Cross-correlation based feature extraction from EMG signals for classification of neuro-muscular diseases

In this contribution, classification of two main neuromuscular diseases namely Myopathy and Neuropathy and Healthy signals is performed using cross-correlation based feature extraction technique. For this purpose, cross-correlation of Healthy, Myopathy and Neuropathy disease EMG signal is done with a reference Healthy signal. Selective features like Hjorth, Adaptive Autoregressive and statistical features comprising mean, standard deviation and power are extracted from the cross-correlated signals. Support Vector Machine(SVM) and k-Nearest Neighbor(kNN) are the two classifiers used for this work. Highest classification accuracy of 100% is obtainedby SVM using Gaussian Radial Basis Function (RBF) as the kernel function with AAR and all combined features as the feature set. For kNN, k=4 yields best result of 100% accuracy using the combined feature set.