Xie Hongbo, Wang Zhizhong
Department of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, PR China.
Comput Methods Programs Biomed. 2006 May;82(2):114-20. doi: 10.1016/j.cmpb.2006.02.009. Epub 2006 Apr 17.
The mean frequency (MNF) of surface electromyography (EMG) signal is an important index of local muscle fatigue. The purpose of this study is to improve the mean frequency (MNF) estimation. Three methods to estimate the MNF of non-stationary EMG are compared. A novel approach based on Hilbert-Huang transform (HHT), which comprises the empirical mode decomposition (EMD) and Hilbert transform, is proposed to estimate the mean frequency of non-stationary signal. The performance of this method is compared with the two existing methods, i.e. autoregressive (AR) spectrum estimation and wavelet transform method. It is observed that our method shows low variability in terms of robustness to the length of the analysis window. The time-varying characteristic of the proposed approach also enables us to accommodate other non-stationary biomedical data analysis.
表面肌电图（EMG）信号的平均频率（MNF）是局部肌肉疲劳的一个重要指标。本研究的目的是改进平均频率（MNF）估计。比较了三种估计非平稳肌电图平均频率的方法。提出了一种基于希尔伯特-黄变换（HHT）的新方法来估计非平稳信号的平均频率，该方法包括经验模态分解（EMD）和希尔伯特变换。将该方法的性能与两种现有方法，即自回归（AR）谱估计和小波变换方法进行了比较。结果表明，我们的方法在对分析窗口长度的鲁棒性方面表现出较低的变异性。所提方法的时变特性还使我们能够适应其他非平稳生物医学数据分析。
