11 Citations (Scopus)

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 fiow 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 fiow 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).

Original languageEnglish (US)
Title of host publicationSpringer Optimization and Its Applications
PublisherSpringer International Publishing
Pages483-502
Number of pages20
Volume7
DOIs
StatePublished - 2007

Publication series

NameSpringer Optimization and Its Applications
Volume7
ISSN (Print)19316828
ISSN (Electronic)19316836

Fingerprint

Information Flow
Coupled System
Nonlinear Systems
Entropy
Surrogate Data
Entropy Method
Epilepsy
Statistical Significance
Interaction
Chaotic System
Linear Systems
Robustness
Series
Brain
Human
Estimate
Demonstrate
Simulation

Keywords

  • Coupled Systems
  • Epilepsy Dynamics
  • Epileptogenic Focus Localization
  • Information Flow
  • Nonlinear Dynamics
  • Transfer Entropy

ASJC Scopus subject areas

  • Control and Optimization

Cite this

Sabesan, S., Krishnamurthi, N., Prasad, A., Iasemidis, L. D., Spanias, A., & Tsakalis, K. (2007). Information flow in coupled nonlinear systems: Application to the epileptic human brain. In Springer Optimization and Its Applications (Vol. 7, pp. 483-502). (Springer Optimization and Its Applications; Vol. 7). Springer International Publishing. https://doi.org/10.1007/978-0-387-69319-4_24

Information flow in coupled nonlinear systems : Application to the epileptic human brain. / Sabesan, S.; Krishnamurthi, Narayanan; Prasad, A.; Iasemidis, L. D.; Spanias, Andreas; Tsakalis, Konstantinos.

Springer Optimization and Its Applications. Vol. 7 Springer International Publishing, 2007. p. 483-502 (Springer Optimization and Its Applications; Vol. 7).

Research output: Chapter in Book/Report/Conference proceedingChapter

Sabesan, S, Krishnamurthi, N, Prasad, A, Iasemidis, LD, Spanias, A & Tsakalis, K 2007, Information flow in coupled nonlinear systems: Application to the epileptic human brain. in Springer Optimization and Its Applications. vol. 7, Springer Optimization and Its Applications, vol. 7, Springer International Publishing, pp. 483-502. https://doi.org/10.1007/978-0-387-69319-4_24
Sabesan S, Krishnamurthi N, Prasad A, Iasemidis LD, Spanias A, Tsakalis K. Information flow in coupled nonlinear systems: Application to the epileptic human brain. In Springer Optimization and Its Applications. Vol. 7. Springer International Publishing. 2007. p. 483-502. (Springer Optimization and Its Applications). https://doi.org/10.1007/978-0-387-69319-4_24
Sabesan, S. ; Krishnamurthi, Narayanan ; Prasad, A. ; Iasemidis, L. D. ; Spanias, Andreas ; Tsakalis, Konstantinos. / Information flow in coupled nonlinear systems : Application to the epileptic human brain. Springer Optimization and Its Applications. Vol. 7 Springer International Publishing, 2007. pp. 483-502 (Springer Optimization and Its Applications).
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