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首页> 外文期刊>IEEE transactions on rehabilitation engineering >Spectral compression of the electromyographic signal due to decreasing muscle fiber conduction velocity
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Spectral compression of the electromyographic signal due to decreasing muscle fiber conduction velocity

机译:由于肌纤维传导速度降低,肌电信号的频谱压缩

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摘要

Spectral compression of the electromyographic (EMG) signal, due largely to decreasing muscle fiber conduction velocity, is commonly used as an indication of muscle fatigue. Current methods of estimating conduction velocity using characteristic frequencies such as the median frequency of the power spectrum, are based on an assumption of uniform spectral compression. To examine changes in the EMG frequency spectrum during fatigue, muscle fiber conduction velocity was measured during sustained, isometric contractions of the biceps brachii. Compression of the EMG power and amplitude spectra was simultaneously examined using the median frequency and-an alternative method-the spectral distribution technique. The spectral distribution technique consistently gave a better estimate of the relative change in muscle fiber conduction velocity than either of the median frequencies. This was further examined using a physiologically based EMG simulation model, which confirmed these findings. The model indicated that firing statistics can significantly influence spectral compression, particularly the behavior of characteristic frequencies in the vicinity of the firing rates. The relative change in the median frequency, whether of the amplitude or frequency spectrum, was consistently greater than the relative change in conduction velocity. The most accurate indication of the relative change in conduction velocity was obtained by calculating the mean shift in the midfrequency region of the EMG amplitude spectrum using the spectral distribution technique.
机译:通常由于肌纤维传导速度降低而引起的肌电图(EMG)信号的频谱压缩通常被用作肌肉疲劳的指标。当前使用特征频率(例如功率谱的中频)估算传导速度的方法是基于均匀频谱压缩的假设。为了检查疲劳期间肌电图频谱的变化,在肱二头肌的持续等距收缩过程中测量了肌纤维传导速度。 EMG功率和振幅频谱的压缩同时使用中频和另一种方法频谱分布技术进行了检查。频谱分布技术始终比任何一个中值频率更好地估计了肌肉纤维传导速度的相对变化。使用基于生理的EMG模拟模型进一步检查了这一点,证实了这些发现。该模型表明,点火统计数据可以显着影响频谱压缩,特别是点火速率附近特征频率的行为。无论是幅度还是频谱,中值频率的相对变化始终大于传导速度的相对变化。通过使用频谱分布技术计算EMG振幅频谱的中频区域的平均偏移,可以最准确地指示传导速度的相对变化。

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