@Article{CapanharoDoesRamo:2020:ApQuGr,
               author = "Capanharo, Andriana Susana Lopes de Oliveira and Doescher, Erwin 
                         and Ramos, Fernando Manuel",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                title = "Application of quantile graphs to the automated analysis of EEG 
                         signals",
              journal = "Neural Processing Letters",
                 year = "2020",
               volume = "52",
               number = "1",
                pages = "5--20",
                month = "Aug.",
             keywords = "Electroencephalographic time series, Epilepsy, Complex networks, 
                         Quantile graphs, Network measures.",
             abstract = "Epilepsy is classified as a chronic neurological disorder of the 
                         brain and affects approximately 2% of the world population. This 
                         disorder leads to a reduction in people's productivity and imposes 
                         restrictions on their daily lives. Studies of epilepsy often rely 
                         on electroencephalogram (EEG) signals to provide information on 
                         the behavior of the brain during seizures. 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 
                         normal, pre-ictal (preceding a seizure), and ictal (occurring 
                         during a seizure) conditions 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 shortest path length, the mean jump length, the 
                         modularity and the betweenness centrality, our results show that 
                         the QG method is able to detect differences in dynamical 
                         properties of brain electrical activity from different 
                         extracranial and intracranial recording regions and from different 
                         physiological and pathological brain states.",
                  doi = "10.1007/s11063-018-9936-z",
                  url = "http://dx.doi.org/10.1007/s11063-018-9936-z",
                 issn = "1370-4621",
             language = "en",
           targetfile = "Campanharo2020_Article_ApplicationOfQuantileGraphsToT.pdf",
        urlaccessdate = "27 abr. 2024"
}