Averaging methods for extracting representative waveforms from motor unit action potential trains

Malanda, Armando; Navallas, Javier; Rodriguez‐Falces, Javier; Carreño, Ignacio Rodríguez; Gila, Luis

doi:10.1016/j.jelekin.2015.04.007

Cited by 10 publications

(6 citation statements)

References 29 publications

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“…Based on the obtained results (Figures, 2,3, and 4), the two medianbased methods median and median-trimmed mean are the two top estimators. This finding is consistent with previous studies that showed these two methods outperformed other MUP template estimation methods [14,15].…”

Section: Discussionsupporting

confidence: 93%

“…Assuming X d×n represents a MUPT containing d MUPs, each of which represented by n time samples of a MUP, the problem of MUP template estimation is to use the information provided in this matrix for estimating an n-dimensional vector that can accurately represent MUP waveform shape of the MU that generated this MUPT. In this work, for a given MUPT, a MUP template was initially estimated, using conventional methods including mean, median, median-trimmed mean, or mode [14][15][16]. Thereafter, the primary estimated template was post-processed using the wavelet denois-ing technique.…”

Section: Mup Template Estimationmentioning

confidence: 99%

“…Due to background noise, interference from other MUs, and biological MUP shape variability of the MU (mainly because of jitter), estimating the MUP template is subject to some errors. Several methods, such as mean, median, median trimmed mean, statistical and interference canceling averaging techniques have been proposed to reduce the effect of interference to provide reliable MUP template estimates for individual MU [12][13][14][15][16]. These methods have produced prom-ising results and performance, but their accuracy decreases significantly when the number of MUP samples extracted for a specific MU is small.…”

Section: Introductionmentioning

confidence: 99%

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Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

2020

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Background: Electromyographic (EMG) signals obtained from a contracted muscle contain valuable information on its activity and health status. Much of this information lies in motor unit potentials (MUPs) of its motor units (MUs), collected during the muscle contraction. Hence, accurate estimation of a MUP template for each MU is crucial. Objective: To investigate the possibility of improving MUP template estimation using the wavelet denoising technique. Material and Methods: In this analytical study, several MUP template estimators were developed by combining conventional estimation methods and wavelet denoising techniques. A MUP template was initially estimated using conventional methods such as mean, median, median-trimmed mean, or mode. Thereafter, it was post-processed using the wavelet denoising technique. Results: Evaluation results of the studied estimators using 40 simulated EMG signals with a true template for each constituent MUP train showed that augmented wavelet- based template estimation methods are more reliable than conventional methods. However, on average, wavelet denoising was not much effective. Around 40 MUPs of a MU is sufficient to estimate its MUP template. Conclusion: Although wavelet techniques are effective in EMG signal analysis, here wavelet denoising did not practically improve MUP template estimation. Considering computational simplicity and estimation error, the two methods median and median-trimmed mean are practical estimators that can provide a good estimation of a MUP template for a MU when approximately 40 MUPs are available. Nevertheless, the baseline noise level in the MUP templates estimated using the median-trimmed mean method is slightly lower than that in the templates estimated using the median method.

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Section: Discussionsupporting

confidence: 93%

Section: Mup Template Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

2020

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“…This strategy is well-known in quantitative electromyography (EMG), where it is used to extract representative MUP waveforms, and several averaging algorithms, such as, [3,32,1,16,23,36,22,20,19,21] have been developed.…”

Section: And 2 (A))mentioning

confidence: 99%

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

Corera

Malanda

Rodriguez‐Falces

et al. 2020

Med Biol Eng Comput

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Removing artifacts from nearby motor units is one of the main objectives when processing scanning-EMG recordings. Methods such as median filtering or masked leastsquares smoothing (MLSS) can be used to eliminate artifacts in recordings with just one discharge of the motor unit potential (MUP) at each location. However, more effective artifact removal can be achieved if several discharges per position are recorded. In this case, processing usually involves averaging the discharges available at each position and then applying a median filter in the spatial dimension. The main drawback of this approach is that the median filter tends to distort the signal waveform. In this paper, we present a new algorithm that operates on multiple discharges simultaneously and in the spatial dimension.We refer to this algorithm as the multi masked least-squares smoothing (MMLSS) algorithm: an extension of the MLSS algorithm for the case of multiple discharges. The algorithm is tested using simulated scanning-EMG signals in different recording conditions, i.e., at different levels of muscle contraction and for different numbers of discharges per position. Results demonstrate that the algorithm eliminates artifacts more effectively than any previously available method and does so without distorting the waveform of the signal.

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“…Following the manual method, MUAP analysis is performed directly by users who visually inspect and identify the distinctive MUAP patterns ( 17 ). This method is time-consuming in practice, highly experience-dependent, and incapable of resolving superimposed waveforms ( 18 , 19 ). Hence, the development of automatic MUAP extraction methods is imperative to improve the work efficiency and clinical applicability of EMG decomposition.…”

Section: Introductionmentioning

confidence: 99%

Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution

Ren

Zhang

et al. 2018

Front. Neurol.

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A novel electromyography (EMG) signal decomposition framework is presented for the thorough and precise analysis of intramuscular EMG signals. This framework first detects all of the active motor unit action potentials (MUAPs) and assigns single MUAP segments to their corresponding motor units. MUAP waveforms that are found to be superimposed are then resolved into their constituent single MUAPs using a peel-off approach and similarly assigned. The method is composed of six stages of analytical procedures: preprocessing, segmentation, alignment and feature extraction, clustering and refinement, supervised classification, and superimposed waveform resolution. The performance of the proposed decomposition framework was evaluated using both synthetic EMG signals and real recordings obtained from healthy and stroke participants. The overall detection rate of MUAPs was 100% for both synthetic and real signals. The average accuracy for synthetic EMG signals was 87.23%. Average assignment accuracies of 88.63 and 94.45% were achieved for the real EMG signals obtained from healthy and stroke participants, respectively. Results demonstrated the ability of the developed framework to decompose intramuscular EMG signals with improved accuracy and efficiency, which we believe will greatly benefit the clinical utility of EMG for the diagnosis and rehabilitation of motor impairments in stroke patients.

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Averaging methods for extracting representative waveforms from motor unit action potential trains

Cited by 10 publications

References 29 publications

Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

Can Wavelet Denoising Improve Motor Unit Potential Template Estimation?

Masked least-squares averaging in processing of scanning-EMG recordings with multiple discharges

Intramuscular EMG Decomposition Basing on Motor Unit Action Potentials Detection and Superposition Resolution

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