1038/s41598-023-32664-8-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/248849-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/248849-
Descrição: dc.descriptionComputational 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.-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Biodiversity and Biostatistics Institute of Biosciences São Paulo State University-
Descrição: dc.descriptionNational Institute for Space Research Earth System Science Center-
Descrição: dc.descriptionDepartment of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State University-
Descrição: dc.descriptionDepartment of Biodiversity and Biostatistics Institute of Biosciences São Paulo State University-
Descrição: dc.descriptionDepartment of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State University-
Descrição: dc.descriptionFAPESP: 2018/25358-9-
Descrição: dc.descriptionCAPES: 88887.602913/2021-00-
Idioma: dc.languageen-
Relação: dc.relationScientific Reports-
???dc.source???: dc.sourceScopus-
Título: dc.titleComputational methods of EEG signals analysis for Alzheimer’s disease classification-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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