Statistical measures derived from the correlation integrals of physiological time series

Chaos
D. K. IvanovCh. Stumpf

Abstract

In this work correlation integrals are used for the analysis of various EEG signals from rabbits in resting states and under the influence of an anesthetic. The comparison with surrogate data reveals nonlinear dynamics in all of the time series. Our attempt to determine the correlation dimension D(2) by the modified algorithm of Theiler [Phys. Rev. A 34, 2427 (1986)] failed since no saturation is reached with increasing embedding dimension. The hypothesis of low-dimensional chaos turns out to be inconsistent with our results, but we can still distinguish, at least qualitatively, between different states of brain dynamics. A quantitative characterization of the time series is possible by defining correlation parameters P(a) derived from correlation integrals reflecting also autocorrelation of the signal. (c) 1996 American Institute of Physics.

References

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Citations

Jun 5, 2003·Chaos·Jonathan B. Dingwell, Joseph P. Cusumano
Aug 31, 2001·Journal of Clinical Neurophysiology : Official Publication of the American Electroencephalographic Society·V A ProtopopescuP C Gailey
Jun 5, 2003·Chaos·L. M. HivelyP. C. Gailey
Jul 27, 2010·Neuroscience Research·Alexander A Fingelkurts, Andrew A Fingelkurts
Feb 27, 2007·International Journal of Psychophysiology : Official Journal of the International Organization of Psychophysiology·Alexander A FingelkurtsTaina Autti
Aug 8, 2006·Progress in Neuro-psychopharmacology & Biological Psychiatry·Alexander A FingelkurtsSeppo Kähkönen
Feb 2, 2006·Clinical Neurophysiology : Official Journal of the International Federation of Clinical Neurophysiology·Alexander A FingelkurtsAlexander Ya Kaplan
Apr 24, 2002·Physical Review. E, Statistical Physics, Plasmas, Fluids, and Related Interdisciplinary Topics·D Kugiumtzis
Apr 12, 2006·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·C J Keylock
Mar 23, 2002·Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics·Ying-Cheng LaiMark G Frei

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