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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/8767


    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)
    Contributors: Department of Bioinformatics
    Keywords: Bioinformatics;Brain;Dynamical systems;Entropy;Optical sensors;Time series;Detrended fluctuation analysis;Electroencephalogram;Epilepsy;Hurst exponent;Sample entropy
    Date: 2009
    Issue Date: 2010-04-08 12:05:53 (UTC+0)
    Publisher: Asia University
    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.
    Relation: Proceedings of the 2009 9th IEEE International Conference on Bioinformatics and BioEngineering, BIBE 2009 :348-351
    Appears in Collections:[生物資訊與醫學工程學系 ] 會議論文

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