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Predicting epileptic seizures in EEG recordings with the use of complex networks

Grant number: 17/09216-7
Support type:Scholarships abroad - Research Internship - Scientific Initiation
Effective date (Start): July 01, 2017
Effective date (End): August 31, 2017
Field of knowledge:Engineering - Biomedical Engineering
Principal researcher:Andriana Susana Lopes de Oliveira Campanharo
Grantee:Gustavo Henrique Tomanik
Supervisor abroad: Luis Nunes Amaral
Home Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
Research place: Northwestern University, Evanston, United States  
Associated to the scholarship:15/22293-5 - Characterization and analysis of physiological time series through a complex network approach, BP.IC

Abstract

Epilepsy is a neurological disorder characterized by the presence of recurring seizures that affects nearly 1% of the general population. Sudden and abrupt seizures that cause momentarily lapses of consciousness can have significant impact on the daily life of sufferers. Thus, epileptic seizure detection would help these people to have a normal life. Recently, a map from time series to networks has been proposed, allowing the use of network statistics to characterize time series. In this approach, time series quantiles are naturally mapped into nodes of a Quantile Graph (QG). In this research project we want to apply the QG method to the problem of detecting the differences between electroencephalographic time series (EEG) ofhealthy and unhealthy subjects. Our main goal is to find out if the proposed method can be useful in the epileptic seizure detection challenge. Moreover, we want to investigate if the QG method can 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). (AU)

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