A new feature selection method for classification of EMG signals

Discrimination of neuromuscular diseases based on electromyogram (EMG) is still a hot topic among the rehabilitation society. Although many attempts have been made to elicit informative features from the discretized EMG signals, traditional visual inspection is still their gold-standard method. Therefore, this paper is aimed at introducing an effective combinational feature to enhance the classification rate among the control group and subjects with neuropathy and myopathy diseases. All EMG signals were artificially simulated, by incorporating statistical and morphological properties of each group into their signal models, in the EMG laboratory of Waterloo University. To classify the subjects by the proposed method, first, EMG signals are decomposed by empirical mode decomposition (EMD) to its natural subspaces, then number of subspaces is aligned through all windowed signals, and Kolmogorov Complexity (KC) and other informative feature are determined to reveal the amount of irregularity within each subspace. Finally, these features are applied to support vector machine (SVM). Experimental results show our method can differentiate these three groups efficiently.