Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/7526
Title: Statistics over features for internal carotid arterial disorders detection
Authors: Übeyli, Elif Derya
Keywords: Doppler signals
Lyapunov exponents
eigenvector methods
feature extraction/selection
mixture of experts
modified mixture of experts
Issue Date: 2008
Publisher: Pergamon-Elsevier Science Ltd
Abstract: The objective of the present study is to extract the representative features of the internal carotid arterial (ICA) Doppler ultrasound signals and to present the accurate classification model. This paper presented the usage of statistics over the set of the extracted features (Lyapunov exponents and the power levels of the power spectral density estimates obtained by the eigenvector methods) 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. Mixture of experts (ME) and modified mixture of experts (MME) architectures were formulated and used as basis for detection of arterial disorders. Three types of ICA Doppler signals (Doppler signals recorded from healthy subjects, subjects having stenosis, and subjects having occlusion) were classified. The classification results confirmed that the proposed ME and MME has potential in detecting the arterial disorders. (C) 2007 Elsevier Ltd. All rights reserved.
URI: https://doi.org/10.1016/j.compbiomed.2007.12.002
https://hdl.handle.net/20.500.11851/7526
ISSN: 0010-4825
1879-0534
Appears in Collections:Elektrik ve Elektronik Mühendisliği Bölümü / Department of Electrical & Electronics Engineering
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collection
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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