Synchronization structure of evolving epileptic networks using cross-entropy
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In this paper we present connectivity patterns of evolving large scale epileptic networks. We employed a cross-entropy measure in the frequency domain on EEG signals to infer the networks, before and during episodes of epileptic seizures. This measure allowed us to make a richer portrait about the node interactions on the graph and to identify emergent structures associated with the synchronization of brain activity. Our results points to a more complex scenario of network organization than the synchronized/unsynchronized dichotomy, with two main results: first, showing regions with unsynchronized (or independent) behavior, even during absence seizures, contradicting the concept of hypersynchrony. Furthermore, we explore the cross-entropy fluctuations along the seizure: a group of nodes became more similar over time while another group became more different, showing a complementary behaviour and different local brain activities. These results bring new questions about the spreading and the sustenance of the epileptic seizures and others synchronization phenomena in living systems.
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- 3.A. Sanz-García, R.G. de Sola, L. Vega-Zelaya, J. Pastor, G.J. Ortega, Network theoretical approach to describe epileptic processes, in Advanced Biosignal Processing and Diagnostic Methods, edited by C. Hintermüller (InTech, Viena, 2016) Google Scholar