Patterns of motor recruitment can be determined using surface EMG
Introduction
An electromyogram (EMG) encodes information about the active motor units within its detection zone. The shape and conduction velocity of the motor unit action potentials, MUAPs, are determined by the intrinsic properties of the muscles fibres (Buchthal et al., 1973) that make up each motor unit and these form the basis for the spectral properties in an EMG. However, volume conduction effects occur through the soft-tissues between the bioelectric source and the recording electrodes and these effects alter the temporal and spatial characteristics of the surface EMG (sEMG) in a complex manner (Blok et al., 2002, Roeleveld et al., 1997). Nonetheless, each MUAP occurs at a distinct time and leaves characteristic spectral components in the sEMG and these can be resolved using time–frequency signal processing techniques such as wavelet analysis (Karlsson et al., 2000, von Tscharner, 2000). A common observation of many recent studies is that separate bursts of myoelectric activity occur with distinct spectral properties, and these can occur within a gait cycle (von Tscharner, 2000, Wakeling, 2004), can vary between locomotor conditions (Wakeling et al., 2006, Wakeling, 2004), and respond specifically to external interventions such as foot orthotics (Mündermann et al., 2006, Wakeling and Liphardt, 2006). This study uses modelled sEMG to address whether such patterns can result from different patterns of motor unit recruitment within a muscle.
Alterations in the myoelectric spectra can result from a number of physiological processes such as changes in muscle temperature (Stålberg, 1966), fatigue status (Kleine et al., 2001, Petrofsky, 1979) and the length of the muscle fibres (Doud and Walsh, 1995). Additionally, higher-frequency source spectra are generated by faster motor units due to the faster conduction velocity of their MUAPs. Careful experimental design can minimise the influence of the physiological processes on the sEMG spectra such as presenting conditions in repeated randomised blocks to minimise progressive bias and using ultrasound to monitor fascicle lengths; nevertheless distinct high- and low-frequency components in the EMG have been recorded despite such measures (Wakeling et al., 2006). It has been suggested that these spectral characteristics are the result of altered recruitment patterns between different motor units (Mündermann et al., 2006, von Tscharner, 2000, Wakeling and Rozitis, 2004, Wakeling et al., 2006). In order to determine whether different recruitment patterns can be detected using sEMG it is necessary to test simulated models of EMG where the precise recruitment pattern is both known and varied. The extent of such tests has previously been limited to varying the way that increases in muscle force are achieved by either recruiting additional MUs or increasing the firing rate of already active units (Farina et al., 2002) within a system of orderly recruitment. Changes in sEMG spectra have been reported when the stimulation pattern was changed in man with stretch reflexes or electrical cutaneous stimulation (Wakeling and Rozitis, 2004), and it was inferred that this reflected different populations of MUs being active. However, in order to confirm such findings it is necessary to simulate known variations in motor recruitment and test the resulting sEMG. To date there have not been tests to evaluate how the sEMG changes in response to different proportions of fast and slow motor units MUs being active within a muscle.
The basic pattern of recruitment of motor units within a motor unit pool is of “orderly recruitment” whereby the motor units are recruited in the order of their excitability (Henneman et al., 1965a, Henneman et al., 1965b). Coupled to this the slower motor units have the lowest excitation thresholds and thus will be recruited before the faster motor units (Andreassen and Arendt-Nielsen, 1987, Garnett et al., 1978). This pattern of orderly recruitment can be modulated by, for instance, the action of interneurones within the spinal cord (Broman et al., 1985, Kanda et al., 1977, Nishimaru et al., 2006, Ryall et al., 1972). Additionally, motor units within a muscle may be members of different pools that can be activated at different times (English, 1984). Thus there are a number of different mechanisms by which the balance of motor units being recruited can be altered. It is beyond the scope of this paper to investigate the mechanisms by which recruitment patterns can be altered, however, this study addresses the functional outcome of such alterations: can sEMG be used to detect differences in the recruitment of the motor units within a muscle?
The purpose of this study was to generate a series of simulated surface EMG signals using a model of a muscle activated with different recruitment strategies and incorporating the volume conductor effects associated with sEMG. We tested the hypothesis that strategies recruiting predominantly higher-threshold motor units, with faster conduction velocities for their MUAPs, would generate higher-frequency signals in the sEMG.
Section snippets
Muscle model
A model was constructed of the medial gastrocnemius (MG) muscle as this muscle has distinct bursts of high- and low-frequency myoelectric spectra reported for both running (Wakeling, 2004) and cycling (von Tscharner, 2000, Wakeling et al., 2006). The model approximated the size of the muscle in man (Wickiewicz et al., 1983) and was represented by a cylindrical muscle belly of radius 1.79 cm and length 15 cm. Tendons at each end were 5 and 3 cm (distal and proximal) and varied in position with a
Results
The results for the models with gain = 0 represent an orderly recruitment of the motor units whereby the progressively higher-threshold MUs were sequentially recruited during the 5 s ramp. The sEMG traces showed an increase in the raw signal, and the time–frequency plots (Fig. 1c) showed a shift to higher frequencies that mainly occurred during the first 2 s. These features were reflected by the rapid initial increase in EMG intensity followed by a more gradual increase to the end of the ramp (Fig.
Discussion
This study shows general patterns of recruitment can be distinguished and thus that the spectral properties of the sEMG respond to the activity of different populations of MUs within a muscle. It should be noted that it is not possible to uniquely identify the activity of individual MUs using the sEMG (Farina et al., 2002, Farina et al., 2004). However, the techniques presented here detect whether variations occur in the populations of active MUs within a muscle. Alternative patterns of MU
Acknowledgement
I thank Emma Hodson-Tole for many interesting discussions.
James M. Wakeling is an Assistant Professor at the School of Kinesiology at Simon Fraser University. He received an undergraduate degree (1992) in Natural Sciences and PhD. (1996) in Zoology at Cambridge University. His primary research interests are the mechanical function of whole muscles during locomotion.
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James M. Wakeling is an Assistant Professor at the School of Kinesiology at Simon Fraser University. He received an undergraduate degree (1992) in Natural Sciences and PhD. (1996) in Zoology at Cambridge University. His primary research interests are the mechanical function of whole muscles during locomotion.