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Automatic detection of the epileptogenic zone from EEG signals with the use of complex networks

Grant number: 19/07469-0
Support type:Scholarships abroad - Research Internship - Master's degree
Effective date (Start): July 01, 2019
Effective date (End): October 31, 2019
Field of knowledge:Engineering - Biomedical Engineering
Principal researcher:Andriana Susana Lopes de Oliveira Campanharo
Grantee:Gustavo Henrique Tomanik
Supervisor abroad: Ernesto Estrada Roger
Home Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
Research place: Universidad de Zaragoza, Spain  
Associated to the scholarship:18/02014-2 - The use of complex networks in the seizures prediction and in the Epileptogenic Zone location of epileptic patients, BP.MS


Epilepsy is a brain disorder predominantly characterized by recurrent and unpredictable interruptions of the normal brain function, called as epileptic seizures. Epileptic seizures are episodes that can vary from brief and nearly undetectable to long periods of vigorous shaking. Such episodes can have a significant impact on the daily life of sufferers and are poorly controlled in more than 30% of epilepsy patients. Therefore, medical intervention is often required and the alternative treatment is the epilepsy surgery for the epileptogenic zone removal, which is the neuronal region of the brain responsible for generating seizures. Electroencephalogram (EEG) technique is one of the most important diagnostic test for investigation of the neuronal activity of patients with seizures disorders and epilepsies. In order to locate the epileptogenic zone, non-invasive techniques have been proposed in the literature, but in most cases the results remain non convergent and/or inconclusive. In the last two decades, research on complex networks became the focus of widespread attention, with developments and applications spanning different scientific areas, from sociology and biology to physics. Recently, Campanharo et al. proposed a map from a time series into a network and previous works have shown that the complex networks inherit some of the corresponding time series properties. In this research project, it is proposed a non-invasive EEG data analysis technique based on this map for the epileptogenic zone detection in epileptic patients. (AU)

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