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Surface electromyography based muscle fatigue progression analysis using modified B distribution time-frequency features

机译:使用改进的B分布时频特征的基于表面肌电图的肌肉疲劳进行分析

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In this work, an attempt has been made to analyze the progression of muscle fatigue using surface electromyography (sEMG) signals and modified B distribution (MBD) based time-frequency analysis. For this purpose, signals are recorded from biceps brachii muscles of fifty healthy adult volunteers during dynamic contractions. The recorded signals are preprocessed and then subjected to MBD based time-frequency distribution (TFD). The instantaneous median frequency (IMDF) is extracted from the time-frequency matrix for different values of kernel parameter. The linear regression technique is used to model the temporal variations of IMDF. Correlation coefficient is computed in order to select the appropriate value for kernel parameter of MBD based TFD. Further, extended version of frequency domain features namely instantaneous spectral ratio (InstSPR) at low frequency band (LFB), medium frequency band (MFB) and high frequency band (HFB) are extracted from the time-frequency spectrum. In addition to these features, IMDF and instantaneous mean frequency (IMNF) are also calculated. The least square error based linear regression technique is used to track the slope variations of these features. The results show that MBD based time-frequency spectrum is able to provide the instantaneous variations of frequency components associated with fatiguing contractions. The values of InstSPR at MFB and HFB regions, IMDF and IMNF show a decreasing trend during the progression of muscle fatigue. However, an increasing trend is observed in LFB regions. Further the coefficient of variation is calculated for all the features. It is found that the values of IMDF, IMNF and InstSPR in LFB region have lowest variability across different subjects in comparison with other two features. It appears that this method could be useful in analyzing various neuromuscular activities in normal and abnormal conditions. (C) 2015 Elsevier Ltd. All rights reserved.
机译:在这项工作中,已经尝试使用表面肌电图(sEMG)信号和基于时频分析的改进B分布(MBD)分析肌肉疲劳的进展。为此,在动态收缩期间从五十名健康成人志愿者的肱二头肌肱二头肌记录信号。对记录的信号进行预处理,然后进行基于MBD的时频分布(TFD)。对于不同的内核参数值,从时频矩阵中提取瞬时中值频率(IMDF)。线性回归技术用于对IMDF的时间变化建模。计算相关系数以便为基于MBD的TFD的内核参数选择适当的值。此外,从时频频谱中提取出频域特征的扩展版本,即低频带(LFB),中频带(MFB)和高频带(HFB)的瞬时频谱比(InstSPR)。除了这些功能,还可以计算IMDF和瞬时平均频率(IMNF)。基于最小二乘误差的线性回归技术用于跟踪这些特征的斜率变化。结果表明,基于MBD的时间频谱能够提供与疲劳收缩相关的频率分量的瞬时变化。在肌肉疲劳进程中,MFB和HFB区域,IMDF和IMNF处的InstSPR值呈下降趋势。但是,在LFB地区观察到增加的趋势。此外,针对所有特征计算变异系数。发现与其他两个特征相比,LFB区域中IMDF,IMNF和InstSPR的值在不同对象之间的变异性最低。看来该方法可用于分析正常和异常情况下的各种神经肌肉活动。 (C)2015 Elsevier Ltd.保留所有权利。

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