Nonlinear dynamic indications in time series of epilepsy electroencephalogram

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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.

([email protected])

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

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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

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