Abstract
Electrophysiological muscle classification involves characterization of extracted motor unit potentials (MUPs) followed by the aggregation of these MUP characterizations. Existing techniques consider three classes (i.e., myopathic, neurogenic, and normal) for both MUP characterization and electrophysiological muscle classification. However, diseased induced MUP changes are continuous in nature, which make it difficult to find distinct boundaries between normal, myopathic and neurogenic MUPs. Hence, MUP characterization based on more than three classes is better able to represent the various effects of disease. Here, a novel, electrophysiological muscle classification system is proposed which considers a dynamic number of classes for characterizing MUPs. To this end, a clustering algorithm called neighborhood distances entropy consistency (NDEC) is proposed to find clusters with arbitrary shapes and densities in a MUP feature space. These clusters represent several concepts of MUP normality and abnormality and are used for MUP characterization instead of the conventional three classes. An examined muscle is then classified by embedding its MUP characterizations in a feature vector fed to an ensemble of SVM and nearest neighbor classifiers. For 103 sets of MUPs recorded in tibialis anterior muscles, the proposed system had a 97% electrophysiological muscle classification accuracy, which is significantly higher than in previous works.