Analysis of concentric and eccentric contractions in biceps brachii muscles using surface electromyography signals and multifractal analysis
23 Jun 2016-Vol. 230, Iss: 9, pp 829-839
TL;DR: 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.
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.
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Cites methods from "Analysis of concentric and eccentri..."
...RMS r epresents the signal power in the time domain and has been used to measure the le vel of activation of a muscle [16, 17]....
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TL;DR: The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature and can help in investigating the progressive changes in uterine muscle contractions during pregnancy.
Abstract: Objectives: The objectives of this paper are to examine the source of multifractality in uterine electromyography (EMG) signals and to study the progression of pregnancy in the term (gestation period > 37 weeks) conditions using multifractal detrending moving average (MFDMA) algorithm. Methods: The signals for the study, considered from an online database, are obtained from the surface of abdomen during the second (T1) and third trimester (T2). The existence of multifractality is tested using Hurst and scaling exponents. With the intention of identifying the origin of multifractality, the preprocessed signals are converted to shuffle and surrogate data. The original and the transformed signals are subjected to MFDMA to extract multifractal spectrum features, namely strength of multifractality, maximum, minimum, and peak singularity exponents. Results: The Hurst and scaling exponents extracted from the signals indicate that uterine EMG signals are multifractal in nature. Further analysis shows that the source of multifractality is mainly owing to the presence of long-range correlation, which is computed as 79.98% in T1 and 82.43% in T2 groups. Among the extracted features, the peak singularity exponent and strength of multifractality show statistical significance in identifying the progression of pregnancy. The corresponding coefficients of variation are found to be low, which show that these features have low intersubject variability. Conclusion: It appears that the multifractal analysis can help in investigating the progressive changes in uterine muscle contractions during pregnancy.
21 citations
Cites background from "Analysis of concentric and eccentri..."
...It corresponds to the activities of the largest fluctuations of the time series [21]....
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...This feature indicates the activities of the smallest fluctuations in the time series [21]....
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...Physiological time series such as surface EMG signals [21], electroencephalography signals [23], heart rate variability [24] and respiration analysis [25], human gait [26], blood flow, blood pressure, glucose levels, gene expression data and DNA sequencing are found to exhibit multifractal characteristics [27]....
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...Thus, when the scale-invariance is indicated as a spectrum rather than a single fractal dimension, the signal is considered to possess multifractal characteristics [21]....
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TL;DR: The preliminary results do not support a relationship between skin temperature measured during exercise and either muscle fatigue during exercise or the ability to predict delayed onset muscle soreness 24 h after exercise.
Abstract: Delayed onset muscle soreness (DOMS) indicates the presence of muscle damage and impairs force production and control. Monitorization of DOMS is useful to improving recovery intervention plans. The magnitude of DOMS may relate to muscle fatigue, which can be monitored by surface electromyography (EMG). Additionally, growing interest has been expressed in determining whether the skin temperature over a muscle group during exercise to fatigue could be a non-invasive marker for DOMS. Here we determine whether skin temperature and manifestations of muscle fatigue during exercise are correlated and can predict DOMS after concentric–eccentric bicep curl exercises. We tested 10 young adults who performed concentric–eccentric bicep curl exercises to induce muscle damage in the biceps brachialis to investigate the relationship between skin temperature and fatigue during exercise and DOMS after exercise. Muscle activation and skin temperature were recorded during exercise. DOMS was evaluated 24 h after exercise. Data analysis was performed using Bayesian regression models with regularizing priors. We found significant muscle fatigue and an increase in skin temperature during exercise. DOMS was observed 24 h after exercise. The regression models showed no correlation of changes in skin temperature and muscle fatigue during exercise with DOMS 24 h after exercise. In conclusion, our preliminary results do not support a relationship between skin temperature measured during exercise and either muscle fatigue during exercise or the ability to predict DOMS 24 h after exercise.
13 citations
Cites methods from "Analysis of concentric and eccentri..."
...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....
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17 Mar 2020
TL;DR: An attempt is made to develop signal processing methods to understand the dynamics of the muscle’s electrical properties and it is observed that at the motif length of 13 all the extracted features are significant, which indicates that the signal has lower complexity.
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9 citations
Cites background from "Analysis of concentric and eccentri..."
...Measurement of these patterns and scaling properties can indicate healthy or abnormal characteristics.(6,7) Recently, symbolic transformation techniques have been used to analyze nonlinear time series data such as gas/liquid two-phase flow measurement fluctuation signals,(9) electroencephalogram(10) and other chaotic signals....
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TL;DR: 3D brain stem structure is segmented and analysed for texture alterations using multifractal features to differentiate EMCI from other Alzheimer’s disease stages and results indicate that the proposed technique is able to segment the brainstem structure from all the considered images.
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Abstract: We propose a description of normalized distributions (measures) lying upon possibly fractal sets; for example those arising in dynamical systems theory. We focus upon the scaling properties of such measures, by considering their singularities, which are characterized by two indices: \ensuremath{\alpha}, which determines the strength of their singularities; and f, which describes how densely they are distributed. The spectrum of singularities is described by giving the possible range of \ensuremath{\alpha} values and the function f(\ensuremath{\alpha}). We apply this formalism to the ${2}^{\ensuremath{\infty}}$ cycle of period doubling, to the devil's staircase of mode locking, and to trajectories on 2-tori with golden-mean winding numbers. In all cases the new formalism allows an introduction of smooth functions to characterize the measures. We believe that this formalism is readily applicable to experiments and should result in new tests of global universality.
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