Electromyogram power spectra frequencies associated with motor unit recruitment strategies

Solomonow, Moshe; Baten, Christian T.M.; Smit, J.P.A.; Baratta, R.; Hermens, Hermie; D’Ambrosia, Robert; Shoji, Hiroshi

doi:10.1152/jappl.1990.68.3.1177

Cited by 280 publications

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“…The limitations discussed in the preceding paragraph are sometimes considered to be negligible (26). A study often cited to support this assertion is the observation by Solomonow et al (23) that orderly stimulation of motor units in the cat gastrocnemius muscle via nerve electrodes gave rise to linearly increasing median frequency of the intramuscular EMG signal. As noted by the authors, however, "attempts to extend the findings .…”

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confidence: 99%

“…. to data obtained from voluntary contractions require the consideration of several secondary but not unimportant factors" (23). These factors, which are common to other animal studies (17,28), include differences between electrically evoked EMG signals and those detected during voluntary contractions, intramuscular (or over the muscle fascia) and surface recordings, and recordings of activity by discrete and mixed populations of muscle fiber types.…”

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confidence: 99%

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Counterpoint: Spectral properties of the surface emg do not provide information about motor unit recruitment and muscle fiber type

Farina

2008

Journal of Applied Physiology

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The surface electromyogram (EMG) comprises the sum of the electrical contributions made by the active motor units to the interference signal detected by electrodes placed on the skin overlying the muscle. Because it provides a global measure of motor unit activity, this signal is a valuable tool for assessing the level of muscle activation.The analysis of the surface EMG in the frequency domain has been used in some instances, such as fatiguing contractions, to provide an indirect measure of the relative changes in average muscle fiber conduction velocity (19) or in the profile of the intracellular action potential (6). In addition to these applications, the spectral characteristics of the surface EMG have been used to infer motor unit recruitment strategies and the fiber-type composition of a muscle. Higher characteristic spectral frequencies are associated with a greater proportion of type II fibers (20, 21) and changes in EMG spectral variables are used to infer the recruitment of faster or slower motor units (3,26).The capacity of surface EMG spectral properties to provide information about motor unit recruitment or the proportion of fiber types is based on the rationale that higher threshold (and type II) motor units produce surface action potentials with larger relative energy at higher frequencies than lower threshold (and type I) motor units.Because in these applications the main determinant of the frequency content of an action potential is assumed to be its conduction velocity (28), the crucial issue in this debate is the validity of two assumptions: 1) average conduction velocity of the active motor units is related to fiber-type proportions, and 2) changes in the spectral properties of the surface EMG are associated with changes in average conduction velocity.Fiber-type composition and average muscle fiber conduction velocity. There are several physiological details that confound this association. First, the two main fiber types do not have distinct conduction velocities in humans, but rather conduction velocity has a continuous distribution with a single peak (25). Second, average conduction velocity of muscle fiber action potentials can differ among populations of motor units due to differences in fiber diameter and independent of changes in fiber-type proportions (4). Third, the number of muscle fibers innervated by a motor unit has a skewed distribution. For example, the first dorsal interosseus muscle comprises an equal number of type I and II fibers, but ϳ84% of the motor units have slow contraction times and are fatigue resistant (7). Fourth, the conduction velocity of muscle fiber action potentials can change by ϳ20% with variation in discharge rate (24) and the range of discharge rates varies with recruitment threshold in a population of motor units (2). Fifth, the conduction velocity of muscle fiber action potentials varies with fiber length (15).Experimental evidence: fiber-type composition vs. muscle fiber conduction velocity. The significance of the physiological limitations discussed in...

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Counterpoint: Spectral properties of the surface emg do not provide information about motor unit recruitment and muscle fiber type

Farina

2008

Journal of Applied Physiology

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“…The effect of the firing rate, however, appears only in the low frequency range (0-40 Hz), so the MNF and MDF methods are weakly dependent on the firing rate 16,17 . Previous theoretical 17,18 and experimental 19 studies report that although the firing statistics of active MUs were changed, they do not significantly affect the MNF and MDF.…”

Section: Introductionmentioning

confidence: 87%

The effects of the force of contraction and elbow joint angle on mean and median frequency analysis for muscle fatigue evaluation

Thongpanja¹,

Phinyomark²,

Hu³

et al. 2015

ScienceAsia

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Mean frequency (MNF) and median frequency (MDF) are often used to assess muscular fatigue from surface electromyography (sEMG) signals. To determine muscle fatigue at different levels of isometric and dynamic contractions, it is necessary to know the relationship between the force of contraction and the MNF/MDF methods. Despite numerous studies, no unique answer has been found. This study re-examines this relationship by investigating 19 subjects and performing isometric and dynamic contractions over a full elbow range of motion measured from the biceps brachii muscle. We obtained the following key results: (i) a strong positive relationship between MNF/MDF methods and the force of contraction was found frequently during isometric contractions at an angle less than 90°and during dynamic contractions in 3 subjects (16%); (ii) a strong negative relationship during isometric contractions at an angle greater than 90°and during dynamic contractions in 8 subjects (42%), and (iii) no strong linear relationship during isometric contractions for all the angles and during dynamic contractions in 8 subjects (42%). After possible causes for these conflicting results are discussed, the study then investigates if it is possible to use anthropometric variables of subjects as the criteria to create homogeneous subgroups of relationships. A number of anthropometric variables were used to separate the subgroups, i.e., the forearm and biceps circumferences for males and the hand and shoulder breadths for females.

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“…Our previous study results indicated that the measurement of back muscle fatigability, especially the erector spinae muscle, indicated that the entropy analysis was a more reliable measure than the power spectral analysis (Sung et al, 2008b). It is well known that the observed EMG signal depends on anatomical factors, such as muscle geometry, subcutaneous fat, gender, and other confounding factors (Lindstrom et al, 1974;Mannion et al, 1997c;Pullman et al, 2000;Roy et al, 1997;Solomonow et al, 1990). As a result, observed quantities from EMG signals show considerable variations even among a group of healthy subjects.…”

Section: Reliability Of Surface Electromyography Analysismentioning

confidence: 97%

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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Electromyogram power spectra frequencies associated with motor unit recruitment strategies

Cited by 280 publications

References 0 publications

Counterpoint: Spectral properties of the surface emg do not provide information about motor unit recruitment and muscle fiber type

Counterpoint: Spectral properties of the surface emg do not provide information about motor unit recruitment and muscle fiber type

The effects of the force of contraction and elbow joint angle on mean and median frequency analysis for muscle fatigue evaluation

Nonlinear Analysis of Surface Electromyography

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