J Med Syst - Detection of carotid artery disease by using Learning Vector Quantization Neural Network.

Tópicos

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Resumo

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.

Resumo Limpo

doppler ultrasound usual prefer investig arteri condit last two decad noninvas easi appli reliabl techniqu studi biomed system base learn vector quantiz neural network lvq nn develop order classifi intern carotid arteri doppler signal obtain subject suffer intern carotid arteri stenosi rest healthi subject system compos featur extract classif part basic featur extract stage power spectral densiti psd estim intern carotid arteri doppler signal obtain use burg autoregress ar spectrum analysi techniqu order obtain medic inform classif stage lvq nn use classifi featur burg ar method experi lvq nn base method reach classif accuraci fold cross valid cv techniqu addit classif perform lvq nn compar method multi layer perceptron mlp nn naiv bay nb knearest neighbor knn decis tree support vector machin svm sensit specif statist paramet classif result show lvq nn method effect classif intern carotid arteri doppler signal

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