Analysis of concentric and eccentric contractions in biceps brachii muscles using surface electromyography signals and multifractal analysis

Abstract

Muscle contractions can be categorized into isometric, isotonic (concentric and eccentric) and isokinetic contractions. The eccentric contractions are very effective for promoting muscle hypertrophy and produce larger forces when compared to the concentric or isometric contractions. Surface electromyography signals are widely used for analyzing muscle activities. These signals are nonstationary, nonlinear and exhibit self-similar multifractal behavior. The research on surface electromyography signals using multifractal analysis is not well established for concentric and eccentric contractions. In this study, an attempt has been made to analyze the concentric and eccentric contractions associated with biceps brachii muscles using surface electromyography signals and multifractal detrended moving average algorithm. Surface electromyography signals were recorded from 20 healthy individuals while performing a single curl exercise. The preprocessed signals were divided into concentric and eccentric cycles and in turn divided into phases based on range of motion: lower (0°-90°) and upper (>90°). The segments of surface electromyography signal were subjected to multifractal detrended moving average algorithm, and multifractal features such as strength of multifractality, peak exponent value, maximum exponent and exponent index were extracted in addition to conventional linear features such as root mean square and median frequency. The results show that surface electromyography signals exhibit multifractal behavior in both concentric and eccentric cycles. The mean strength of multifractality increased by 15% in eccentric contraction compared to concentric contraction. The lowest and highest exponent index values are observed in the upper concentric and lower eccentric contractions, respectively. The multifractal features are observed to be helpful in differentiating surface electromyography signals along the range of motion as compared to root mean square and median frequency. It appears that these multifractal features extracted from the concentric and eccentric contractions can be useful in the assessment of surface electromyography signals in sports medicine and training and also in rehabilitation programs.

Exploring Acute Changes in Hamstring EMG after Warm-up and Stretching Using a Multifractal Analysis

“…Although a causal link is highly speculative, an increase in excitability (already registered after active dynamic stretching [17]) that might shift motor unit recruitment thresholds could be expected. For instance, authors have speculated that the larger multifractal characteristics during eccentric contractions might be attributed to a selective fast-twitch motor unit recruitment (40). The association of MFDFA and other physiological mechanisms (e.g., excitability) should therefore be investigated further.…”

“…The higher the exponents of the multifractal spectrum, the smaller the fluctuations. Small fluctuations represent a smooth or more periodic time series as compared with large fluctuations that characterize randomlike signals (40). Accordingly, our results first revealed a dominance of small amplitude fluctuations that was not affected by the experimental procedure.…”

“…Our results would therefore tend toward a lower degree of multifractality after warm-up. Previous authors have obtained alterations of the degree of multifractality of the EMG time series depending on contraction modes, contraction velocities, or fatigue (31,32,40). For instance, the degree of multifractality has been shown to be smaller in concentric versus eccentric contractions (40), with slow versus fast movements (31) and in fresh versus fatigue conditions (32).…”

Relationship between Skin Temperature, Electrical Manifestations of Muscle Fatigue, and Exercise-Induced Delayed Onset Muscle Soreness for Dynamic Contractions: A Preliminary Study

“…The EMG signals were then reconstructed. The frequency spectrum was determined using the fast Fourier algorithm [ 42 ] extracting the median and peak of frequencies of a non-overlapped rectangular window of 500 ms from the Fourier spectrum. Finally, the slopes of the median and peak frequencies of each and the entire series were obtained using a linear regression method.…”

Muscle Electrical Impedance Properties and Activation Alteration After Functional Electrical Stimulation-Assisted Cycling Training for Chronic Stroke Survivors: A Longitudinal Pilot Study

“…Surface electromyography (sEMG) is widely applied to evaluate muscle activation and muscle contraction pattern. A root mean square (RMS) value of sEMG signal is commonly calculated, which directly reflected the muscle activation ability ( 31 ). In addition, the co-contraction index (CI) is computed between two related muscles to identify the muscle contraction pattern during movements ( 32 ).…”