@Article{PinedaRamoBettCamp:2020:QuGrEE,
               author = "Pineda, Aruane Mello and Ramos, Fernando Manuel and Betting, Luiz 
                         Eduardo Gomes Garcia and Campanharo, Andriana Susana Lopes de 
                         Oliveira",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade Estadual Paulista (UNESP)}",
                title = "Quantile graphs for EEG-based diagnosis of Alzheimer’s disease",
              journal = "PLoS One",
                 year = "2020",
               volume = "15",
               number = "6",
                pages = "e0231169",
                month = "June",
             abstract = "Known as a degenerative and progressive dementia, Alzheimers 
                         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 
                         hereclustering coefficient, mean jump length, betweenness 
                         centrality, modularity, and Laplacian Estrada indexshowed 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.",
                  doi = "10.1371/journal.pone.0231169",
                  url = "http://dx.doi.org/10.1371/journal.pone.0231169",
                 issn = "1932-6203",
             language = "en",
           targetfile = "braga_mudancas.pdf",
        urlaccessdate = "28 abr. 2024"
}