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MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition

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Abstract

We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short periodȁ9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive characteristics compared with the former decomposition methods: (1) it bandpass filters the EMG signal through wavelet filter and utilizes threshold estimation calculated in wavelet transform for noise reduction in EMG signals to detect MUAPs before amplitude single threshold filtering; (2) it removes the power interference component from EMG recordings by combining independent component analysis (ICA) and wavelet filtering method together; (3) the similarity measure for MUAP clustering is based on the variance of the error normalized with the sum of RMS values for segments; (4) it finally uses ICA method to subtract all accurately classified MUAP spikes from original EMG signals. The technique of our EMG signal decomposition is fast and robust, which has been evaluated through synthetic EMG signals and real EMG signals.

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Acknowledgements

This research was supported by National Basic Research Program Grant No.2005CB724303 of PR China (973 Program). The authors would also like to thank Dr. Zhang for her assistance with our data acquisition and analysis.

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Authors and Affiliations

  1. Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, Peopleȁ9s Republic of China

    Xiaomei Ren, Zhizhong Wang & Zhiguo Yan

  2. Laser Life Science Institute, South China Normal University, Guangzhou, 510631, Peopleȁ9s Republic of China

    Xiao Hu

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  1. Xiaomei Ren
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  2. Xiao Hu
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  4. Zhiguo Yan
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Correspondence to Xiaomei Ren.

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Ren, X., Hu, X., Wang, Z. et al. MUAP extraction and classification based on wavelet transform and ICA for EMG decomposition. Med Bio Eng Comput 44, 371–382 (2006). https://doi.org/10.1007/s11517-006-0051-3

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