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(Referência obtida automaticamente do Web of Science, por meio da informação sobre o financiamento pela FAPESP e o número do processo correspondente, incluída na publicação pelos autores.)

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

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Pineda, Aruane M. [1] ; Ramos, Fernando M. [2] ; Betting, Luiz Eduardo [3] ; Campanharo, Andriana S. L. O. [1]
Número total de Autores: 4
Afiliação do(s) autor(es):
[1] Sao Paulo State Univ UNESP, Inst Biosci, Dept Biostat, Botucatu, SP - Brazil
[2] Earth Syst Sci Ctr CCST, Natl Inst Space Res INPE, Sao Jose Dos Campos, SP - Brazil
[3] Sao Paulo State Univ UNESP, Botucatu Med Sch, Inst Biosci, Dept Neurol Psychol & Psychiat, Botucatu, SP - Brazil
Número total de Afiliações: 3
Tipo de documento: Artigo Científico
Fonte: PLoS One; v. 15, n. 6 JUN 5 2020.
Citações Web of Science: 0

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. (AU)

Processo FAPESP: 16/17914-3 - Efeito dos fármacos antiepilépticos e das descargas epileptiformes na conectividade funcional de pacientes com epilepsia mioclônica juvenil
Beneficiário:Luiz Eduardo Gomes Garcia Betting
Linha de fomento: Auxílio à Pesquisa - Regular
Processo FAPESP: 20/04989-0 - Grafos de quantis para o diagnóstico baseado em EEG da Doença de Alzheimer
Beneficiário:Andriana Susana Lopes de Oliveira Campanharo
Linha de fomento: Auxílio à Pesquisa - Publicações científicas - Artigo
Processo FAPESP: 18/25358-9 - Uso de redes complexas na detecção automática, no diagnóstico e na classificação da Doença de Alzheimer
Beneficiário:Andriana Susana Lopes de Oliveira Campanharo
Linha de fomento: Auxílio à Pesquisa - Regular