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

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 perioda9s 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.

EMG Signal Decomposition Using Motor Unit Potential Train Validity

“…Recently, complex wavelets and empirical mode decomposition (EMD) methods have been applied to remove noise from EMG signals [53][54][55][56][57]. These methods, in general, include a signal decomposition step, a soft thresholding step, and a signal reconstruction step.…”

The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal

“…Therefore, many works have tried to find the optimal wavelet denoising parameters which lead to maximum filtration performance. All research studies about wavelet denoising parameters in analysis of the sEMG signals have been discussed in this section (Guo et al, 2004a(Guo et al, , 2004b(Guo et al, , 2005Hussain et al, 2007Hussain et al, , 2009Jiang & Kuo, 2007;Khezri & Jahed, 2008;Li et al, 2010;Liu & Luo, 2008;Luo et al, 2007;Moshou et al, 2000;Ren et al, 2006;Yang & Luo, 2004;Zhang & Luo, 2006;Zhang et al, 2010) as summarized in Table 3. In details, the sEMG signal analysis based on WT has been firstly proposed in 2000 by Moshou et al (2000).…”

“…All of these introduced literatures work well for the surface EMG signal. On the other hand, for the intramuscular EMG signal, Ren et al (2006) developed a technique for extracting and classifying motor unit action potentials (MUAPs) for needle EMG signal decomposition. In that technique, noise reduction based on threshold estimation calculated in the WT was proposed.…”

“…On the contrary, some high frequency components are still important for surface EMG signal, thus proposed technique in Ren et al work (2006) is not included in our study. However, even though the results from most of previous works are not presented using the quantitative results, the improving ability in those applications based on wavelet denoising algorithm has been definitely established (Hussain et al, 2009;Khezri & Jahed, 2008;Ren et al, 2006;Zhang et al, 2010). In order to more clearly understand the effectiveness of wavelet denoising algorithm over conventional filters, discussion and illustration have been presented in review of Zhang et al (2010).…”

“…Recommendation above can be useful to apply for many sEMG applications. However, for analysis of intramuscular EMG signal, re-evaluation of wavelet denoising parameters should be done because the purpose in interpretation is different (Ren et al, 2006). However, the pre-processing stage based on wavelet denoising algorithm is recommended to be implemented in analysis of sEMG signal, especially in multifunction myoelectric control system.…”