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Title: Statistics over Lyapunov Exponents for Feature Extraction: Electroencephalographic Changes Detection Case
Authors: Übeyli, Elif Derya
Güler, Inan
Keywords: Chaotic signal
Electroencephalogram (EEG) signals
Feature extraction/selection
Lyapunov exponents
Issue Date: 2005
Publisher: World Acad Sci, Eng & Tech-Waset
Source: Conference of the World-Academy-of-Science-Engineering-and-Technology -- JAN 19-21, 2005 -- Berlin, GERMANY
Series/Report no.: Proceedings of World Academy of Science Engineering and Technology
Abstract: A new approach based on the consideration that electroencephalogram (EEG) signals are chaotic signals was presented for automated diagnosis of electroencephaloggraphic changes. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. This paper presented the usage of statistics over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. Since classification is more accurate when the pattern is simplified through representation by important features, feature extraction and selection play an important role in classifying systems such as neural networks. Multilayer perceptron neural network (MLPNN) architectures were formulated and used as basis for detection of electroencephalographic changes. Three types of EEG signals (EEG signals recorded from healthy volunteers with eyes open, epilepsy patients in the epileptogenic zone during a seizure-free interval, and epilepsy patients during epileptic seizures) were classified. The selected Lyapunov exponents of the EEG signals were used as inputs of the MLPNN trained with Levenberg-Marquardt algorithm. The classification results confirmed that the proposed MLPNN has potential in detecting the electroencephalographic changes.
ISSN: 1307-6884
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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