@InCollection{CampanharoDoesRamo:2017:AuEESi,
               author = "Campanharo, Adriana Susana Lopes de Oliveira and Doescher, E. and 
                         Ramos, Fernando Manuel",
                title = "Automated EEG signals analysis using quantile graphs",
            booktitle = "Advances in computational intelligence: 14th International 
                         Work-Conference on Artificial Neural Networks, IWANN 2017 Cadiz, 
                         Spain, June 14–16, 2017 Proceedings, Part II",
            publisher = "Springer",
                 year = "2017",
               editor = "Rojas, Ignacio Rojas and Joya, Gonzalo and Catala, Andreu",
                pages = "95--103",
             keywords = "Electroencephalographic time series, Epilepsy, Complex networks, 
                         Quantile graphs.",
             abstract = "Recently, a map from time series to networks has been proposed [7, 
                         8], allowing the use of network statistics to characterize time 
                         series. In this approach, time series quantiles are naturally 
                         mapped into nodes of a graph. Networks generated by this method, 
                         called Quantile Graphs (QGs), are able to capture and quantify 
                         features such as long-range correlations or randomness present in 
                         the underlying dynamics of the original signal. Here we apply the 
                         QG method to the problem of detecting the differences between 
                         electroencephalographic time series (EEG) of healthy and unhealthy 
                         subjects. Our main goal is to illustrate how the differences in 
                         dynamics are reflected in the topology of the corresponding QGs. 
                         Results show that the QG method cannot only differentiate 
                         epileptic from normal data, but also distinguish the different 
                         abnormal stages/patterns of a seizure, such as pre-ictal (EEG 
                         changes preceding a seizure) and ictal (EEG changes during a 
                         seizure).",
          affiliation = "{Universidade Estadual Paulista (UNESP)} and {Universidade Federal 
                         de S{\~a}o Paulo (UNIFESP)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)}",
                 isbn = "978-3-319-59146-9",
             language = "en",
          seriestitle = "Lecture Notes in Computer Science, 10306",
        urlaccessdate = "27 abr. 2024"
}