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).
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Acknowledgments
A.S.L.O. Campanharo acknowledges the support of FAPESP: 2013/19905-3. The support of Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) (Brazil) is acknowledged by F.M. R. All figures were generated with PyGrace (http://pygrace.github.io/) with color schemes from Colorbrewer (http://colorbrewer.org).
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Campanharo, A.S.L.O., Doescher, E., Ramos, F.M. (2017). Automated EEG Signals Analysis Using Quantile Graphs. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2017. Lecture Notes in Computer Science(), vol 10306. Springer, Cham. https://doi.org/10.1007/978-3-319-59147-6_9
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DOI: https://doi.org/10.1007/978-3-319-59147-6_9
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