Decomposition Of Surface Electromyographic Signals Research Articles

A comparison of techniques for verifying the accuracy of precision decomposition-derived relationships between motor unit firing rates and recruitment thresholds from surface EMG signals.

Approaches for validating motor unit firing times following surface electromyographic (EMG) signal decomposition with the precision decomposition III (PDIII) algorithm have not been agreed upon. Two approaches have been common: (1) "reconstruct-and-test" and (2) spike-triggered averaging (STA). We sought to compare motor unit results following the application of these approaches. Surface EMG signals were recorded from the vastus lateralis of 13 young males performing trapezoidal, isometric knee extensions at 50% and 80% of maximum voluntary contraction (MVC) force. The PDIII algorithm was used to quantify motor unit firing rates. Motor units were excluded using eight combinations of the reconstruct-and-test approach with accuracy thresholds of 0, 90, 91, and 92% with and without STA. The mean firing rate versus recruitment threshold relationship was minimally affected by STA. At 80% MVC, slopes acquired at the 0% accuracy threshold were significantly greater (i.e., less negative) than when 91% (p = .010) and 92% (p = .030) accuracy thresholds were applied. The application of STA has minimal influence on surface EMG signal decomposition results. Stringent reconstruct-and-test accuracy thresholds influence motor unit-derived relationships at high forces, perhaps explained through the increased presence of large motor unit action potentials. Investigators using the PDIII algorithm can expect negligible changes in motor unit-derived linear regression relationships with the application of secondary validation procedures.

Vastus Lateralis Motor Unit Recruitment Thresholds Are Compressed towards Lower Forces in Older Men.

Aging results in adaptations which may affect the control of motor units. We sought to determine if younger and older men recruit motor units at similar force levels. Cross-sectional, between-subjects design. Controlled laboratory setting. Twelve younger (age = 25 ± 3 years) and twelve older (age = 75 ± 8 years) men. Participants performed isometric contractions of the dominant knee extensors at a force level corresponding to 50% maximal voluntary contraction (MVC). Bipolar surface electromyographic (EMG) signals were detected from the vastus lateralis. A surface EMG signal decomposition algorithm was used to quantify the recruitment threshold of each motor unit, which was defined as the force level corresponding to the first firing. Recruitment thresholds were expressed in both relative (% MVC) and absolute (N) terms. To further understand age-related differences in motor unit control, we examined the mean firing rate versus recruitment threshold relationship at steady force. MVC force was greater in younger men (p = 0.010, d = 1.15). Older men had lower median recruitment thresholds in both absolute (p = 0.005, d = 1.29) and relative (p = 0.001, d = 1.53) terms. The absolute recruitment threshold range was larger for younger men (p = 0.020; d = 1.02), though a smaller difference was noted in relative terms (p = 0.235, d = 0.50). These findings were complimented by a generally flatter slope (p = 0.070; d = 0.78) and lower y-intercept (p = 0.009; d = 1.17) of the mean firing rate versus recruitment threshold relationship in older men. Older men tend to recruit more motor units at lower force levels. We speculate that recruitment threshold compression may be a neural adaptation serving to compensate for lower motor unit firing rates and/or denervation and subsequent re-innervation in aged muscle.

A method for editing motor unit potential trains obtained by decomposition of surface electromyographic signals

Rather than discarding motor unit potential trains (MUPTs) because they do not meet 100% validity criteria, we describe and evaluate a novel editing routine that preserves valid discharge times, based on decreasing shape variability (variance ratio, VR) within a MUPT. The error filtered estimation (EFE) algorithm is then applied to the remaining ‘high confidence’ discharge times to estimate inter-discharge interval (IDI) statistics. Decomposed surface EMG data from the flexor carpi radialis recorded from 20 participants during 60% MVC wrist flexion was used. There were two levels of denoising criteria (relaxed and strict) criteria for removing MUPs to decrease the VR and increase the signal-to-noise ratio (SNR) of a MUPT. In total, VR decreased 24.88% and SNR increased 6.0% (p’s < 0.05). The MUP template peak-to-peak (P-P) amplitude and P-P duration were dependent on the level of denoising (p’s < 0.05). The standard error of the estimate (SEE) of the mean IDI before and after editing using the relaxed criteria (3.2% versus 3.69%), was very similar (p > 0.05). The same was true for the SEE between denoising criteria, which increased only to 5.14% for the strict criteria (p > 0.05). Editing the MUPTs resulted in a significant decrease in MUP shape variability and in the measures extracted from the MUP templates, with trivial differences between the SEE of the mean IDI between the edited and unedited MUPTs.

