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

• Grant number: 17/09216-7
• Support Opportunities: Scholarships abroad - Research Internship - Scientific Initiation
• Start date: July 01, 2017
• End date: August 31, 2017
• Field of knowledge: Engineering - Biomedical Engineering
• Principal Investigator: Andriana Susana Lopes de Oliveira Campanharo
• Grantee: Gustavo Henrique Tomanik
• Supervisor: Luis Nunes Amaral
• Host Institution: Instituto de Biociências (IBB). Universidade Estadual Paulista (UNESP). Campus de Botucatu. Botucatu , SP, Brazil
• Institution abroad: 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)

Scientific publications

(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)

TOMANIK, GUSTAVO H.; BETTING, LUIZ E.; CAMPANHARO, ANDRIANA S. L. O.; ROJAS, I; JOYA, G; CATALA, A. Automatic Identification of Interictal Epileptiform Discharges with the Use of Complex Networks. ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT I, v. 11506, p. 10-pg., 2019-01-01. (17/09216-7, 16/17914-3, 18/25358-9, 18/02014-2)