Journée Henricus Louis, Postma Alida Annechien, Sun Mingui, Staal Michiel J
Department of Neurosurgery, University Medical Center Groningen, University of Groningen, P.O. Box 30.001, 9700RB Groningen, The Netherlands.
Med Eng Phys. 2008 Jan;30(1):75-83. doi: 10.1016/j.medengphy.2006.12.005. Epub 2007 Feb 5.
Conventional linear signal processing techniques are not always suitable for the detection of tremor bursts in clinical practice due to inevitable noise from electromyographic (EMG) bursts. This study introduces (1) a non-linear analysis technique based on a running second order moment function (SOMF) and (2) auto- and cross-interburst interval histograms (IBIH) showing distributions of interburst interval EMG bursts of pathological tremors illustrating an application of the SOMF.
EMG recordings from extensors and flexors of two patients with Parkinson's disease with a rest tremor and from a healthy subject during sustained muscular contraction were preliminary analyzed in a pilot study. The SOMF was obtained by repeated second order moment calculations within a window of fixed width W (time scale parameter) plotted as a function of time. Minimum SOMF values indicate local "moments of inertia" of each EMG burst. Bursts were detected and located when minimum SOMF values were below level L (decision parameter). Optimal settings of parameters W and L were calculated empirically for pathological tremor EMGs. Auto- and cross-IBIHs were obtained from minimum SOMF values of detected bursts.
Tremor frequency and phase relation between EMG bursts from auto- and cross-IBIHs agreed with those derived from spectral analysis. Burst detection by SOMF has a high sensitivity and selectivity even with noisy background.
The SOMF is appropriate for detection of individual EMG bursts of pathological tremors. The technique is sensitive to non-stationary changes of tremor bursts regardless of their amplitude. IBIHs provide a measure of tremor frequency and phase difference between EMG bursts.
在临床实践中，由于肌电图（EMG）爆发中不可避免的噪声，传统的线性信号处理技术并不总是适用于震颤爆发的检测。本研究介绍了（1）一种基于运行二阶矩函数（SOMF）的非线性分析技术，以及（2）自爆发间隔直方图和交叉爆发间隔直方图（IBIH），它们展示了病理性震颤的爆发间隔EMG爆发的分布，说明了SOMF的应用。
在一项初步研究中，对两名患有静止性震颤的帕金森病患者的伸肌和屈肌以及一名健康受试者在持续肌肉收缩期间的EMG记录进行了初步分析。通过在固定宽度W（时间尺度参数）的窗口内重复进行二阶矩计算来获得SOMF，并将其绘制为时间的函数。SOMF的最小值表示每个EMG爆发的局部“惯性矩”。当SOMF最小值低于水平L（决策参数）时，检测并定位爆发。针对病理性震颤EMG，通过经验计算参数W和L的最佳设置。自IBIH和交叉IBIH从检测到的爆发的最小SOMF值中获得。
自IBIH和交叉IBIH的EMG爆发之间的震颤频率和相位关系与频谱分析得出的结果一致。即使在有噪声背景的情况下，通过SOMF进行的爆发检测也具有高灵敏度和选择性。
SOMF适用于检测病理性震颤的单个EMG爆发。该技术对震颤爆发的非平稳变化敏感，无论其幅度如何。IBIH提供了EMG爆发之间震颤频率和相位差的度量。
