M Ghofrani Jahromi - Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran
H Parsaei - Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran
A Zamani - Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran
M Dehbozorgi - Department of Medical Physics and Engineering, Shiraz University of Medical Sciences, Shiraz, Iran

Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impact on the performance of a decomposition system. EMG decomposition has been studied well and several systems were proposed, but feature extraction step has not been investigated in detail.Objective: Several EMG signals were generated using a physiologically-based EMG signal simulation algorithm. For each signal, the firing patterns of motor units (MUs) provided by the simulator were used to extract MUPs of each MU. For feature extraction, different wavelet families including Daubechies (db), Symlets, Coiflets, bi-orthogonal, reverse bi-orthogonal and discrete Meyer were investigated. Moreover, the possibility of reducing the dimensionality of MUP feature vector is explored in this work. The MUPs represented using wavelet-domain features are transformed into a new coordinate system using Principal Component Analysis (PCA). The features were evaluated regarding their capability in discriminating MUPs of individual MUs. Results: Extensive studies on different mother wavelet functions revealed that db۲, coif۱, sym۵, bior۲.۲, bior۴.۴, and rbior۲.۲ are the best ones in differentiating MUPs of different MUs. The best results were achieved at the ۴th detail coefficient. Overall, rbior۲.۲ outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than ۱۲ MUPTs, syms۵ wavelet function is the best function. Applying PCA slightly enhanced the results.

Electromyographic signal, EMG decomposition, Decomposability index, Feature extraction, Motor Unit Potential Classification, Wavelet Function, Wavelet Transform