<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The aim of this work was twofold: first, to propose signal processing methods for assessing the temporal and spectral changes of parameters (mean absolute value, the energy and standard deviation as temporal parameters, total and mean power as frequency parameters) of the surface myoelectric signal of the various patient groups like normal, myopathic and neuropathic during muscles contraction of biceps. Secondly, to analyze this electrical manifestation of neuromuscular disorders by the implementation of time-frequency analysis using continuous wavelet that allows us to qualify this method to evaluate, appreciate the pathology and determine its degree of severity which was unable by extracting mentioned parameters. Our results showed that this approach presents satisfactory performances especially to follow patients with the least severe pathology.</p></abstract>

The aim of this work was twofold: first, to propose signal processing methods for assessing the temporal and spectral changes of parameters (mean absolute value, the energy and standard deviation as temporal parameters, total and mean power as frequency parameters) of the surface myoelectric signal of the various patient groups like normal, myopathic and neuropathic during muscles contraction of biceps. Secondly, to analyze this electrical manifestation of neuromuscular disorders by the implementation of time-frequency analysis using continuous wavelet that allows us to qualify this method to evaluate, appreciate the pathology and determine its degree of severity which was unable by extracting mentioned parameters. Our results showed that this approach presents satisfactory performances especially to follow patients with the least severe pathology.

Application of the continuous wavelet transform for the analysis of pathological severity degree of electromyograms (EMGs) signals

Genie Biomedical Laboratory (GBM), Faculty of Technology

Polish Journal of Medical Physics and Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The aim of this work was twofold: first, to propose signal processing methods for assessing the temporal and spectral changes of parameters (mean absolute...

Application of the continuous wavelet transform for the analysis of pathological severity degree of electromyograms (EMGs) signals

Online veröffentlicht: 29. Sept. 2020

Seitenbereich: 149 - 154

Eingereicht: 31. März 2020

Akzeptiert: 06. Juni 2020

DOI: https://doi.org/10.2478/pjmpe-2020-0017

Schlüsselwörter
EMG, parameters, time-frequency, continuous wavelet, severity

© 2020 Aicha Mokdad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Application of the continuous wavelet transform for the analysis of pathological severity degree of electromyograms (EMGs) signals

Online veröffentlicht: 29. Sept. 2020

Seitenbereich: 149 - 154

Eingereicht: 31. März 2020

Akzeptiert: 06. Juni 2020

DOI: https://doi.org/10.2478/pjmpe-2020-0017

SchlüsselwörterEMG, parameters, time-frequency, continuous wavelet, severity

© 2020 Aicha Mokdad et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
EMG, parameters, time-frequency, continuous wavelet, severity