Quantile graphs for EEG-based diagnosis of Alzheimer’s disease

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (Unesp)-
Autor(es): dc.contributorEarth System Science Center (CCST)-
Autor(es): dc.creatorPineda, Aruane M. [UNESP]-
Autor(es): dc.creatorRamos, Fernando M.-
Autor(es): dc.creatorBetting, Luiz Eduardo [UNESP]-
Autor(es): dc.creatorCampanharo, Andriana S.L.O. [UNESP]-
Data de aceite: dc.date.accessioned2022-02-22T00:31:06Z-
Data de disponibilização: dc.date.available2022-02-22T00:31:06Z-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-12-11-
Data de envio: dc.date.issued2020-06-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1371/journal.pone.0231169-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/200576-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/200576-
Descrição: dc.descriptionKnown 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.-
Descrição: dc.descriptionFlorida State University-
Descrição: dc.descriptionCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)-
Descrição: dc.descriptionDepartment of Biostatistics Institute of Biosciences São Paulo State University (UNESP)-
Descrição: dc.descriptionNational Institute for Space Research (INPE) Earth System Science Center (CCST)-
Descrição: dc.descriptionDepartment of Neurology Psychology and Psychiatry Institute of Biosciences Botucatu Medical School São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Biostatistics Institute of Biosciences São Paulo State University (UNESP)-
Descrição: dc.descriptionDepartment of Neurology Psychology and Psychiatry Institute of Biosciences Botucatu Medical School São Paulo State University (UNESP)-
Descrição: dc.descriptionCAPES: 2016/ 17914-3-
Idioma: dc.languageen-
Relação: dc.relationPLoS ONE-
???dc.source???: dc.sourceScopus-
Título: dc.titleQuantile graphs for EEG-based diagnosis of Alzheimer’s disease-
Tipo de arquivo: dc.typelivro digital-
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