Comparison of AM-FM Features with Standard Features for the Classification of Surface Electromyographic Signals

In this work AM-FM features extracted from surface electromyographic (SEMG) signals were compared with standard time and frequency domain features, for the classification of neuromuscular disorders at different force levels. SEMG signals were recorded from a total of 40 subjects: 20 normal and 20 abnormal cases, at 10%, 30%, 50%, 70% and 100% of maximum voluntary contraction (MVC), from the biceps brachii muscle. For the classification, three classifiers were used: (i) the statistical K-nearest neighbour (KNN), (ii) the neural self-organizing map (SOM) and (iii) the neural support vector machine (SVM). For all classifiers the leave-one-out methodology was implemented for the classification of the SEMG signals into normal or pathogenic. The test results reached a classification success rate of 77% for the AM-FM features whereas standard features failed to provide any meaningful results on the given dataset.