A new chaotic network model for epilepsy

Panahi, S, Shirzadian, T, Jalili, M and Jafari, S 2019, 'A new chaotic network model for epilepsy', Applied Mathematics and Computation, vol. 346, pp. 395-407.

Document type: Journal Article
Collection: Journal Articles

Title A new chaotic network model for epilepsy
Author(s) Panahi, S
Shirzadian, T
Jalili, M
Jafari, S
Year 2019
Journal name Applied Mathematics and Computation
Volume number 346
Start page 395
End page 407
Total pages 13
Publisher Elsevier
Abstract Epilepsy is a prevalent neurological disorder with symptoms characterized by abnormal discharge in the brain. According to the classification of the International League Against Epilepsy (ILAE) Commission, temporal lobe epilepsy is the most common type of epilepsy accounting for the most cases of the disorder observed in patients. Electroencephalography (EEG) is the most common diagnostic tool for Epilepsy, by which abnormal electrical activity of the brain can be clearly seen. This paper uses chaos theory and proposes a new analytical mass model for temporal lobe Epilepsy. Chaotic behavior of the model indicates normal model, while its periodic behavior indicate epileptic mode of the brain. The proposed model includes a number of parameters for which a full bifurcation analysis is conducted. This fully characterizes different regimes of the model and allows studying how one can control the parameters to switch between different modes. The proposed model enables to effectively use advance chaos-based mathematical tools to get further insights on the underlying mechanisms of epilepsy.
Subject Dynamical Systems in Applications
Complex Physical Systems
Keyword(s) Analytical modeling
Chaotic behavior
Neural network
DOI - identifier 10.1016/j.amc.2018.10.061
Copyright notice © 2018 Elsevier Inc.
ISSN 0096-3003
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 3 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 0 times in Scopus Article
Altmetric details:
Access Statistics: 28 Abstract Views  -  Detailed Statistics
Created: Thu, 21 Feb 2019, 12:10:00 EST by Catalyst Administrator
© 2014 RMIT Research Repository • Powered by Fez SoftwareContact us