Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains

Chang-Chia Liu (), Panos M. Pardalos (), W. Art Chaovalitwongse (), Deng-Shan Shiau (), Georges Ghacibeh (), Wichai Suharitdamrong () and J. Chris Sackellares ()
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Chang-Chia Liu: University of Florida
Panos M. Pardalos: University of Florida
W. Art Chaovalitwongse: Rutgers University
Deng-Shan Shiau: Downtown Technology Center
Georges Ghacibeh: Northeast Regional Epilepsy Group
Wichai Suharitdamrong: University of Florida
J. Chris Sackellares: Downtown Technology Center

Journal of Combinatorial Optimization, 2008, vol. 15, issue 3, No 5, 276-286

Abstract: 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.

Keywords: Dynamical system; Complexity analysis; Electroencephalogram (EEG); Minimum embedding dimension (search for similar items in EconPapers)
Date: 2008
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DOI: 10.1007/s10878-007-9118-9

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