Epileptic seizure prediction and control

Abstract

Epileptic seizures are manifestations of epilepsy, a serious brain dynamical disorder second only to strokes. Of the world's approximately 50 million people with epilepsy, fully 1/3 have seizures that are not controlled by anti-convulsant medication. The field of seizure prediction, in which engineering technologies are used to decode brain signals and search for precursors of impending epileptic seizures, holds great promise to elucidate the dynamical mechanisms underlying the disorder, as well as to enable implantable devices to intervene in time to treat epilepsy. There is currently an explosion of interest in this field in academic centers and medical industry with clinical trials underway to test potential prediction and intervention methodology and devices for Food and Drug Administration (FDA) approval. This invited paper presents an overview of the application of signal processing methodologies based upon the theory of nonlinear dynamics to the problem of seizure prediction. Broader application of these developments to a variety of systems requiring monitoring, forecasting and control is a natural outgrowth of this field.

Amplitude death: The emergence of stationarity in coupled nonlinear systems

“…In many practical situations AD, is desirable, as for example in laser applications [16,17,18,19,20,21] where a constant output is needed and fluctuations should be suppressed. There are also other situations where oscillations need to be maintained, as for instance in brain functioning [22,23]. These different requirements suggest that control strategies to either to achieve or to avoid AD in coupled systems are necessary, and in Sec.…”

Detection and Prediction of Absence Seizures Based on Nonlinear Analysis of the EEG in Wag/Rij Animal Model

“…Signal analysis and its trajectory in the phase space can lead to a better understanding of the system's dynamics and provide valuable information about attractors and system behavior. In particular, nonlinear time series analysis methods are presented to identify epileptic seizure states 16,[21][22][23][24][25][26] . To a certain extent, these methods mainly include the Lyapunov exponent and the correlation dimension, which is able to extract the properties of the useful EEG data to provide evidence to confirm the existence of a previous state of seizure in temporal lobe epilepsy [22][23][24][25] .…”

“…16,[21][22][23][24][25][26] To a certain extent, these methods mainly include the Lyapunov exponent and the correlation dimension, which is able to extract the properties of the useful EEG data to provide evidence to confirm the existence of a previous state of seizure in temporal lobe epilepsy. [21][22][23][24] Ouyang et al used recurrent quantification analysis to distinguish between states in GEARS genetic model of absence epilepsy and show that certainty in seizure periods is higher than in two other states. 27 Multiscale permutation entropy (MPE) was used to describe the dynamic properties of EEG on various human absence epilepsy and the ability to classify MPE by linear discrimination analysis (LDA) was evaluated.…”

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

“…However, a robust definition of such a state and with it, its possibility to predict an impeding seizure, is a much harder mathematical problem. For the time being, one can assume the existence of a preictal state based on recent investigations that have found physiological and clinical support for the idea that certain types of seizures are predictable, [44,45,46,47,48,49]. Functional mri based evidence of transitional states preceding seizures [8] and interictal spikes [50] can also lend support to this hypothesis.…”