Analysis Of Surface Electromyography Signals Research Articles

Recurrence quantification analysis of surface electromyographic signal: Sensitivity to potentiation and neuromuscular fatigue

This study aimed to assess the capacity of recurrence quantification analysis (RQA) to detect potentiation and to determine the fatigue components to which RQA is sensitive. Fifteen men were divided in two groups [8 endurance-trained athletes (END) and 7 power-trained athletes (POW)]. They performed a 10-min intermittent (5 s contraction, 5 s rest) knee extension exercise at 50% of their maximal voluntary isometric contraction. Muscular fatigue and potentiation were evaluated with neurostimulation technique. Mechanical (peak torque, Pt) and electrophysiological (M-wave) responses following electrical stimulation of the femoral nerve were measured at rest and every 10 s throughout exercise. Vastus lateralis muscle activity (root mean square, RMS) was recorded during each contraction, and RMS was normalized to M-wave area (RMS/M). During contraction, muscle activity was analyzed with RQA to obtain the percentage of determinism (%Det). At the beginning of exercise, a significant Pt increase (+52%, P < 0.001) was observed in both groups, indicating potentiation. At this time, %Det remained constant in both groups, indicating that RQA did not detect potentiation. Thereafter, Pt decreased in POW from 5 min 30 s of exercise (−30%, P < 0.001), reflecting impairment in excitation–contraction coupling, and %Det increased from 3 min 30 s ( P < 0.01). In END, Pt remained high and %Det was unchanged. These two results indicated that RQA detected the peripheral component of fatigue. Conversely, RQA did not detect central adaptation to fatigue since %Det remained constant when a significant increase in RMS/M ( P < 0.01) appeared in END.

Fractal analysis of surface electromyography signals: A novel power spectrum-based method

This paper presents a novel power spectrum-based method for fractal analysis of surface electromyography signals. This method, named the bi-phase power spectrum method, provides a bi-phase power-law which represents a multi-scale statistically self-affine signal. This form of statistical self-affinity provides an accurate approximation for stochastic signals originating from a strong non-linear combination of a number of similar distributions, such as surface electromyography signals which are formed by the summation of a number of single muscle fiber action potentials. This power-law is characterized by a set of spectral indicators, which are related to distributional and geometrical characteristics of the electromyography signal’s interference pattern. These novel spectral indicators are capable of sensing the effects of motor units’ recruitment and shape separately by exploiting the geometry of the interference pattern. The bi-phase power spectrum method is compared to geometrical techniques and the 1/ f α approach for fractal analysis of electromyography signals. The extracted indicators using the bi-phase power spectrum method are evaluated in the context of force and joint angle and the results of a human study are presented. Results demonstrate that the bi-phase power spectrum method provides reliable information, consisting of components capable of sensing force and joint angle effects separately, which could be used as complementary information for confounded conventional measures.

Factors affecting the turns analysis of the interference EMG signal

This study was performed to evaluate the relative significance of changes typical for muscle fatigue on quantitative parameters obtained from turns analysis of simulated intramuscular and surface interference electromyographic (EMG) signals. Effects of reduction of firing rate of motor units (MUs) and changes of intracellular action potential (IAP) profile along active fibers were analyzed. A new analytic function was proposed to simulate changes in IAP shape at different stages of muscle fatigue. In intramuscular EMG, both the decrease in firing rate of MUs and the changes in IAP profile led to reduction in the number of turns per second (NTs) and mean turn amplitude (MTA). The development of fatigue and especially the changes in IAP profile could explain why NTs increased up to only about 50% of maximal voluntary contraction, and remained unchanged above that level of efforts or even decreased. These effects should be especially pronounced in patients with myopathy whose IAP and muscle fatigability are expected to be abnormal. In surface EMG, the MTA increased considerably with fatigue; the sensitivity of NTs to reduction in firing rate (or number of discharges) was low. Thus, the benefits of the turns analysis of surface EMG signals should be lower not only in diagnosis of myopathy but also neuropathy.

Duchenne muscular dystrophy quantification: a multivariate analysis of surface EMG.

The paper describes a method of quantifying Duchenne muscular dystrophy which is examiner independent and uses surface electromyographic signals (EMG). A standardised protocol is proposed. Spectral parameters are first computed from digitised EMG, then a polynomial model is deduced from the evolution of each parameter. A discriminant analysis between healthy and DMD subjects leads to the determination of a discriminant plane and a level of sickness index.