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[基于希尔伯特-黄变换的肌肉疲劳肌动图分析研究]

[A study of mechanomyography analysis for muscle fatigue with Hilbert-Huang transform].

作者信息

Hu Shuxian, Shi Jun

机构信息

School of Communication and Information Engineering, Shanghai University, Shanghai 200072, China.

出版信息

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2011 Apr;28(2):243-7.

Abstract

The mechanomyography (MMG) records and quantifies the low-frequency lateral oscillations of active muscle fibers. It can represent the mechanical characteristics of muscle activity. MMG has been used to evaluate muscle fatigue. Hilbert-Huang transform (HHT) is a time-frequency method with the feature of self-adaptation, and designed specifically for nonlinear and nonstationary signal analysis. In this study, MMG signal was recorded from biceps brachii during isometric fatigue contraction. HHT was used to calculate the difference between the maximum and minimum values of instantaneous frequency, named as the band ratio, to estimate muscle fatigue. The results showed that the band ratios were 0.431 +/- 0.607 and 0.286 +/- 0.218 after fatigue for the maximum voluntary contraction (MVC) of 50% and 70%, respectively. These indicated that the frequency declined after muscles fatigue.

摘要

肌动图(MMG)记录并量化活动肌纤维的低频横向振荡。它可以表征肌肉活动的力学特性。MMG已被用于评估肌肉疲劳。希尔伯特-黄变换(HHT)是一种具有自适应特性的时频方法,专门设计用于非线性和非平稳信号分析。在本研究中,在等长疲劳收缩期间从肱二头肌记录MMG信号。使用HHT计算瞬时频率最大值与最小值之间的差值,称为带宽比,以估计肌肉疲劳。结果表明,在50%和70%的最大自主收缩(MVC)疲劳后,带宽比分别为0.431±0.607和0.286±0.218。这些表明肌肉疲劳后频率下降。

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