Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains |
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Authors: | Chang-Chia Liu Panos M Pardalos W Art Chaovalitwongse Deng-Shan Shiau Georges Ghacibeh Wichai Suharitdamrong J Chris Sackellares |
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Institution: | (1) Department of Industrial and Systems Engineering, Biomedical Engineering, University of Florida, 303 Weil Hall, P.O. Box 116595, Gainesville, FL 32611-6595, USA;(2) Department of Industrial and Systems Engineering, Rutgers University, 96 Frelinghuysen Rd., Piscataway, NJ 08854, USA;(3) Optima Neuroscience, Inc., Downtown Technology Center, 101 SE 2nd Place, Suite 201-A, Gainesville, FL 32601, USA;(4) Northeast Regional Epilepsy Group, 20 Prospect Ave., Suite 800, Hackensack, NJ 07601, USA;(5) Department of Industrial and Systems Engineering, University of Florida, 303 Weil Hall, P.O. Box 116595, Gainesville, FL 32611-6595, USA |
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Abstract: | Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or
may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than
10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via
synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks
are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings
represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we
sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization
of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties
of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging
to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical
measurements appear to reflect the changes of EEG’s dynamical structure. We suggest that the nonlinear dynamical analysis
can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional
linear analysis. |
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Keywords: | Dynamical system Complexity analysis Electroencephalogram (EEG) Minimum embedding dimension |
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