Use of Complex Networks for the Automatic Detection and the Diagnosis of Alzheimer’s Disease

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MetadadosDescriçãoIdioma
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.creatorPineda, Aruane Mello-
Autor(es): dc.creatorRamos, Fernando M.-
Autor(es): dc.creatorBetting, Luiz Eduardo-
Autor(es): dc.creatorCampanharo, Andriana S. L. O.-
Data de aceite: dc.date.accessioned2021-03-11T01:38:26Z-
Data de disponibilização: dc.date.available2021-03-11T01:38:26Z-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2019-10-06-
Data de envio: dc.date.issued2019-01-01-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1007/978-3-030-20521-8_10-
Fonte completa do material: dc.identifierhttp://hdl.handle.net/11449/189277-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/189277-
Descrição: dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)-
Descrição: dc.descriptionProcesso FAPESP: 2018/25358-9-
Descrição: dc.descriptionAlzheimer’s disease (AD) is classified as a chronic neurological disorder of the brain and affects approximately 25 million elderly individuals worldwide. This disorder leads to a reduction in people’s productivity and imposes restrictions on their daily lives. Studies of AD often rely on electroencephalogram (EEG) signals to provide information on the behavior of the brain. Recently, a map from a time series to a network has been proposed and that is based on the concept of transition probabilities; the series results in a so-called “quantile graph” (QG). Here, this map, which is also called the QG method, is applied for the automatic detection of healthy patients and patients with AD from recorded EEG signals. Our main goal is to illustrate how the differences in dynamics in the EEG signals are reflected in the topology of the corresponding QGs. Based on various network metrics, namely, the clustering coefficient, the mean jump length and the betweenness centrality, our results show that the QG method can be used as an effective tool for automated diagnosis of Alzheimer’s disease.-
Formato: dc.format115-126-
Idioma: dc.languageen-
Relação: dc.relationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
Direitos: dc.rightsopenAccess-
Título: dc.titleUse of Complex Networks for the Automatic Detection and the Diagnosis of Alzheimer’s Disease-
Aparece nas coleções:Repositório Institucional - Unesp

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