1,794 research outputs found
Mapping the epileptic brain with EEG dynamical connectivity: established methods and novel approaches
Several algorithms rooted in statistical physics, mathematics and machine learning are used to analyze neuroimaging data from patients suffering from epilepsy, with the main goals of localizing the brain region where the seizure originates from and of detecting upcoming seizure activity in order to trigger therapeutic neurostimulation devices. Some of these methods explore the dynamical connections between brain regions, exploiting the high temporal resolution of the electroencephalographic signals recorded at the scalp or directly from the cortical surface or in deeper brain areas. In this paper we describe this specific class of algorithms and their clinical application, by reviewing the state of the art and reporting their application on EEG data from an epileptic patient
Internetwork and intranetwork communications during bursting dynamics: Applications to seizure prediction
We use a simple dynamical model of two interacting networks of integrate-and-fire neurons to explain a seemingly paradoxical result observed in epileptic patients indicating that the level of phase synchrony declines below normal levels during the state preceding seizures (preictal state). We model the transition from the seizure free interval (interictal state) to the seizure (ictal state) as a slow increase in the mean depolarization of neurons in a network corresponding to the epileptic focus. We show that the transition from the interictal to preictal and then to the ictal state may be divided into separate dynamical regimes: the formation of slow oscillatory activity due to resonance between the two interacting networks observed during the interictal period, structureless activity during the preictal period when the two networks have different properties, and bursting dynamics driven by the network corresponding to the epileptic focus. Based on this result, we hypothesize that the beginning of the preictal period marks the beginning of the transition of the epileptic network from normal activity toward seizing
Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient
Background: Investigation of the functioning of the brain in living systems
has been a major effort amongst scientists and medical practitioners. Amongst
the various disorder of the brain, epilepsy has drawn the most attention
because this disorder can affect the quality of life of a person. In this paper
we have reinvestigated the EEGs for normal and epileptic patients using
surrogate analysis, probability distribution function and Hurst exponent.
Results: Using random shuffled surrogate analysis, we have obtained some of
the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev
E 2001, 64:061907], for the epileptic patients during seizure. Probability
distribution function shows that the activity of an epileptic brain is
nongaussian in nature. Hurst exponent has been shown to be useful to
characterize a normal and an epileptic brain and it shows that the epileptic
brain is long term anticorrelated whereas, the normal brain is more or less
stochastic. Among all the techniques, used here, Hurst exponent is found very
useful for characterization different cases.
Conclusions: In this article, differences in characteristics for normal
subjects with eyes open and closed, epileptic subjects during seizure and
seizure free intervals have been shown mainly using Hurst exponent. The H shows
that the brain activity of a normal man is uncorrelated in nature whereas,
epileptic brain activity shows long range anticorrelation.Comment: Keywords:EEG, epilepsy, Correlation dimension, Surrogate analysis,
Hurst exponent. 9 page
Seizure characterisation using frequency-dependent multivariate dynamics
The characterisation of epileptic seizures assists in the design of targeted pharmaceutical seizure prevention techniques
and pre-surgical evaluations. In this paper, we expand on recent use of multivariate techniques to study the crosscorrelation
dynamics between electroencephalographic (EEG) channels. The Maximum Overlap Discrete Wavelet
Transform (MODWT) is applied in order to separate the EEG channels into their underlying frequencies. The
dynamics of the cross-correlation matrix between channels, at each frequency, are then analysed in terms of the
eigenspectrum. By examination of the eigenspectrum, we show that it is possible to identify frequency dependent
changes in the correlation structure between channels which may be indicative of seizure activity.
The technique is applied to EEG epileptiform data and the results indicate that the correlation dynamics vary over
time and frequency, with larger correlations between channels at high frequencies. Additionally, a redistribution of wavelet energy is found, with increased fractional energy demonstrating the relative importance of high frequencies
during seizures. Dynamical changes also occur in both correlation and energy at lower frequencies during seizures,
suggesting that monitoring frequency dependent correlation structure can characterise changes in EEG signals during
these. Future work will involve the study of other large eigenvalues and inter-frequency correlations to determine
additional seizure characteristics
Delay Differential Analysis of Seizures in Multichannel Electrocorticography Data
High-density electrocorticogram (ECoG) electrodes are capable of recording neurophysiological data with high temporal resolution with wide spatial coverage. These recordings are a window to understanding how the human brain processes information and subsequently behaves in healthy and pathologic states. Here, we describe and implement delay differential analysis (DDA) for the characterization of ECoG data obtained from human patients with intractable epilepsy. DDA is a time-domain analysis framework based on embedding theory in nonlinear dynamics that reveals the nonlinear invariant properties of an unknown dynamical system. The DDA embedding serves as a low-dimensional nonlinear dynamical basis onto which the data are mapped. This greatly reduces the risk of overfitting and improves the method's ability to fit classes of data. Since the basis is built on the dynamical structure of the data, preprocessing of the data (e.g., filtering) is not necessary. We performed a large-scale search for a DDA model that best fit ECoG recordings using a genetic algorithm to qualitatively discriminate between different cortical states and epileptic events for a set of 13 patients. A single DDA model with only three polynomial terms was identified. Singular value decomposition across the feature space of the model revealed both global and local dynamics that could differentiate electrographic and electroclinical seizures and provided insights into highly localized seizure onsets and diffuse seizure terminations. Other common ECoG features such as interictal periods, artifacts, and exogenous stimuli were also analyzed with DDA. This novel framework for signal processing of seizure information demonstrates an ability to reveal unique characteristics of the underlying dynamics of the seizure and may be useful in better understanding, detecting, and maybe even predicting seizures
Quantitative analysis by renormalized entropy of invasive electroencephalograph recordings in focal epilepsy
Invasive electroencephalograph (EEG) recordings of ten patients suffering
from focal epilepsy were analyzed using the method of renormalized entropy.
Introduced as a complexity measure for the different regimes of a dynamical
system, the feature was tested here for its spatio-temporal behavior in
epileptic seizures. In all patients a decrease of renormalized entropy within
the ictal phase of seizure was found. Furthermore, the strength of this
decrease is monotonically related to the distance of the recording location to
the focus. The results suggest that the method of renormalized entropy is a
useful procedure for clinical applications like seizure detection and
localization of epileptic foci.Comment: 10 pages, 5 figure
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