Is the Mean Firing Rate versus Recruitment Threshold Relationship Linear?

Advances in surface electromyographic (EMG) signal decomposition now allow investigators to analyze firing rate data for 20-50 motor units per contraction. To simplify data interpretation, some investigators have relied on group mean analysis of the mean firing rate versus recruitment threshold relationship. It is unclear, however, whether this association is consistently linear. PURPOSE: To determine whether the motor unit mean firing rate versus recruitment threshold relationship is strongest when analyzed via linear, quadratic, or cubic regression. METHODS: Twenty-one men (mean ± SD age = 24 ± 4 years) participated in this study. After determining maximal voluntary contraction (MVC) force of the dominant knee extensors, participants performed trapezoidal isometric contractions at 50% MVC by tracing a visual template displayed on a monitor directly in front of them. Participants were instructed to increase force from 0 to 50% in five seconds, maintain 50% MVC for 15 seconds, and decrease force from 50% to 0 in five seconds. Bipolar surface EMG signals were recorded from the vastus lateralis during each contraction. A surface EMG signal decomposition algorithm was used to calculate mean firing rate and recruitment threshold of each detected motor unit. Motor units with decomposition accuracy levels <90% were discarded. Polynomial regression was used to determine if each mean firing rate versus recruitment threshold relationship was best fit with a linear, quadratic, or cubic model. Data were interpreted on an individual participant basis. RESULTS: Statistically significant (p <.05) moderate to strong (r2=.599-.964) linear relationships existed for all 21 participants. Fourteen of the 21 participants demonstrated relationships that were best fit with a linear model (r2=.599-.964). Of the remaining seven participants, five were best fit with a quadratic model (r2=.864-.953) and two were best fit with a cubic model (r2=.977-.989). CONCLUSION: While moderate to strong linear relationships were found between the firing rate of motor units and their recruitment thresholds for all participants, in certain cases the strength of the association was enhanced when analyzed via a non-linear model. Our findings provide further support for the need to examine motor unit data on a participant-by-participant basis.

The Motor Unit Mean Firing Rate versus Recruitment Threshold Relationship is Unaffected by Short-Term Disuse

The loss of muscle strength during periods of disuse is rapid. Some investigators have postulated that these changes are due to neural, rather than muscular, adaptations. It is unclear, however, if short-term immobilization of the knee joint affects the voluntary control of motor units. PURPOSE: To determine whether the slope and y-intercept of the motor unit mean firing rate versus recruitment threshold relationship is altered by 72 hours of disuse. METHODS: Fifteen healthy females (mean ± SD age = 21 ± 2 years, body mass index [BMI] = 23.1 ± 2.3 kg/m2) voluntarily underwent left knee joint unloading via ambulating on crutches and use of a brace. The brace was worn at all times except during sleep, and compliance was confirmed via accelerometers secured around both ankles. Following two extensive familiarization sessions at the laboratory, testing was performed immediately prior to immobilization (PRE) and 72 hours later (POST). During both testing sessions, participants performed trapezoidal isometric contractions at a torque level corresponding to 50% of their maximal voluntary contraction (MVC). Participants were instructed to increase torque from 0 to 50% in five seconds, maintain 50% MVC for 15 seconds, and decrease torque from 50% to 0 in five seconds. Bipolar surface electromyographic (EMG) signals were recorded from the vastus lateralis. A surface EMG signal decomposition algorithm was used to calculate the mean firing rate (pulses per second [pps]) and recruitment threshold (% MVC) of each detected motor unit. Motor units with decomposition accuracy levels < 90% were discarded. Linear regression was then used to quantify the slope (pps/% MVC) and y-intercept (pps) of each relationship. RESULTS: Immobilization had no influence on the linear slope coefficient for the mean firing rate versus recruitment threshold relationship (PRE = -0.362 ± 0.127, POST = -0.399 ± 0.108 pps/%MVC; p = 0.413, d = 0.218). Similarly, no change in the y-intercept was observed (PRE = 23.2 ± 2.8, POST = 23.3 ± 4.0 pps; p = 0.972, d = 0.009) CONCLUSION: Our findings demonstrated that 72 hours of knee joint immobilization in healthy females had little influence on vastus lateralis motor unit control during submaximal contractions. Funding: The De Luca Foundation and the UCF Office of Research’s Advancement of Early Career Researchers program

