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
Adaptive mixtures of local experts
Neural Computation
Analysis of spike-wave discharges in rats using discrete wavelet transform
Computers in Biology and Medicine
Statistics over features: EEG signals analysis
Computers in Biology and Medicine
Classification of internal carotid artery Doppler signals using fuzzy discrete hidden Markov model
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Detection of Carotid Artery Disease by Using Learning Vector Quantization Neural Network
Journal of Medical Systems
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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.