Multiresolution MUAPs decomposition and SVM-based analysis in the classification of neuromuscular disorders

摘要

This paper describes a new method for the classification of neuromuscular disorders based on the analysis of scalograms determined by the Symlet 4 wavelet technique. The approach involves isolating single motor unit action potentials (MUAPs), computing their scalograms, taking the maximum values of the scalograms in five selected scales, and averaging across MUAPs to give a single 5-dimensional feature vector per subject. After SVM analysis, the vector is reduced to a single decision parameter, called the Wavelet Index, allowing the subject to be assigned to one of three groups: myogenic, neurogenic or normal. The software implementation of the method described above created a tool supporting electromyographic (EMG) examinations. The method is characterized by a high probability for the accurate diagnosis of muscle state. The method produced 5 misclassifications out of 800 examined cases (total error of 0.6%).