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.
|Title of host publication||2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)|
|Publication status||Published (in print/issue) - 23 Feb 2017|
|Event||2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI) - Kolkata, India|
Duration: 21 Oct 2016 → 23 Oct 2016
|Conference||2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI)|
|Period||21/10/16 → 23/10/16|