Classification of neuromuscular diseases with artificial intelligence methods

Abstract

In this study, a classification structure consisting of five processing stages was organized for the diagnosis of ALS and Myopathic diseases, the most common types of neuromuscular diseases.EMG (Electromyogram) signals have been passed through pre-processing, division, clustering, and feature extraction steps before being classified. Hybrid clustering method is used in clustering phase. Afterwards, feature vectors intime and frequency domains and their different combinations of multiple feature vectors (a total of 25 feature vectors) are used. In the next step, data are classified by Support Vector Machine (DVM), K-Nearest Neighbor (K-EYK) algorithm and Discriminant Analysis (DA) algorithms. Verification is used as a measure of cross-validation method. Cross-validation of the k-value of 10 is selected. Experimental results show that the proposed feature vectors are more successful than the single feature vectors of multiple feature vectors. When usedin multiple feature vectors; SVM classifier, has classified the EMG signals withhigher accuracy in according to the K-NN and DA classifiers. Total accuracy is97.39% for ALS and 86.74% for the myogenic. It is understood with this study; the classification performance depends on a high degree of feature vectors of interclass separability.