Multiscale entropy analysis of different spontaneous motor unit discharge patterns.
Multiscale entropy analysis of different spontaneous motor unit discharge patterns.
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DOI：
10.1109/jbhi.2013.2241071
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发表时间：
2013-03
影响因子：
7.7
通讯作者：
Zhou P
中科院分区：
文献类型：
--
作者：
Zhang X;Chen X;Barkhaus PE;Zhou P
本研究探讨了一种新的应用多尺度熵（MSE）分析表征不同模式的自发肌电图（EMG）信号，包括零星的，紧张性和重复性自发运动单位放电，和正常的表面EMG基线。MSE分析的两种算法，即标准的MSE和固有模式熵（IMEn）（基于最近开发的多元经验模式分解（MEMD）方法），被施加到不同的模式的自发肌电图。对于任何两种自发EMG模式，在标准MSE和IMEn分析的多个尺度中观察到显著差异（p < 0.001），而从单尺度熵分析可能未观察到这种显著性。与标准MSE相比，IMEn分析便于使用相对低的尺度数来辨别自发EMG信号的各种模式之间的熵差。本研究的发现有助于我们理解不同的自发肌电图模式的非线性动力学特性，这可能与脊髓运动神经元或运动单位的健康。
This study explores a novel application of multi-scale entropy (MSE) analysis for characterizing different patterns of spontaneous electromyogram (EMG) signals including sporadic, tonic and repetitive spontaneous motor unit discharges, and normal surface EMG baseline. Two algorithms for MSE analysis, namely the standard MSE and the intrinsic mode entropy (IMEn) (based on the recently developed multivariate empirical mode decomposition (MEMD) method), were applied to different patterns of spontaneous EMG. Significant differences were observed in multiple scales of the standard MSE and IMEn analyses (p < 0.001) for any two of the spontaneous EMG patterns, while such significance may not be observed from the single scale entropy analysis. Compared to the standard MSE, the IMEn analysis facilitates usage of a relatively low scale number to discern entropy difference among various patterns of spontaneous EMG signals. The findings from this study contribute to our understanding of the nonlinear dynamic properties of different spontaneous EMG patterns, which may be related to spinal motoneuron or motor unit health.