Search

Epilepsy affects 1%-2% of the general population, almost 50% of whom are medically refractory and poses a huge burden. A device can possibly be designed for responsive brain stimulation of the epileptogenic focus for seizure control but optimal seizure detection patterns and stimulus parameters are not yet determined. To explore the possibility of identifying a signature prior to the seizure, we studied the dynamic evolution of frequency from electroencephalography (EEG) of three patients with partial epilepsy using the method of frequency flow. We found that prior to seizure, the frequency flow dynamics exhibit a buildup of 5-12 Hz activity which remains high during the initial stage of the seizure with a characteristic time constant and may be used for seizure detection and/or anticipation. We conclude that dynamics of frequency flow in the 5-12 Hz range can be a potential marker for the detection and/or the anticipation of the seizure in temporal lobe epilepsy.

At an organism level, the mammalian circadian pacemaker is a two-dimensional system. For these two dimensions, phase (relative timing) and amplitude of the circadian pacemaker are commonly used. Both the phase and the amplitude (A) of the human circadian pacemaker can be observed within multiple physiological measures--including plasma cortisol, plasma melatonin, and core body temperature (CBT)--all of which are also used as markers of the circadian system. Although most previous work has concentrated on changes in phase of the circadian system, critically timed light exposure can significantly reduce the amplitude of the pacemaker. The rate at which the amplitude recovers to its equilibrium level after reduction can have physiological significance. Two mathematical models that describe the phase and amplitude dynamics of the pacemaker have been reported. These models are essentially equivalent in predictions of phase and in predictions of amplitude recovery for small changes from an equilibrium value (A = 1), but are markedly different in the prediction of recovery rates when A < 0.6. To determine which dynamic model best describes the amplitude recovery observed in experimental data; both models were fit to CBT data using a maximum likelihood procedure and compared using Akaike's Information Criterion (AIC). For all subjects, the model with the lower recovery rate provided a better fit to data in terms of AIC, supporting evidence that the amplitude recovery of the endogenous pacemaker is slow at low amplitudes. Experiments derived from model predictions are proposed to test the influence of low amplitude recovery on the physiological and neurobehavioral functions.

A study of the effect of time scales in brain dynamics on the unfolding of the attractors in the phase space, reconstructed by a time delay embedding of the EEG signal, was carried out. Applying the techniques of nonlinear time series analysis, the unfolding rate of the system attractor was determined by analyzing the variation of the correlation dimension parameter and subjecting it to a bi-parametric fit. The behavior of the parameter, which measures the rate of unfolding, was monitored for varying time scales in two cases: (a) normal eyes closed condition and (b) the pathological case of epilepsy. Significant results were obtained.

We propose to show in this paper, that the time series obtained from biological systems such as human brain are invariably nonstationary because of different time scales involved in the dynamical process. This makes the invariant parameters time dependent. We made a global analysis of the EEG data obtained from the eight locations on the skull space and studied simultaneously the dynamical characteristics from various parts of the brain. We have proved that the dynamical parameters are sensitive to the time scales and hence in the study of brain one must identify all relevant time scales involved in the process to get an insight in the working of brain.

It is shown that variation of Kolmogorov entropy is significantly sensitive to the choice of time lag during epileptic conditions. This clearly indicates that the human brain dynamics consist of different time scales and they are very crucial in the study of various disorders of brain from a nonlinear dynamical point of view.