Detecting variabilities of Doppler ultrasound signals by a modified mixture of experts with diverse features

Authors:
Elif Derya Übeyli;İnan Güler
Affiliations:
Department of Electrical and Electronics Engineering, Faculty of Engineering, TOBB Ekonomive Teknoloji Üniversitesi, 06530 Söğütözü, Ankara, Turkey;Department of Electronics and Computer Education, Faculty of Technical Education, Gazi University, 06500 Teknikokullar, Ankara, Turkey
Venue:
Digital Signal Processing
Year:
2008

Citing 7
Cited 0

Hierarchical mixtures of experts and the EM algorithm

Neural Computation
A connectionist method for pattern classification with diverse features

Pattern Recognition Letters
An improved neural classification network for the two-group problem

Computers and Operations Research
Improved learning algorithms for mixture of experts in multiclass classification

Neural Networks
Neural Networks: A Comprehensive Foundation

Neural Networks: A Comprehensive Foundation
A Mixture of Experts Network Structure Construction Algorithm for Modelling and Control

Applied Intelligence
Adaptive mixtures of local experts

Neural Computation

Quantified Score

Hi-index	0.00

Visualization

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.