Motor Unit Interpulse Intervals During High Force Contractions.

We examined the means, medians, and variability for motor-unit interpulse intervals (IPIs) during voluntary, high force contractions. Eight men (mean age = 22 years) attempted to perform isometric contractions at 90% of their maximal voluntary contraction force while bipolar surface electromyographic (EMG) signals were detected from the vastus lateralis and vastus medialis muscles. Surface EMG signal decomposition was used to determine the recruitment thresholds and IPIs of motor units that demonstrated accuracy levels ≥ 96.0%. Motor units with high recruitment thresholds demonstrated longer mean IPIs, but the coefficients of variation were similar across all recruitment thresholds. Polynomial regression analyses indicated that for both muscles, the relationship between the means and standard deviations of the IPIs was linear. The majority of IPI histograms were positively skewed. Although low-threshold motor units were associated with shorter IPIs, the variability among motor units with differing recruitment thresholds was comparable.

Decomposition of surface EMG signals from cyclic dynamic contractions.

Over the past 3 decades, various algorithms used to decompose the electromyographic (EMG) signal into its constituent motor unit action potentials (MUAPs) have been reported. All are limited to decomposing EMG signals from isometric contraction. In this report, we describe a successful approach to decomposing the surface EMG (sEMG) signal collected from cyclic (repeated concentric and eccentric) dynamic contractions during flexion/extension of the elbow and during gait. The increased signal complexity introduced by the changing shapes of the MUAPs due to relative movement of the electrodes and the lengthening/shortening of muscle fibers was managed by an incremental approach to enhancing our established algorithm for decomposing sEMG signals obtained from isometric contractions. We used machine-learning algorithms and time-varying MUAP shape discrimination to decompose the sEMG signal from an increasingly challenging sequence of pseudostatic and dynamic contractions. The accuracy of the decomposition results was assessed by two verification methods that have been independently evaluated. The firing instances of the motor units had an accuracy of ∼90% with a MUAP train yield as high as 25. Preliminary observations from the performance of motor units during cyclic contractions indicate that during repetitive dynamic contractions, the control of motor units is governed by the same rules as those evidenced during isometric contractions. Modifications in the control properties of motoneuron firings reported by previous studies were not confirmed. Instead, our data demonstrate that the common drive and hierarchical recruitment of motor units are preserved during concentric and eccentric contractions.

Preferred sensor sites for surface EMG signal decomposition

Technologies for decomposing the electromyographic (EMG) signal into its constituent motor unit action potential trains have become more practical by the advent of a non-invasive methodology using surface EMG (sEMG) sensors placed on the skin above the muscle of interest (De Luca et al 2006 J. Neurophysiol. 96 1646–57 and Nawab et al 2010 Clin. Neurophysiol. 121 1602–15). This advancement has widespread appeal among researchers and clinicians because of the ease of use, reduced risk of infection, and the greater number of motor unit action potential trains obtained compared to needle sensor techniques. In this study we investigated the influence of the sensor site on the number of identified motor unit action potential trains in six lower limb muscles and one upper limb muscle with the intent of locating preferred sensor sites that provided the greatest number of decomposed motor unit action potential trains, or motor unit yield. Sensor sites rendered varying motor unit yields throughout the surface of a muscle. The preferred sites were located between the center and the tendinous areas of the muscle. The motor unit yield was positively correlated with the signal-to-noise ratio of the detected sEMG. The signal-to-noise ratio was inversely related to the thickness of the tissue between the sensor and the muscle fibers. A signal-to-noise ratio of 3 was found to be the minimum required to obtain a reliable motor unit yield.