Accession number:20094712473394
Title: Nonlinear dynamic indications in time series of epilepsy electroencephalogram
Authors: Chen, Ta-Cheng (1); Wang, Jing-Doo (2); Shieh, Jiunn-I. (3);
Chang, Pei-Chun (1); Lee, Kuei-Jen (1); Liu, Hsiang-Chuan (1)
Author affiliation:(1) Department of Bioinformatics, Asia University, Taichun County, Taiwan; (2) Department of Computer Science and Information Engineering, Asia University, Taichun County, Taiwan;
(3) Department of Information Science and Applications, Asia University, Taichun County, Taiwan
Corresponding author:Chen, T.-C.
(vicvic@ms5.hinet.net)
Source title: Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Abbreviated source title:Proc. IEEE Int. Conf. Bioinformatics BioEng., BIBE
Monograph title:Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 Issue date:2009
Publication year:2009 Pages:348-351
Article number:5211248 Language:English
ISBN-13:9780769536569
Document type:Conference article (CA)
Conference name:2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009
Conference date:June 22, 2009 - June 24, 2009 Conference location:Taichung, Taiwan
Conference code:78002
Sponsor:IEEE Computer Society Asia University; Biological and AI Society
Publisher:IEEE Computer Society, 445 Hoes Lane - P.O.Box 1331, Piscataway, NJ 08855-1331, United States
Abstract:Epilepsy is a chronic neurological disorder that is
characterized by recurrent unprovoked seizures. These seizures are due to abnormal, excessive or synchronous neuronal activity in the
brain. For a neural network, such as brain, nonlinearity is necessary to descript the complexity of dynamic system. In this study, we compared some nonlinear dynamic indictions, such as Hurst
exponent, sample entropy, and detrended fluctuation in time series of epilepsy electroencephalogram regarding different physiological and pathological brain states. We found that The Hurst exponent did not differ between healthy volunteers and intracranial patients (p>0.05). The sample entropy value did not differ between
healthy volunteers and seizure active patients (p>0.05). In other cases we found statistical significant differences between
investigated data sets. We concluded that using nonlinear dynamic indications we could discriminate the electroencephalogram
regarding different physiological and pathological brain states of epilepsy patients. © 2009 IEEE.
Number of references:13
Main heading:Electroencephalography
Controlled terms: Bioinformatics - Brain - Dynamical systems - Entropy - Optical sensors - Time series
Uncontrolled terms: Detrended fluctuation analysis -
Electroencephalogram - Epilepsy - Hurst exponent - Sample entropy Classification code:931 Classical Physics; Quantum Theory;
Relativity - 922.2 Mathematical Statistics - 921 Mathematics - 903 Information Science - 801 Chemistry - 741.3 Optical Devices and Systems - 732.2 Control Instrumentation - 641.1 Thermodynamics - 461.8.2 Bioinformatics - 461.6 Medicine and Pharmacology - 461.1 Biomedical Engineering
DOI:10.1109/BIBE.2009.49 Database:Compendex
Compilation and indexing terms, Copyright 2009 Elsevier Inc.
