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@InCollection{PinedaRamoBettCamp:2019:UsCoNe,
               author = "Pineda, Aruane Mello and Ramos, Fernando Manoel and Betting, Luiz 
                         Eduardo and Campanharo, Andriana S. L. O.",
                title = "Use of complex networks for the automatic detection and the 
                         diagnosis of Alzheimer’s disease",
            booktitle = "Advances in Computational Intelligence",
            publisher = "Springer Verlag",
                 year = "2019",
               editor = "Rojas, Ignacio and Joya, Gonzalo and Catala, Andreu",
                pages = "115--126",
             keywords = "complex networks, Alzheimer disease.",
             abstract = "Alzheimers 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 
                         peoples 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 Alzheimers disease.",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Instituto Nacional 
                         de Pesquisas Espaciais (INPE)} and {Universidade Estadual Paulista 
                         (UNESP)} and {Universidade Estadual Paulista (UNESP)}",
                  doi = "10.1007/978-3-030-20521-8_10",
                  url = "http://dx.doi.org/10.1007/978-3-030-20521-8_10",
                 isbn = "978-303020520-1",
             language = "en",
           targetfile = "10.1007@978-3-030-20521-810.pdf",
        urlaccessdate = "30 nov. 2020"
}


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