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

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dc.contributor.authorVicchietti, Mário L. [UNESP]
dc.contributor.authorRamos, Fernando M.
dc.contributor.authorBetting, Luiz E. [UNESP]
dc.contributor.authorCampanharo, Andriana S. L. O. [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionEarth System Science Center
dc.date.accessioned2023-07-29T13:55:27Z
dc.date.available2023-07-29T13:55:27Z
dc.date.issued2023-12-01
dc.description.abstractComputational 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.en
dc.description.affiliationDepartment of Biodiversity and Biostatistics Institute of Biosciences São Paulo State University
dc.description.affiliationNational Institute for Space Research Earth System Science Center
dc.description.affiliationDepartment of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State University
dc.description.affiliationUnespDepartment of Biodiversity and Biostatistics Institute of Biosciences São Paulo State University
dc.description.affiliationUnespDepartment of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State University
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.description.sponsorshipCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
dc.description.sponsorshipIdFAPESP: 2018/25358-9
dc.description.sponsorshipIdCAPES: 88887.602913/2021-00
dc.identifierhttp://dx.doi.org/10.1038/s41598-023-32664-8
dc.identifier.citationScientific Reports, v. 13, n. 1, 2023.
dc.identifier.doi10.1038/s41598-023-32664-8
dc.identifier.issn2045-2322
dc.identifier.scopus2-s2.0-85159678189
dc.identifier.urihttp://hdl.handle.net/11449/248849
dc.language.isoeng
dc.relation.ispartofScientific Reports
dc.sourceScopus
dc.titleComputational methods of EEG signals analysis for Alzheimer’s disease classificationen
dc.typeArtigo

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