Title :
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
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;
Conference_Titel :
Engineering in Medicine and Biology Society, 2001. Proceedings of the 23rd Annual International Conference of the IEEE
Print_ISBN :
0-7803-7211-5
DOI :
10.1109/IEMBS.2001.1020602
