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

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.