Spectral and entropy changes for back muscle fatigability following spinal stabilization exercises

Lee, Tae Ro; Kim, Yoon Hyuk; Sung, Paul S.

doi:10.1682/jrrd.2009.07.0088

Cited by 17 publications

(9 citation statements)

References 39 publications

(62 reference statements)

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“…However, it is necessary to determine www.intechopen.com whether the observed Shannon (information) entropy, as compared with MF, was able to differentiate fatigability of the thoracic and lumbar parts of the erector spinae muscle following the intervention. Previously, our lab investigated tools for evaluating back muscle fatigability after spinal stabilization exercises in participants with chronic LBP (Lee et al, 2010;Sung et al, 2005;Sung et al, 2008a;Sung et al, 2010). The results of our previous study indicated that Shannon entropy might be a valuable tool to measure the differences of outcomes following the exercise intervention.…”

Section: Sensitivity Of Surface Electromyography Analysismentioning

confidence: 89%

“…As reported in our previous studies, the complexity of physiologic time series is a sensitive measure for muscle fatigability (Lee et al, 2010). However, it is necessary to determine www.intechopen.com whether the observed Shannon (information) entropy, as compared with MF, was able to differentiate fatigability of the thoracic and lumbar parts of the erector spinae muscle following the intervention.…”

Section: Sensitivity Of Surface Electromyography Analysismentioning

confidence: 92%

“…Nonlinear analysis using fractal geometry and random walks theory proved to be useful in the analysis of a variety of time series such as correlations between global temperatures and solar activity (Scafetta & West, 2003), earthquake statistics (Scafetta & West, 2004), human heartbeat (Ivanov et al, 1999), and shapes of red cells under flow stress (Korol & Rasia, 2003). Recently, nonlinear time series generated by the back muscles' electrical activity was investigated between subjects with and without LBP motivated by the need to develop an evaluation tool for LBP (Lee et al, 2010;Sung et al, 2005;Sung et al, 2007a;Sung et al, 2010). Using random walk concepts, Collins and De Luca have studied the erratic motion of the center of pressure of a standing human body (Collins & De Luca, 1994;Collins & De Luca, 1995).…”

Section: Entropy Of Electromyographymentioning

confidence: 99%

“…The original study linking LBP with fatigue was presented by De Luca (De Luca, 1984) who found that subjects with LBP have less endurance, and thus smaller MF slopes, during sustained muscle contractions (Merletti et al, 1999;Roy et al, 1989). However, contradictory results have subsequently been reported and have shown that MF slope is not better than chance in predicting LBP (Humphrey et al, 2005;Lee et al, 2010;Mannion et al, 2001). Thus, a connection between spectral quantities and musculoskeletal pain/dysfunction remains elusive despite considerable efforts.…”

Section: Reliability Of Surface Electromyography Analysismentioning

confidence: 99%

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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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Section: Sensitivity Of Surface Electromyography Analysismentioning

confidence: 89%

Section: Sensitivity Of Surface Electromyography Analysismentioning

confidence: 92%

Section: Entropy Of Electromyographymentioning

confidence: 99%

Section: Reliability Of Surface Electromyography Analysismentioning

confidence: 99%

See 2 more Smart Citations

Nonlinear Analysis of Surface Electromyography

Sung¹

2012

EMG Methods for Evaluating Muscle and Nerve Function

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“…This plateau value of S(t) was referred to as the entropy. The detailed entropy calculation process is fully described in previous studies [3–5,25]. Our previous data were partially used in this study in order to monitor the effects of the exercise intervention.…”

Section: Methodsmentioning

confidence: 99%

A comparison of pain level and entropy changes following core stability exercise intervention

2011

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SummaryBackgroundAs reported in our previous studies, the complexity of physiologic time series is a sensitive measure of muscle fatigability. This study compared the differences between 2 different analyses following 4 weeks of core stability exercises (CSE) in subjects with and without chronic low back pain (LBP). We examined whether the observed Shannon (information) entropy, as compared with median frequency (MF), was able to differentiate fatigability of the thoracic and lumbar parts of the erector spinae (ES) muscles following the intervention.Material/MethodsIn total, 32 subjects participated in this study. There were 13 subjects in the CSE intervention group (average age 50.4±9.1 years) and 19 subjects in the control group (average age 46.6±9.1 years). The CSE group performed the specific exercise intervention, but the control group was asked to maintain their current activity and/or exercise levels. The endurance of the back muscles was determined by using a modified version of the isometric fatigue test as originally introduced by Sorensen.ResultsPain level decreased significantly for all subjects (F=25.29, p=0.001), but there was no difference between groups (F=0.42, p=0.52). The MF was not different between groups following treatment (F=0.81, p=0.37). Although there was no entropy level changes following treatment (F=0.01, p=0.93), the interactions between muscles and groups following treatment were significant (F=7.25, p=0.01). The entropy level decreased in both thoracic ES muscles following intervention in the exercise group, while remaining the same in the control group.ConclusionsAlthough the change in pain level was not different between groups, the Shannon entropy measure more sensitively differentiated the exercise intervention than did MF. In addition, the results also suggested that complexity is related to muscle fatigue, which corresponds to the values of entropy between groups. Further studies are needed to investigate the effectiveness of nonlinear time series of EMG data for fatigability.

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