Changes in Surface Electromyography Signal according to Severity in Patients with Carpal Tunnel Syndrome

Kim, Changbeom; Park, Chan-Hyuk; Kim, Chang-Hwan; Lee, Hyun-Sung; Kim, Myeong-Ok

doi:10.18214/jend.2020.22.1.15

Cited by 2 publications

(1 citation statement)

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“…Evaluation of muscle fatigue based on EMG signals can be performed using various methods, including spectral analysis, time-frequency distribution analysis, fractal analysis, and signal entropy analysis ( González-Izal et al, 2012 ; Sun et al, 2022 ). One of the most popular methods for quantitatively determining muscle fatigue based on non-invasive sEMG measurements is spectral analysis, specifically analyzing the frequency parameters: mean frequency (MNF) and median frequency (MDF) of the electromyographic signal ( Phinyomark et al, 2012a ; Kim et al, 2020 ). Numerous studies have shown that an increase in muscle fatigue leads to a decrease in the values of MDF and MNF over time ( Dantas et al, 2010 ; Yousif et al, 2019a ; Özgören and Arıtan, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

Quantitative assessment of muscle fatigue during rowing ergometer exercise using wavelet analysis of surface electromyography (sEMG)

Daniel,

Małachowski,

Sybilski

et al. 2024

Front. Bioeng. Biotechnol.

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In this paper, we present a quantitative assessment of muscle fatigue using surface electromyography (sEMG), a widely recognized method that is conducted through various analytical approaches, including analysis of spectral and time-frequency distributions. Existing research in this field has demonstrated considerable variability in the computational methods used. Although some studies highlight the efficacy of wavelet analysis in dynamic motion, few offer a comprehensive method for determining fatigue and applying it to specific movements. Previous research has focused primarily on discerning differences based on sport type or gender, with a notable absence of studies that presented results for quantifying fatigue during exercise with rowing ergometers. Developing on our previous work, where we introduced a method for determining muscle fatigue through wavelet analysis, considering biomechanical aspects of limb position changes, this current article serves as a continuation. Our study refines the research approach for a selected group, focusing on fatigue determination using the previously established method. The results obtained confirm the effectiveness of DWT analysis in assessing muscle fatigue, as evidenced by the achievement of negative values of the regression coefficients of Median Frequency (MDF) during exercises performed to maximal fatigue. Furthermore, it has been confirmed that the homogeneity of the group and, in the case of the examined group, the results previously achieved or lower limb strength do not have an impact on the results. Finally, we discuss the main limitations of our study and outline the subsequent steps of our investigation, providing valuable information for future investigations in this field.

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