Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition

• Published in: Journal of biomedical physics and engineering 2017-12, Vol.7 (4), p.365-378
• Main Authors: Ghofrani Jahromi, M, Parsaei, H, Zamani, A, Dehbozorgi, M
• Format: Article
• Language: English
• Subjects: Decomposability index; Electromyographic signal; EMG decomposition; Feature extraction; Motor Unit Potential Classification; Original; Wavelet Function; Wavelet Transform
• ISSN: 2251-7200; 2251-7200
• DOI: 10.22086/jbpe.v0i0.538

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. 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. Extensive studies on different mother wavelet functions revealed that db2, coif1, sym5, bior2.2, bior4.4, and rbior2.2 are the best ones in differentiating MUPs of different MUs. The best results were achieved at the 4th detail coefficient. Overall, rbior2.2 outperformed all wavelet functions studied; nevertheless for EMG signals composed of more than 12 MUPTs, syms5 wavelet function is the best function. Applying PCA slightly enhanced the results.