Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/6512
Title: Detecting variabilities of Doppler ultrasound signals by a modified mixture of experts with diverse features
Authors: Übeyli, Elif Derya
Güler, İnan
Keywords: modified mixture of experts
expectation-maximization algorithm
spectral analysis methods
Doppler ultrasound signals
diverse features
Issue Date: 2008
Publisher: Academic Press Inc Elsevier Science
Abstract: This paper illustrates the use of modified mixture of experts (MME) network structure to guide model selection for classification of Doppler ultrasound signals with diverse features. The MME is a modular neural network architecture for supervised learning. Expectation-maximization (EM) algorithm was used for training the MME so that the learning process is decoupled in a manner that fits well with the modular structure. In practical applications of pattern recognition, there are often diverse features extracted from raw data which needs recognizing. Decision making was performed in two stages: feature extraction by spectral analysis methods (fast Fourier transform and model-based methods) and classification using the classifiers trained on the extracted features. In order to discriminate the Doppler ultrasound signals, the ability of designed and trained MME network structure combined with spectral analysis methods was explored. The MME achieved accuracy rates which were higher than that of the mixture of experts (ME) and feedforward neural network models (multilayer perceptron neural network-MLPNN). The proposed MME approach can be useful in classifying the Doppler ultrasound signals for early detection of arterial diseases. (C) 2007 Elsevier Inc. All rights reserved.
URI: https://doi.org/10.1016/j.dsp.2007.02.002
https://hdl.handle.net/20.500.11851/6512
ISSN: 1051-2004
1095-4333
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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