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, J. Chris Sackellares

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)276-286
Number of pages11
JournalJournal of Combinatorial Optimization
Volume15
Issue number3
DOIs
StatePublished - Apr 2008
Externally publishedYes

Fingerprint

Epilepsy
Complexity Analysis
Electroencephalography
Quantitative Analysis
Brain
Neurons
Neuron
Impulse
Active networks
Neuronal Network
Action Potential
Synapse
Bioelectric potentials
Progression
Disorder
Time series
Disturbance
Classify
Voltage
Electroencephalogram

Keywords

  • Complexity analysis
  • Dynamical system
  • Electroencephalogram (EEG)
  • Minimum embedding dimension

ASJC Scopus subject areas

  • Computer Science Applications
  • Computational Theory and Mathematics
  • Mathematics(all)
  • Control and Optimization
  • Applied Mathematics
  • Discrete Mathematics and Combinatorics

Cite this

Liu, C. C., Pardalos, P. M., Chaovalitwongse, W. A., Shiau, D. S., Ghacibeh, G., Suharitdamrong, W., & Sackellares, J. C. (2008). Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains. Journal of Combinatorial Optimization, 15(3), 276-286. https://doi.org/10.1007/s10878-007-9118-9

Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains. / Liu, Chang Chia; Pardalos, Panos M.; Chaovalitwongse, W. Art; Shiau, Deng Shan; Ghacibeh, Georges; Suharitdamrong, Wichai; Sackellares, J. Chris.

In: Journal of Combinatorial Optimization, Vol. 15, No. 3, 04.2008, p. 276-286.

Research output: Contribution to journalArticle

Liu, CC, Pardalos, PM, Chaovalitwongse, WA, Shiau, DS, Ghacibeh, G, Suharitdamrong, W & Sackellares, JC 2008, 'Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains', Journal of Combinatorial Optimization, vol. 15, no. 3, pp. 276-286. https://doi.org/10.1007/s10878-007-9118-9
Liu, Chang Chia ; Pardalos, Panos M. ; Chaovalitwongse, W. Art ; Shiau, Deng Shan ; Ghacibeh, Georges ; Suharitdamrong, Wichai ; Sackellares, J. Chris. / Quantitative complexity analysis in multi-channel intracranial EEG recordings form epilepsy brains. In: Journal of Combinatorial Optimization. 2008 ; Vol. 15, No. 3. pp. 276-286.
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