ArticleJournal of biomedical physics & engineering2017
Comparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition.
Article in Journal of biomedical physics & engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Muscle Fatigue in Dynamic Movement: Limitations and Challenges, Experimental Design, and New Research Horizons.Bioengineering (Basel, Switzerland) · 2026Review
- Improving Fast EMG Classification for Hand Gesture Recognition: A Comprehensive Analysis of Temporal, Spatial, and Algorithm Configurations for Healthy and Post-Stroke Subjects.Sensors (Basel, Switzerland) · 2025Article
- EM-DeepSD: A Deep Neural Network Model Based on Cell-Free DNA End-Motif Signal Decomposition for Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Correlation analysis of upper limb muscle activation in the frequency domain in wheelchair fencers.Frontiers in human neuroscience · 2025Article
- Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?Journal of biomedical physics & engineering · 2020Article
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Authors and funding
4 authors.
Funding
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Abstract
backgroundElectromyographic (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.
objectiveSeveral 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.
resultsExtensive 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.
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29392120PMC5758715What OpenQuestion holds
Registered trials
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