Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/5617
Title: Combined neural network model employing lyapunov exponents: Internal carotid arterial disorders detection case
Authors: Güler N. F.
Übeyli, Elif Derya
Güler, İnan
Keywords: Chaotic signal
Combined neural network
Doppler signal
Internal carotid artery
Lyapunov exponents
Issue Date: 2005
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source: 2005 27th Annual International Conference of the Engineering in Medicine and Biology Society, IEEE-EMBS 2005, 1 September 2005 through 4 September 2005, Shanghai, 69123
Abstract: This paper illustrates the use of combined neural network models to guide model selection for diagnosis of internal carotid arterial disorders. The method presented in this study was directly based on the consideration that internal carotid arterial Doppler signals are chaotic signals. This consideration was tested successfully using the nonlinear dynamics tools, like the computation of Lyapunov exponents. Statistics were used over the set of the Lyapunov exponents in order to reduce the dimensionality of the extracted feature vectors. The first level networks were implemented for the diagnosis of internal carotid arterial disorders using the selected Lyapunov exponents as inputs. To improve diagnostic accuracy, the second level network was trained using the outputs of the first level networks as input data. The combined neural network models achieved accuracy rates which were higher than that of the stand-alone neural network models. © 2005 IEEE.
URI: https://doi.org/10.1109/iembs.2005.1615485
https://hdl.handle.net/20.500.11851/5617
ISBN: 0780387406; 9780780387409
ISSN: 0589-1019
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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