Information Flow in Coupled Nonlinear Systems: Application to the Epileptic Human Brain

S. Sabesan, K. Narayanan, A. Prasad, L. D. Iasemidis, A. Spanias and K. Tsakalis
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S. Sabesan: Arizona State University
K. Narayanan: Arizona State University
A. Prasad: University of Delhi
L. D. Iasemidis: Arizona State University
A. Spanias: Arizona State University
K. Tsakalis: Arizona State University

A chapter in Data Mining in Biomedicine, 2007, pp 483-503 from Springer

Abstract: Abstract A recently proposed measure, namely Transfer Entropy (TE), is used to estimate the direction of information flow between coupled linear and nonlinear systems. In this study, we suggest improvements in the selection of parameters for the estimation of TE that significantly enhance its accuracy and robustness in identifying the direction of information flow and quantifying the level of interaction between observed data series from coupled systems. We demonstrate the potential usefulness of the improved method through simulation examples with coupled nonlinear chaotic systems. The statistical significance of the results is shown through the use of surrogate data. The improved TE method is then used for the study of information flow in the epileptic human brain. We illustrate the application of TE to electroencephalographic (EEG) signals for the study of localization of the epileptogenic focus and the dynamics of its interaction with other brain sites in two patients with Temporal Lobe Epilepsy (TLE).

Keywords: Nonlinear Dynamics; Coupled Systems; Transfer Entropy; Information Flow; Epilepsy Dynamics; Epileptogenic Focus Localization (search for similar items in EconPapers)
Date: 2007
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DOI: 10.1007/978-0-387-69319-4_24

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