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局部肌肉耐力与基于疲劳的肌电图频谱特性变化有关,但与传导速度无关。

Local muscle endurance is associated with fatigue-based changes in electromyographic spectral properties, but not with conduction velocity.

作者信息

Beck Travis W, Ye Xin, Wages Nathan P

机构信息

Biophysics Laboratory, University of Oklahoma, Department of Health and Exercise Science, 110 Huston Huffman Center, Norman, OK 73019-6081, United States.

Biophysics Laboratory, University of Oklahoma, Department of Health and Exercise Science, 110 Huston Huffman Center, Norman, OK 73019-6081, United States.

出版信息

J Electromyogr Kinesiol. 2015 Jun;25(3):451-6. doi: 10.1016/j.jelekin.2015.02.006. Epub 2015 Feb 20.

Abstract

The purpose of this study was to examine the associations amongst muscle fiber action potential conduction velocity (CV), spectral characteristics of the surface electromyographic (EMG) signal, and endurance time during a sustained submaximal isometric muscle action. Eleven men (mean±SD age=23±4yrs) performed a sustained, submaximal isometric muscle action of the dominant forearm flexors at 60% of the maximum voluntary contraction (MVC) until the designated force level could no longer be maintained. Sixteen separate bipolar surface EMG signals were detected from the biceps brachii with a linear electrode array during this contraction. Two channels from this array were used to measure CV, and one of these two channels was used for further EMG signal processing. The channels that provided the highest signal quality were used for the CV measurements and further data analysis. A wavelet analysis was then used to analyze the bipolar EMG signal, and the resulting wavelet spectrum was decomposed with a nonparametric spectral decomposition procedure. The results showed that the time to exhaustion during the sustained contraction was not correlated with the rate of decrease in CV, but it was highly correlated with both the decrease in high-frequency spectral power (r=0.947) and the increase in low-frequency spectral power (r=0.960). These findings are particularly interesting, considering that the decrease in traditional EMG spectral variables (e.g., mean frequency or median frequency) with fatigue is generally attributed to reductions in CV. While this may indeed be true, the present results suggested that other factors (i.e., other than CV) that can affect the shape of the EMG frequency spectrum during fatigue are more important in determining the endurance capabilities of the muscle than is CV.

摘要

本研究的目的是探讨在持续的次最大等长肌肉收缩过程中,肌纤维动作电位传导速度(CV)、表面肌电图(EMG)信号的频谱特征与耐力时间之间的关联。11名男性(平均±标准差年龄=23±4岁)对优势前臂屈肌进行持续的次最大等长收缩,收缩强度为最大自主收缩(MVC)的60%,直至无法再维持指定的力量水平。在此收缩过程中,使用线性电极阵列从肱二头肌检测到16个独立的双极表面EMG信号。该阵列中的两个通道用于测量CV,这两个通道中的一个用于进一步的EMG信号处理。提供最高信号质量的通道用于CV测量和进一步的数据分析。然后使用小波分析来分析双极EMG信号,并使用非参数频谱分解程序对所得的小波频谱进行分解。结果表明,持续收缩过程中的疲劳时间与CV的下降速率无关,但与高频频谱功率的下降(r=0.947)和低频频谱功率的增加(r=0.960)均高度相关。考虑到传统EMG频谱变量(如平均频率或中位数频率)随疲劳的下降通常归因于CV的降低,这些发现尤其有趣。虽然这可能确实如此,但目前的结果表明,在疲劳过程中,其他能够影响EMG频谱形状的因素(即除CV之外的因素)在决定肌肉的耐力能力方面比CV更重要。

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