The value of electromyography of the lumbar paraspinal muscles in discriminating between chronic-low-back-pain sufferers and normal subjects

Humphrey, Anthony R.; Nargol, Avf; Jones, Anthony P. C.; Ratcliffe, Amy; Greenough, C G

doi:10.1007/s00586-004-0792-3

Cited by 45 publications

(47 citation statements)

References 26 publications

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“…He found that subjects with LBP have less endurance and thus smaller MF slopes during sustained muscle contractions [4][5]7]. However, contradictory results have been subsequently reported [4,[8][9]. Humphrey et al reported that MF slope is not better than chance in predicting LBP in subjects [8].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

Sung¹,

Zürcher²,

Kaufman³

2007

JRRD

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Abstract-In a previous study, we reported that the mean square displacement calculated from the surface electromyography (sEMG) signal of low back muscles exhibits a plateaulike behavior for intermediate times 20 ms < t < 400 ms. This property indicates the existence of correlations in the signal for times much longer than the inverse of the median frequency (MF), which is calculated from the power spectrum 1/ = 1/(100 Hz) = 10 ms, where is the MF. This result suggests the use of methods from nonlinear analysis to characterize sEMG time series. In this study, we applied these techniques to sEMG signals and calculated the time-dependent entropy. The results showed that the entropy of physiological time series from nondisabled control subjects is higher than the entropy from subjects with low back pain (LBP). The entropy reveals properties of the sEMG signal that are not captured by the power spectrum. In turn, this suggests a possible benefit of entropy as a tool for the clinical assessment of LBP. Because the two groups of subjects were not matched by age, the physiological origin of the observed differences between groups could be attributed to either LBP, age, or both. Additional studies with larger sample sizes and age-matched subjects are needed to investigate the relationship between LBP and entropy.

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Section: Introductionmentioning

confidence: 99%

“…However, contradictory results have been subsequently reported [4,[8][9]. Humphrey et al reported that MF slope is not better than chance in predicting LBP in subjects [8]. Thus, despite considerable efforts, a connection between spectral quantities and musculoskeletal pain and/or dysfunction remains elusive.…”

Section: Introductionmentioning

confidence: 99%

Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

Sung¹,

Zürcher²,

Kaufman³

2007

JRRD

View full text Add to dashboard Cite

show abstract

“…Other results indicated that subjects with LBP show less fatigue than healthy subjects (Humphrey et al, 2005;Mannion et al, 2001). Thus, despite considerable efforts by many researchers, a link between MF and musculoskeletal pain/dysfunction remains elusive.…”

Section: Emg Methods For Evaluating Muscle and Nerve Function 134mentioning

confidence: 99%

“…Individuals with better endurance than others exhibit a less precipitous decay rate of the MF (Mannion et al, 1997b). Thus, it would be necessary to compare the results between Shannon entropy levels of the EMG and MF of the spectral quantities following intervention to enhance outcome measurements.Other results indicated that subjects with LBP show less fatigue than healthy subjects (Humphrey et al, 2005;Mannion et al, 2001). Thus, despite considerable efforts by many researchers, a link between MF and musculoskeletal pain/dysfunction remains elusive.…”

mentioning

confidence: 99%

Nonlinear Analysis of Surface Electromyography

Sung¹

2012

EMG Methods for Evaluating Muscle and Nerve Function

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EMG Methods for Evaluating Muscle and Nerve Function 134that the signal must be interpreted as "noise." The noise is due to the interaction between a particular muscle and all other biomechanical "units" of the body. In many cases, the power spectrum follows an algebraic dependence P(f) ~ 1/f. The case =0 corresponds to "white noise" while =2 characterizes diffusive Brownian motion. Therefore, the MF of the EMG power spectrum is sensitive to physiological manifestations of muscular dysfunction as an alternative assessment tool to identify muscle fatigue (Mannion et al., 1997b;Roy et al., 1997). H o w e v e r , t h e r e i s a l a c k o f r e s e a r c h t h a t c o m p a r e s t h i s t o o l w i t h o t h e r n o n l i n e a r measurements based on pain level or dysfunction. During a fatiguing contraction, a compression of the power spectrum of the EMG signal to lower frequencies is typically observed (Lindstrom et al., 1974). This phenomenon is measured during a contraction as a decrease in the MF of the EMG signal. Individuals with better endurance than others exhibit a less precipitous decay rate of the MF (Mannion et al., 1997b). Thus, it would be necessary to compare the results between Shannon entropy levels of the EMG and MF of the spectral quantities following intervention to enhance outcome measurements.Other results indicated that subjects with LBP show less fatigue than healthy subjects (Humphrey et al., 2005;Mannion et al., 2001). Thus, despite considerable efforts by many researchers, a link between MF and musculoskeletal pain/dysfunction remains elusive. Moreover, the surface EMG is not a scientifically acceptable tool for the diagnosis of pain/dysfunction, and further studies are recommended to assess the specificity and sensitivity of surface EMG (Pullman et al., 2000). Therefore, a clinical diagnosis and evaluation of LBP is still elusive, and the efficacy of therapeutic intervention and assessment for LBP cannot be tested reliably. The power spectrum analysis provides an objective and noninvasive assessment of muscle function since EMG changes are associated with fatigue (De Luca, 1984;Mannion et al., 1997a). However, contradictory results have been reported in studies using EMG as an outcome measure. The power spectrum has a limited dynamic range, and the change of the MF does not reflect such long-time correlations. New methods must be designed to capture biologically important characteristics from noisy time series. Researchers using nonlinear time series analysis have developed several mathematical tools to reveal the presence of power-law time correlations. Investigations of physiologic time series have led to the understanding that some degree of noise is necessary for the proper functioning of biological systems (Belair et al., 1995b;Glass, 2001;Strogatz, 2001). These systems must respond to external stimuli that may vary both in strength and time scale by many orders of magnitude. The "degree of irregularity" of time series can be quantified by computing the (information) entropy of...

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“…The paper by Humphrey et al [17] deals with the value of electromyography of the lumbar paraspinal muscle in discriminating between chronic low back pain sufferers and normal subjects. It has been established by the unit publishing this work, (who have a long involvement in the field) and by others, that EMGs in the spinal muscles differ between people with low back pain, and those without.…”

Section: Spinal Stenosismentioning

confidence: 99%

The Michel Benoist and Robert Mulholland yearly European Spine Journal review

Mulholland¹

2006

Eur Spine J

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The value of electromyography of the lumbar paraspinal muscles in discriminating between chronic-low-back-pain sufferers and normal subjects

Cited by 45 publications

References 26 publications

Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

Comparison of spectral and entropic measures for surface electromyography time series: A pilot study

Nonlinear Analysis of Surface Electromyography

The Michel Benoist and Robert Mulholland yearly European Spine Journal review

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