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Tytuł artykułu

Complexity analysis of spontaneous EEG

Autorzy

Warianty tytułu

Języki publikacji

EN

Abstrakty

EN
The aim of the present paper is the assessment of the overall complexity of spontaneous and non-paroxysmal EEG signals obtained from three groups of human subjects, e.g., healthy, seizure and mania. Linear complexity measure suitable for multi-variate signals, along with nonlinear measures such as approximate entropy (ApEn) and Taken's estimator are considered. The degree of linear complexity is significantly reduced for the pathological groups compared with healthy group. The nonlinear measures of complexity are significantly decreased in the seizure group for most of the electrodes, whereas a distinct discrimination between the maniac and healthy groups based on these nonlinear measures is not evident.

Wydawca

-

Rocznik

Tom

60

Numer

4

Opis fizyczny

p.495-501,fig.

Twórcy

  • Austrian Academy of Sciences, Sonnenfelsgasse 19/2, A-1010 Vienna, Austria

Bibliografia

  • Babloyantz A., Destexhe A. (1986) Low dimensional chaos innan instance of epilepsy. Proc. Natl. Acad. Sci. USA 83: 3513-3517.
  • Diambra L., Bastos de Figueireda J.C., Malta C.P. (1999) Epi­leptic activity recognition in EEG recording. Phisica A 273: 495-505.
  • Freeman W.J. (2000) A proposed name for aperiodic brain ac­tivity: stochastic chaos. Neural Networks 13: 11-13.
  • Glass L., Mackey M.C. (1988) Rhythms of life: from clocks to chaos. Princeton University Press, Princeton, NJ, 248 p.
  • Golub G.H., Van Loan C.F. (1996) The matrix computations. Johns Hopkins University Press, Baltimore, MD, 694 p.
  • Grassberger P. (1989) Information content and predictability of lumped and distributed dynamical systems. Physica Scripta 40:346-353
  • Hegger R., Kantz H, Schreiber T. (1999) Practical implemen­tation of nonlineartime series methods: The TISEAN pack­age. Chaos 9: 413-435.
  • Hjorth B. (1973) The physical significance of the time domain descriptors in EEG analysis. Electroencephalogr. Clin. Neurophysiol. 34: 321-325.
  • Kantz H., Schreiber T. (1997) Nonlinear time series analysis. Cambridge University Press, Cambridge, 320 p.
  • Klonowski W., Jernajczyk W., Niedzielska K, Rydz A., Stepien R. (1999) Quantitative measure of complexity of EEG signal dynamics. Acta. Neurobiol. Exp. 59:315-321.
  • Lehnertz K., Elger C. (1998) Can epileptic seizures be pre­dicted - evidence from nonlinear time series analysis of brain electrical activity. Phys. Rev. Lett. 80: 5019-5022.
  • Morgera S.S. (1985) Information theoretic complexity and its relation to pattern recognition. IEEE. Trans. Syst. Man. Cybernet. 15: 608-619.
  • Niedermeyer E., Lopes da SilvaF.H. (1993) Electroencepha- lography: basic principles, clinical applications, and re­lated fields. Lippincott Williams & Wilkins, Baltimore, MD, 1258 p.
  • Palus M., Dvorak I., David I. (1991) Remarks on spatial and temporal dynamics of EEG. In: Mathematical approaches to brain functioning diagnostics (Eds. I. Dvorak and A.V. Holden). Manchester University Press, Manchester, p. 369-385
  • Pezard L. et al. (1996) Depression as a dynamical disease. Biol. Psychiat. 39: 991-999.
  • Pincus S.M. (1991) Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. USA 88: 2297-2301.
  • Pincus S.M., Goldberger, A.L. (1994) Physiological time-series analysis: what does regularity quantify? Am. J. Physiol. 266: H1643-H1656.
  • Soong A.C.K. Stuart C.I.J.M. (1989) Evidence of chaotic dy­namics underlying the human alpha-rhythm electroen­cephalogram. Biol. Cybernet. 62: 55-62.
  • Szelenberger W., Wackermann J., Skalski M., Niemcewicz S., Drojewski J. (1996) Analysis of complexity of EEG during sleep. Acta Neurobiol. Exp. 56: 165-169.
  • Takens, F. (1985) On the numerical determination of the dimension of an attractor. In: Dynamical systems and bi­furcations (Eds. B.L.J. Braaksma, H.W. Broer and F. Takens). Lect. Notes in Math. 1125, Springer, Heidel­berg.
  • Wackermann J. (1996) Beyond mapping: estimating com­plexity of multichannel EEG recordings. Acta Neurobiol. Exp. 56: 197-208.
  • Wackermann J. (1999) Towards a quantitative characteriza­tion of functional states of the brain: from the non-linear methodology to the global linear description. Int. J. Psychphysiol. 34: 65-80.
  • Wackerbauer R., Witt A., Atmanspacher H., Kurths H., Scheingraber H. (1994) A comparative classification of complexity measures. Chaos Solit. Fract. 4: 133-173.
  • Weber B., Lehnertz K., Elger C.E., Wieser, H.G. (1998) Neuronal complexity loss in interictal EEG recorded with foreman ovale electrodes predicts side of primary epileptogenic area in temporal lobe epilepsy: a replication study. Epilepsia 39: 922-927.

Typ dokumentu

Bibliografia

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