Abstract
Symbolic entropy is proposed to measure the complexity of the electroencephalogram (EEG) signal under different brain functional states. The EEG data recorded from different subjects were investigated and compared with both approximate entropy (ApEn) and Shannon entropy. The experimental results show that the proposed method can effectively distinguish the complexities of two groups. The experimental results provide preliminary support for the notion that the complex nonlinear nature of brain electrical activity may be the result of isolation or impairment of the neural information transmission within the brain. It is concluded that symbolic entropy serves a better measure for EEG signals and other medical signals.
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References
Freeman, W.J.: Simulation of Chaotic EEG Patterns with a Dynamic Model of the Olfactory System. Biological Cybernetics 56, 139–150 (1987)
Freeman, W.J.: Tutorial on Neurobiology: Form Single Neurons to Brain Chaos. Int. J. Bifurcation and Chaos 2, 451–482 (1992)
Zhang, T., Turner, D.L.: A Visuomotor Reaction Time Task Increases the Irregularity and Complexity of Inspiratory Airflow Pattern in Man. Neuroscience Lett. 297, 41–44 (2001)
Gallez, D.: Predictability of Human EEG: a Dynamical Approach. Biological Cybernetics 64, 381–391 (1991)
Roschke, J.: The Dimensionality of Human’s Electroencephalogram during Sleep. Biological Cybernetics 64(91), 307–313
Werner, L.: Dimensional Analysis of the Human EEG and Intelligence. Neuroscience Letters 143, 10–14 (1992)
Zhang, T., Johns, E.J.: Chaotic Characteristics of Renal Nerve Peak Interval Sequence in Norm Intensive and Hypertensive Rats. Clin. Exp. Pharmacol Physiol. 25, 896–903 (1998)
Pincus, S.M.: Approximate Entropy as a Measure of System Complexity. Proc. Natl. Acad. Sci. 88, 2297–2301 (1991)
Pincus, S.M.: Quantification of Evolution from Order to Randomness in Practical Time Series Analysis. Methods Enzymol 240, 68–89 (1994)
Pincus, S.M.: Approximate Entropy (ApEn) as a Complexity Measure. Chaos 5, 110–117 (1995)
Joydeep, B.: Complexity Analysis of Spontaneous EEG. Acta Neurobiol. Exp. 60, 495–501 (2000)
Cysarz, D., et al.: Irregularities and Nonlinearities in Fetal Heart Period Time Series in the Course of Pregnancy. Herzschr Elektrophys 11, 179–183 (2000)
Cysarz, D., et al.: Entropies of Short Binary Sequences in Heart Period Dynamics. Am. J. Physiol. Heart Circ. Physiol. 278, H2163–H2172 (2000)
Alligood, K.T., Sauer, T.D., Yorke, J.A.: Chaos-an Introduction to Dynamical Systems. Springer, Heidelberg (1996)
Francis, C.M.L., Chi, K.T.: Co-Existence of Chaos-Based Communication Systems and Conventional Spread-Spectrum, Communication Systems. In: International Symposium on Nonlinear Theory and its Applications, vol. 7, pp. 107–110 (2002)
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Sun, L., Yu, J., Beadle, P.J. (2009). A Novel Method for Analyzing Dynamic Complexity of EEG Signals Using Symbolic Entropy Measurement. In: Yu, W., He, H., Zhang, N. (eds) Advances in Neural Networks – ISNN 2009. ISNN 2009. Lecture Notes in Computer Science, vol 5553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01513-7_58
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DOI: https://doi.org/10.1007/978-3-642-01513-7_58
Publisher Name: Springer, Berlin, Heidelberg
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