Digital signal processing (3rd ed.): principles, algorithms, and applications
Digital signal processing (3rd ed.): principles, algorithms, and applications
Application of Periodogram and AR Spectral Analysis to EEG Signals
Journal of Medical Systems
An Artificial Neural Network Approach to Diagnosing Epilepsy Using Lateralized Bursts of Theta EEGs
Journal of Medical Systems
Classification of heart sounds using an artificial neural network
Pattern Recognition Letters
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Application of Classical and Model-Based Spectral Methods to Describe the State of Alertness in EEG
Journal of Medical Systems
Pattern Recognition Letters
A recurrent neural network classifier for Doppler ultrasound blood flow signals
Pattern Recognition Letters
Training a learning vector quantization network using the pattern electroretinography signals
Computers in Biology and Medicine
Computers in Biology and Medicine
Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals
Expert Systems with Applications: An International Journal
Computer Methods and Programs in Biomedicine
Statistics over features for internal carotid arterial disorders detection
Computers in Biology and Medicine
A Study on Chronic Obstructive Pulmonary Disease Diagnosis Using Multilayer Neural Networks
Journal of Medical Systems
Recognition of facial expressions using Gabor wavelets and learning vector quantization
Engineering Applications of Artificial Intelligence
A comparative study on thyroid disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
Early prostate cancer diagnosis by using artificial neural networks and support vector machines
Expert Systems with Applications: An International Journal
Computers and Electrical Engineering
Expert Systems with Applications: An International Journal
Computers in Biology and Medicine
Urinary nucleosides as potential tumor markers evaluated by learning vector quantization
Artificial Intelligence in Medicine
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Doppler ultrasound has been usually preferred for investigation of the artery conditions in the last two decades, because it is a non-invasive, easy to apply and reliable technique. In this study, a biomedical system based on Learning Vector Quantization Neural Network (LVQ NN) has been developed in order to classify the internal carotid artery Doppler signals obtained from the 191 subjects, 136 of them had suffered from internal carotid artery stenosis and rest of them had been healthy subject. The system is composed of feature extraction and classification parts, basically. In the feature extraction stage, power spectral density (PSD) estimates of internal carotid artery Doppler signals were obtained by using Burg autoregressive (AR) spectrum analysis technique in order to obtain medical information. In the classification stage, LVQ NN was used classify features from Burg AR method. In experiments, LVQ NN based method reached 97.91% classification accuracy with 5 fold Cross Validation (CV) technique. In addition, the classification performance of the LVQ NN was compared with some methods such as Multi Layer Perceptron (MLP) NN, Naive Bayes (NB), K-Nearest Neighbor (KNN), decision tree and Support Vector Machine (SVM) with sensitivity and specificity statistical parameters. The classification results showed that the LVQ NN method is effective for classification of internal carotid artery Doppler signals.