Original article: Bifurcation analysis of the Poincaré map function of intracranial EEG signals in temporal lobe epilepsy patients

Authors:
Mahmood Amiri;Esmaeil Davoodi-Bojd;Fariba Bahrami;Mohsin Raza
Affiliations:
School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IR Iran;School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IR Iran;CIPCE, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, IR Iran;Section of Neurosciences and Ethics, Chemical Injuries Research Centre, Baqiyatallah University of Medical Sciences, Tehran, IR Iran
Venue:
Mathematics and Computers in Simulation
Year:
2011

Citing 4
Cited 1

Analysis of the dynamical behavior of a feedback auto-associative memory

Neurocomputing
BAM learning of nonlinearly separable tasks by using an asymmetrical output function and reinforcement learning

IEEE Transactions on Neural Networks
Feedback associative memory based on a new hybrid model of generalized regression and self-feedback neural networks

Neural Networks
Functional modeling of astrocytes in epilepsy: a feedback system perspective

Neural Computing and Applications - Special Issue: ISNN 2010

Modified thalamocortical model: A step towards more understanding of the functional contribution of astrocytes to epilepsy

Journal of Computational Neuroscience

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, the Poincare map function as a one-dimensional first-return map is obtained by approximating the scatter plots of inter-peak interval (IPI) during preictal and postictal periods from invasive EEG recordings of nine patients suffering from medically intractable focal epilepsy. Evolutionary Algorithm (EA) is utilized for parameter estimation of the Poincare map. Bifurcation analyses of the iterated map reveal that as the neuronal activity progresses from preictal state toward the ictal event, the parameter values of the Poincare map move toward the bifurcation points. However, following the seizure occurrence and in the postictal period, these parameter values move away from the bifurcation points. Both flip and fold bifurcations are analyzed and it is demonstrated that in some cases the flip bifurcation and in other cases the fold bifurcation are the dynamical regime underlying epileptiform events. This information can offer insights into the dynamical nature and variability of the brain signals and consequently could help to predict and control seizure events.