scholarly journals Effects of Muscle Fatigue and Recovery on Complexity of Surface Electromyography of Biceps Brachii

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 1036
Author(s):  
Fuyuan Liao ◽  
Xueyan Zhang ◽  
Chunmei Cao ◽  
Isabella Yu-Ju Hung ◽  
Yanni Chen ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Biceps Brachii ◽  
Cupping Therapy ◽  
Nonlinear Method ◽  
Muscle Recovery ◽  
Sham Control ◽  
Semg Signal ◽  
Relative Change ◽  
Semg Signals

This study aimed to investigate the degree of regularity of surface electromyography (sEMG) signals during muscle fatigue during dynamic contractions and muscle recovery after cupping therapy. To the best of our knowledge, this is the first study assessing both muscle fatigue and muscle recovery using a nonlinear method. Twelve healthy participants were recruited to perform biceps curls at 75% of the 10 repetitions maximum under four conditions: immediately and 24 h after cupping therapy (−300 mmHg pressure), as well as after sham control (no negative pressure). Cupping therapy or sham control was assigned to each participant according to a pre-determined counter-balanced order and applied to the participant’s biceps brachii for 5 min. The degree of regularity of the sEMG signal during the first, second, and last 10 repetitions (Reps) of biceps curls was quantified using a modified sample entropy (Ems) algorithm. When exercise was performed immediately or 24 h after sham control, Ems of the sEMG signal showed a significant decrease from the first to second 10 Reps; when exercise was performed immediately after cupping therapy, Ems also showed a significant decrease from the first to second 10 Reps but its relative change was significantly smaller compared to the condition of exercise immediately after sham control. When exercise was performed 24 h after cupping therapy, Ems did not show a significant decrease, while its relative change was significantly smaller compared to the condition of exercise 24 h after sham control. These results indicated that the degree of regularity of sEMG signals quantified by Ems is capable of assessing muscle fatigue and the effect of cupping therapy. Moreover, this measure seems to be more sensitive to muscle fatigue and could yield more consistent results compared to the traditional linear measures.

Classification of Muscle Fatigue in Dynamic Contraction Using Surface Electromyography Signals and Multifractal Singularity Spectral Analysis

2016 ◽  
Vol 138 (11) ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Nearest Neighbor ◽  
Biceps Brachii ◽  
Information Gain ◽  
Multifractal Spectrum ◽  
Singularity Spectrum ◽  
K Nearest Neighbor ◽  
Dynamic Contraction ◽  
Semg Signals

Muscle fatigue is a neuromuscular condition experienced during daily activities. This phenomenon is generally characterized using surface electromyography (sEMG) signals and has gained a lot of interest in the fields of clinical rehabilitation, prosthetics control, and sports medicine. sEMG signals are complex, nonstationary and also exhibit self-similarity fractal characteristics. In this work, an attempt has been made to differentiate sEMG signals in nonfatigue and fatigue conditions during dynamic contraction using multifractal analysis. sEMG signals are recorded from biceps brachii muscles of 42 healthy adult volunteers while performing curl exercise. The signals are preprocessed and segmented into nonfatigue and fatigue conditions using the first and last curls, respectively. The multifractal detrended moving average algorithm (MFDMA) is applied to both segments, and multifractal singularity spectrum (SSM) function is derived. Five conventional features are extracted from the singularity spectrum. Twenty-five new features are proposed for analyzing muscle fatigue from the multifractal spectrum. These proposed features are adopted from analysis of sEMG signals and muscle fatigue studies performed in time and frequency domain. These proposed 25 feature sets are compared with conventional five features using feature selection methods such as Wilcoxon rank sum, information gain (IG) and genetic algorithm (GA) techniques. Two classification algorithms, namely, k-nearest neighbor (k-NN) and logistic regression (LR), are explored for differentiating muscle fatigue. The results show that about 60% of the proposed features are statistically highly significant and suitable for muscle fatigue analysis. The results also show that eight proposed features ranked among the top 10 features. The classification accuracy with conventional features in dynamic contraction is 75%. This accuracy improved to 88% with k-NN-GA combination with proposed new feature set. Based on the results, it appears that the multifractal spectrum analysis with new singularity features can be used for clinical evaluation in varied neuromuscular conditions, and the proposed features can also be useful in analyzing other physiological time series.

scholarly journals Immediate and Delayed Effects of Cupping Therapy on Reducing Neuromuscular Fatigue

