Faculty of Electrical Engineering and Computing, University of Zagreb, Unska 3, HR-10000 Zagreb, Croatia.
Med Biol Eng Comput. 2011 Jun;49(6):659-69. doi: 10.1007/s11517-010-0718-7. Epub 2010 Dec 9.
Surface electromyography (sEMG) is a common technique used in the assessment of local muscle fatigue. As opposed to static contraction situations, sEMG recordings during dynamic contractions are particularly characterised by non-stationary (and non-linear) features. Standard signal processing methods using Fourier and wavelet based procedures demonstrate well known restrictions on time-frequency resolution and the ability to process non-stationary and/or non-linear time-series, thus aggravating the spectral parameters estimation. The Hilbert-Huang transform (HHT), comprising of the empirical mode decomposition (EMD) and Hilbert spectral analysis (HSA), provides a new approach to overcome these issues. The time-dependent median frequency estimate is used as muscle fatigue indicator, and linear regression parameters are derived as fatigue quantifiers. The HHT method is utilised for the analysis of the sEMG signals recorded over quadriceps muscles during cyclic dynamic contractions. The results are compared with those obtained by the Fourier and wavelet based methods. It is shown that HHT procedure provides the most consistent and reliable assessment of spectral and derived linear regression parameters, given the time epoch width and sampling interval in the time domain. The suggested procedure successfully deals with non-stationary and non-linear properties of biomedical signals.
表面肌电图（sEMG）是评估局部肌肉疲劳的常用技术。与静态收缩情况相比，动态收缩期间的 sEMG 记录特别具有非平稳（和非线性）特征。使用基于傅里叶和小波的标准信号处理方法在时频分辨率和处理非平稳和/或非线性时间序列的能力方面表现出明显的局限性，从而加剧了光谱参数的估计。希尔伯特-黄变换（HHT），包括经验模态分解（EMD）和希尔伯特谱分析（HSA），提供了一种克服这些问题的新方法。时变中值频率估计用作肌肉疲劳指标，并得出线性回归参数作为疲劳量化指标。该 HHT 方法用于分析在周期性动态收缩过程中记录的股四头肌的 sEMG 信号。将结果与基于傅里叶和小波的方法获得的结果进行比较。结果表明，考虑到时域中的时间区间宽度和采样间隔，HHT 过程提供了最一致和可靠的光谱和衍生线性回归参数评估。所提出的方法成功地处理了生物医学信号的非平稳和非线性特性。
