Kalitzin S.N.,Foundation Epilepsy Institute in the Netherlands SEIN |
Kalitzin S.N.,University Utrecht |
Bauer P.R.,Foundation Epilepsy Institute in the Netherlands SEIN |
Bauer P.R.,University College London |
And 6 more authors.
IFMBE Proceedings | Year: 2016
Rationale. Automated monitoring and alerting for adverse events in patients with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently we explored the relation between clonic slowing at the end of a convulsive seizure and the occurrence and duration of a subsequent period of post-ictal generalized EEG suppression (PGES). We found that prolonged periods of PGES can be predicted by the amount of progressive increase of inter-clonic intervals (ICI) during the seizure. PGES was previously linked to SUDEP The purpose of the present study is to develop an automated, remote video sensing based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES and which may eventually help preventing sudden unexpected death in epilepsy (SUDEP). Methods. The technique is based on our earlier published optical flow video sequence processing paradigm that has been applied for automated detection of major motor seizures. Here we introduce an integral Radon-like transformation on the timefrequency wavelet spectrum in order to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed EEG traces as “gold standard”. We studied 48 cases of convulsive seizures for which synchronized EEG-video recording was available. Results. In most cases the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during seizure (the gold standard). The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. © Springer International Publishing Switzerland 2016.
Kalitzin S.,Foundation Epilepsy Institute in the Netherlands SEIN |
Koppert M.,University of Exeter |
Petkov G.,Foundation Epilepsy Institute in the Netherlands SEIN |
Petkov G.,University of Exeter |
And 2 more authors.
International Journal of Neural Systems | Year: 2014
In our previous studies, we showed that the both realistic and analytical computational models of neural dynamics can display multiple sustained states (attractors) for the same values of model parameters. Some of these states can represent normal activity while other, of oscillatory nature, may represent epileptic types of activity. We also showed that a simplified, analytical model can mimic this type of behavior and can be used instead of the realistic model for large scale simulations. The primary objective of the present work is to further explore the phenomenon of multiple stable states, co-existing in the same operational model, or phase space, in systems consisting of large number of interconnected basic units. As a second goal, we aim to specify the optimal method for state control of the system based on inducing state transitions using appropriate external stimulus. We use here interconnected model units that represent the behavior of neuronal populations as an effective dynamic system. The model unit is an analytical model (S. Kalitzin et al., Epilepsy Behav. 22 (2011) S102-S109) and does not correspond directly to realistic neuronal processes (excitatory-inhibitory synaptic interactions, action potential generation). For certain parameter choices however it displays bistable dynamics imitating the behavior of realistic neural mass models. To analyze the collective behavior of the system we applied phase synchronization analysis (PSA), principal component analysis (PCA) and stability analysis using Lyapunov exponent (LE) estimation. We obtained a large variety of stable states with different dynamic characteristics, oscillatory modes and phase relations between the units. These states can be initiated by appropriate initial conditions; transitions between them can be induced stochastically by fluctuating variables (noise) or by specific inputs. We propose a method for optimal reactive control, allowing forced transitions from one state (attractor) into another. © 2014 World Scientific Publishing Company.