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Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

Soares FA, Carvalho JL, Miosso CJ, de Andrade MM, da Rocha AF - Biomed Eng Online (2015)

Bottom Line: The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm.Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals.Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, University of Brasília, Campus Darcy Ribeiro, Caixa Postal 4386, 70910-900, Brasília, DF, Brazil. soaresfabiano@gmail.com.

ABSTRACT
In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

No MeSH data available.


Related in: MedlinePlus

Evaluation of the influence of the number of channels on CV estimation in the MLE and IPE methods, for a force level of 20 % of the MVC. The root mean squared error is shown as a function of the length of the window used to estimate the CV, for simulated signals with CV of 4 m/s, at different noise levels: a noise free; and b 16 dB SNR
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Fig6: Evaluation of the influence of the number of channels on CV estimation in the MLE and IPE methods, for a force level of 20 % of the MVC. The root mean squared error is shown as a function of the length of the window used to estimate the CV, for simulated signals with CV of 4 m/s, at different noise levels: a noise free; and b 16 dB SNR

Mentions: Figure 6 shows the results of the experiments which evaluated the influence of the number of channels on CV estimation, considering a force level of 20 % of the MVC. For 16 dB SNR signals (Fig. 6b), both, MLE and IPE methods have similar behavior, with RMSE decreasing while the number channels used to CV estimation increases. For noise free signals (Fig. 6a), both algorithms presents the RMSE increasing with channel (except for 5 channels in MLE method). In all cases, it is evident that the use of 5 or more channels is recommended to avoid RMSE higher than the high-goodness threshold.Fig. 6


Motor unit action potential conduction velocity estimated from surface electromyographic signals using image processing techniques.

Soares FA, Carvalho JL, Miosso CJ, de Andrade MM, da Rocha AF - Biomed Eng Online (2015)

Evaluation of the influence of the number of channels on CV estimation in the MLE and IPE methods, for a force level of 20 % of the MVC. The root mean squared error is shown as a function of the length of the window used to estimate the CV, for simulated signals with CV of 4 m/s, at different noise levels: a noise free; and b 16 dB SNR
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4574452&req=5

Fig6: Evaluation of the influence of the number of channels on CV estimation in the MLE and IPE methods, for a force level of 20 % of the MVC. The root mean squared error is shown as a function of the length of the window used to estimate the CV, for simulated signals with CV of 4 m/s, at different noise levels: a noise free; and b 16 dB SNR
Mentions: Figure 6 shows the results of the experiments which evaluated the influence of the number of channels on CV estimation, considering a force level of 20 % of the MVC. For 16 dB SNR signals (Fig. 6b), both, MLE and IPE methods have similar behavior, with RMSE decreasing while the number channels used to CV estimation increases. For noise free signals (Fig. 6a), both algorithms presents the RMSE increasing with channel (except for 5 channels in MLE method). In all cases, it is evident that the use of 5 or more channels is recommended to avoid RMSE higher than the high-goodness threshold.Fig. 6

Bottom Line: The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm.Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals.Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, University of Brasília, Campus Darcy Ribeiro, Caixa Postal 4386, 70910-900, Brasília, DF, Brazil. soaresfabiano@gmail.com.

ABSTRACT
In surface electromyography (surface EMG, or S-EMG), conduction velocity (CV) refers to the velocity at which the motor unit action potentials (MUAPs) propagate along the muscle fibers, during contractions. The CV is related to the type and diameter of the muscle fibers, ion concentration, pH, and firing rate of the motor units (MUs). The CV can be used in the evaluation of contractile properties of MUs, and of muscle fatigue. The most popular methods for CV estimation are those based on maximum likelihood estimation (MLE). This work proposes an algorithm for estimating CV from S-EMG signals, using digital image processing techniques. The proposed approach is demonstrated and evaluated, using both simulated and experimentally-acquired multichannel S-EMG signals. We show that the proposed algorithm is as precise and accurate as the MLE method in typical conditions of noise and CV. The proposed method is not susceptible to errors associated with MUAP propagation direction or inadequate initialization parameters, which are common with the MLE algorithm. Image processing -based approaches may be useful in S-EMG analysis to extract different physiological parameters from multichannel S-EMG signals. Other new methods based on image processing could also be developed to help solving other tasks in EMG analysis, such as estimation of the CV for individual MUs, localization and tracking of innervation zones, and study of MU recruitment strategies.

No MeSH data available.


Related in: MedlinePlus