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dc.contributor.authorPineda, Aruane Mello [UNESP]
dc.contributor.authorRamos, Fernando M.
dc.contributor.authorBetting, Luiz Eduardo [UNESP]
dc.contributor.authorCampanharo, Andriana S. L. O. [UNESP]
dc.date.accessioned2019-10-06T16:35:34Z
dc.date.available2019-10-06T16:35:34Z
dc.date.issued2019-01-01
dc.identifierhttp://dx.doi.org/10.1007/978-3-030-20521-8_10
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v. 11506 LNCS, p. 115-126.
dc.identifier.issn1611-3349
dc.identifier.issn0302-9743
dc.identifier.urihttp://hdl.handle.net/11449/189277
dc.description.abstractAlzheimer’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.en
dc.description.sponsorshipFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.format.extent115-126
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.sourceScopus
dc.titleUse of Complex Networks for the Automatic Detection and the Diagnosis of Alzheimer’s Diseaseen
dc.typeTrabalho apresentado em evento
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionNational Institute for Space Research (INPE)
dc.description.affiliationInstitute of Biosciences Department of Biostatistics São Paulo State University (UNESP)
dc.description.affiliationLaboratory for Computing and Applied Mathematics National Institute for Space Research (INPE)
dc.description.affiliationInstitute of Biosciences Department of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State University (UNESP)
dc.description.affiliationUnespInstitute of Biosciences Department of Biostatistics São Paulo State University (UNESP)
dc.description.affiliationUnespInstitute of Biosciences Department of Neurology Psychology and Psychiatry Botucatu Medical School São Paulo State University (UNESP)
dc.identifier.doi10.1007/978-3-030-20521-8_10
dc.rights.accessRightsAcesso aberto
dc.description.sponsorshipIdFAPESP: 2018/25358-9
dc.identifier.scopus2-s2.0-85067423504
unesp.author.lattes4947092280690606[4]
unesp.author.orcid0000-0002-0501-5303[4]
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