Abstract
During dynamic or sustained isometric contractions, bursts of muscle activity appear in the electromyography (EMG) signal. Theoretically, these bursts of activity likely occur because motor units are constrained to fire temporally close to one another and thus the impulses are “clustered” with short delays to elicit bursts of muscle activity. The purpose of this study was to investigate whether a sequence comprised of “clustered” motor unit action potentials (MUAP) can explain spectral and amplitude changes of the EMG during a simulated motor task. This question would be difficult to answer experimentally and thus, required a model to study this type of muscle activation pattern. To this end, we modeled two EMG signals, whereby a single MUAP was either convolved with a randomly distributed impulse train (EMG-rand) or a “clustered” sequence of impulses (EMG-clust). The clustering occurred in windows lasting 5–100 ms. A final mixed signal of EMG-clust and EMG-rand, with ratios (1:1–1:10), was also modeled. A ratio of 1:1 would indicate that 50% of MUAP were randomly distributed, while 50% of “clustered” MUAP occurred in a given time window (5–100 ms). The results of the model showed that clustering MUAP caused a downshift in the mean power frequency (i.e., ~30 Hz) with the largest shift occurring with a cluster window of 10 ms. The mean frequency shift was largest when the ratio of EMG-clust to EMG-rand was high. Further, the clustering of MUAP also caused a substantial increase in the amplitude of the EMG signal. This model potentially explains an activation pattern that changes the EMG spectra during a motor task and thus, a potential activation pattern of muscles observed experimentally. Changes in EMG measurements during fatiguing conditions are typically attributed to slowing of conduction velocity but could, per this model, also result from changes of the clustering of MUAP. From a clinical standpoint, this type of muscle activation pattern might help describe the pathological movement issues in people with Parkinson’s disease or essential tremor. Based on our model, researchers moving forward should consider how MUAP clustering influences EMG spectral and amplitude measurements and how these changes influence movements.
Highlights
During dynamic and fatiguing contractions to volitional failure, bursts of muscle activation appear in the electromyography (EMG) signal
Using surface EMG, further difficulties arise because of distorted signals being recorded due to volume conductor effects, amplitude cancellation, or cross-talk (Solomonow et al, 1994; Winter et al, 1994; Farina et al, 2004b; Keenan et al, 2005, 2006; von Tscharner, 2010; De Luca et al, 2012; Mesin, 2013). Since these distortion effects are present in experimental time series EMG, researchers often choose to model the EMG signal to understand the underlying sequence of activation to create a muscle activity pattern, while controlling for factors such as location and orientation of muscle fibers, motor unit recruitment patterns, rate coding, conduction velocities and/or shapes of motor unit action potentials (MUAP) and how these factors contribute to an EMG signal (Fuglevand et al, 1993a; Farina and Merletti, 2000; Stegeman et al, 2000; Farina et al, 2002)
The purpose of this study is to increase the understanding of the effect of motor unit action potential clustering (MUAP arriving between >5 ms and
Summary
During dynamic and fatiguing contractions to volitional failure, bursts of muscle activation appear in the electromyography (EMG) signal. Using surface EMG, further difficulties arise because of distorted signals being recorded due to volume conductor effects, amplitude cancellation, or cross-talk (Solomonow et al, 1994; Winter et al, 1994; Farina et al, 2004b; Keenan et al, 2005, 2006; von Tscharner, 2010; De Luca et al, 2012; Mesin, 2013) Since these distortion effects are present in experimental time series EMG, researchers often choose to model the EMG signal to understand the underlying sequence of activation to create a muscle activity pattern, while controlling for factors such as location and orientation of muscle fibers, motor unit recruitment patterns, rate coding, conduction velocities and/or shapes of MUAP and how these factors contribute to an EMG signal (Fuglevand et al, 1993a; Farina and Merletti, 2000; Stegeman et al, 2000; Farina et al, 2002)
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