Faculty Collaboration Database

Multiscale entropy analysis of different spontaneous motor unit discharge patterns. IEEE J Biomed Health Inform 2013 Mar;17(2):470-6

Date

11/16/2013

Pubmed ID

24235117

Pubmed Central ID

PMC3831372

DOI

10.1109/JBHI.2013.2241071

Scopus ID

2-s2.0-84885155783 (requires institutional sign-in at Scopus site)   39 Citations

Abstract

This study explores a novel application of multiscale 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 method), were applied to different patterns of spontaneous EMG. Significant differences were observed in multiple scales of the standard MSE and IMEn analyses (<;i>p<;/i> <; 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.

Author List

Zhang X, Chen X, Barkhaus PE, Zhou P

Author

Paul E. Barkhaus MD Professor in the Neurology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Action Potentials
Aged
Algorithms
Analysis of Variance
Arm
Electromyography
Entropy
Female
Humans
Male
Middle Aged
Muscle, Skeletal
Signal Processing, Computer-Assisted