Probability and statistics with reliability, queuing and computer science applications
Probability and statistics with reliability, queuing and computer science applications
Implementing wavelet/probabilistic neural networks for Doppler ultrasound blood flow signals
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Complex-valued wavelet artificial neural network for Doppler signals classifying
Artificial Intelligence in 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
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Computers in Biology and Medicine
Journal of Systems and Software
Carotid artery image segmentation using modified spatial fuzzy c-means and ensemble clustering
Computer Methods and Programs in Biomedicine
Application of data mining techniques for detecting asymptomatic carotid artery stenosis
Computers and Electrical Engineering
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Carotid artery diseases are defined as the narrowing or the blockage of the carotid arteries. These two conditions are called carotid artery stenosis or occlusion respectively. Stenosis and occlusion are usually caused by cholesterol deposits and fatty substances which are called plaque. In addition, they represent significant causes of strokes. Thus, they should be a part of regular physical examinations. An important and preliminary diagnosis is to listen to the arteries in the neck using a stethoscope or a Doppler ultrasound (US) device. However, it is sometimes very difficult for a non-professional physician to differentiate between a normal and an abnormal sound due to blood flow blockage. This paper presents a low-cost efficient method that can be used in the automatic screening of carotid artery diseases, especially in areas with high population. Doppler US signals are preprocessed for noise elimination. Then, some features for normal, stenosis and occlusion signals are extracted from the frequency domain of these signals using their spectrograms. A multi-layer feed forward neural-network (MLFFNN) and a k-nearest neighbor (KNN) classifiers were used to automatically diagnose the input signals. The approach is applied to 72 samples divided into three equal sets which represent the three main classes to be identified, i.e., normal, stenosis and occlusion patterns. We used in the training phase 75% of each set and the rest was used in the test phase. Experimental results show the simplicity and efficiency of the presented approach for automatic diagnosis of carotid artery diseases. The maximum obtained classification accuracies are 91.67%, 100%, and 95.89% for the normal, stenosis and occlusion patterns respectively when the MLFFNN classifier is used. In comparison with similar approaches, the proposed approach is less complex, hence runs faster which suggests its suitability as an efficient screening method for the detection of carotid artery diseases.