Detection of tremor bursts by a running second order moment function and analysis using interburst histograms

Introduction: 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 SOME Materials and methods: 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. Results: 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. Conclusion: 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. (c) 2007 IPEM. Published by Elsevier Ltd. All rights reserved.