 A new chaotic network model for epilepsyShirin Panahi, Touraj Shirzadian, Mahdi Jalili and Sajad Jafari Applied Mathematics and Computation, 2019, vol. 346, issue C, 395-407 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. Keywords: Analytical modeling; Epilepsy; Seizure; Chaotic behavior; Neural network (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:346:y:2019:i:c:p:395-407 DOI: 10.1016/j.amc.2018.10.061 Access Statistics for this article Applied Mathematics and Computation is currently edited by Theodore Simos More articles in Applied Mathematics and Computation from Elsevier |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.