Features based on intrinsic mode functions for classification of EMG signals
by Varun Bajaj; Anil Kumar
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 18, No. 2, 2015

Abstract: In this paper, the features based on Intrinsic Mode Functions (IMFs) for classification of EMG signals are presented. The EMD method decomposes EMG signals into a set of narrow-band components known as IMFs. The features, namely mean frequency estimation and singular value computation, extracted from IMFs are exploited for classification of EMG signals. These parameters are used as an input to Least Squares Support Vector Machine (LS-SVM) with Radial Basis Function (RBF) for automatic classification of EMG signals. The classification accuracy for classification of normal and abnormal EMG signals obtained by the proposed method is 99.03% with RBF kernel of LS-SVM. The experimental results are presented to show the effectiveness of the proposed method for classification of normal and abnormal EMG signals (myopathy and neuropathy).

Online publication date: Thu, 25-Jun-2015

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