Nonadditive information theory for the analysis of brain rhythms

Author

Bezerianos, A. ; Tong, S. ; Zhu, Y. ; Thakor, N.

Author_Institution

Dept. of Biomed. Eng., Johns Hopkins Univ. Sch. of Med., Baltimore, MD, USA

Volume

2

fYear

2001

fDate

2001

Firstpage

1923

Abstract

In this paper, we introduce Nonadditive Information Theory through the axiomatic formulation of Tsallis entropy. We show that systems with transitions from high dimensionality to few degrees of freedom are better described by nonadditive formalism. Such a biological system is the brain and brain rhythms is its macroscopic dynamic trace. We will show with simulations that Tsallis entropy is a powerful information measure, and we present results of brain dynamics analyzed using EEG recordings from a brain injury model.

Keywords

electroencephalography; entropy; medical signal processing; statistical mechanics; Boltzmann-Gibbs statistical mechanics; EEG recordings; Shannon entropy; Tsallis entropy; axiomatic formulation; brain injury model; brain rhythms; few degrees of freedom; high dimensionality; macroscopic dynamic trace; nonadditive information theory; pseudoadditivity; Analytical models; Biological system modeling; Biological systems; Brain injuries; Brain modeling; Electroencephalography; Entropy; Information analysis; Information theory; Rhythm;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE

ISSN

1094-687X

Print_ISBN

0-7803-7211-5

Type

conf

DOI

10.1109/IEMBS.2001.1020602

Filename

1020602