2021 ◽  
Vol 9 ◽  
Author(s):  
Xiao Hou ◽  
Xiaoling Wang ◽  
Lisa Griffin ◽  
Fuyuan Liao ◽  
Joseph Peters ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Repeated Measures ◽  
Biceps Brachii ◽  
Cupping Therapy ◽  
Neuromuscular Fatigue ◽  
Median Frequency ◽  
Dominant Hand ◽  
Delayed Effect ◽  
Sham Control ◽  
Delayed Effects

Cupping therapy has been popular in elite athletes in recent years. However, the effect of cupping therapy on reducing muscle fatigue has not been investigated. The purpose of this study was to investigate the immediate and delayed effects of cupping therapy on reducing biceps brachii fatigue during biceps curls. Twelve healthy untrained participants were recruited for this repeated-measures study. Cupping therapy (–300 mmHg pressure for 5 min) and sham control (no negative pressure for 5 min) were applied after biceps fatigue induced by performing repeated biceps curls at 75% of the 10 repetitions of maximum of the non-dominant hand. Surface electromyography (EMG) with spectral analyses [mean frequency (MNF), median frequency (MDF), and spectral moments ratio (SMR)] were used to assess muscle fatigue during the fatigue task. EMG signals during the first 10 repetitions and the last 10 repetitions of biceps curls were used to assess neuromuscular fatigue. There were significant decreases in MNF and MDF and a significant increase in SMR immediately and 24 h after the sham control (no intervention). When comparing the MNF, MDF, and SMR after cupping therapy to the sham control, there was no significant immediate effect on reducing muscle fatigue. However, there was a significant delayed effect on improving recovery following fatigue for the cupping therapy compared to the sham control (MNF changes: sham 0.87 ± 0.02 vs. cupping 0.91 ± 0.02, p < 0.05; MDF changes sham: 0.85 ± 0.03 vs. cupping: 0.91 ± 0.02, p < 0.05; SMR changes: sham 1.89 ± 0.15 vs. cupping 1.58 ± 0.13, p < 0.05). The findings of this study demonstrate that there is a time effect of cupping therapy for reducing muscle fatigue. Cupping therapy is effective on reducing biceps brachii muscle fatigue after 24 h.

Classification of Muscular Nonfatigue and Fatigue Conditions Using Surface EMG Signals and Fractal Algorithms

2016 ◽  
Author(s):  
Kiran Marri ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Fractal Analysis ◽  
Sports Medicine ◽  
Biceps Brachii ◽  
Moving Average ◽  
Surface Emg ◽  
Feature Subset ◽  
Scale Invariant ◽  
Semg Signal ◽  
Semg Signals

The application of surface electromyography (sEMG) technique for muscle fatigue studies is gaining importance in the field of clinical rehabilitation and sports medicine. These sEMG signals are highly nonstationary and exhibit scale-invariant self-similarity structure. The fractal analysis can estimate the scale invariance in the form of fractal dimension (FD) using monofractal (global single FD) or multifractal (local varying FD) algorithms. A comprehensive study of sEMG signal for muscle fatigue using both multifractal and monofractal FD features have not been established in the literature. In this work, an attempt has been made to differentiate sEMG signals recorded nonfatigue and fatigue conditions using monofractal and multifractal algorithms, and machine learning methods. For this purpose, sEMG signals have been recorded from biceps brachii muscles of fifty eight healthy subjects using a standard protocol. The signals of nonfatigue and fatigue region were subjected to eight monofractal (Higuchi, Katz, Petrosian, Sevcik, box counting, multi-resolution length, Hurst and power spectrum density) and two multifractal (detrended fluctuating and detrended moving average) algorithms and 28 FD features were extracted. The features were ranked using conventional and genetic algorithms, and a subset of FD features were further subjected to Naïve Bayes (NB), Logistic Regression (LR) and Multilayer Perceptron (MLP) classifiers. The results show that all fractal features are statistically significant. The classification accuracy using feature subset of conventional method is observed to be from 83% to 88%. The highest accuracy of 93.96% was achieved using genetic algorithm and LR classifier combination. The result demonstrated that the performance of multifractal FD features to be more suitable for sEMG signals as compared to monofractal FD features. The fractal analysis of sEMG signals appears to be a very promising biomarker for muscle fatigue classification and can be extended to detection of fatigue onset in varied neuromuscular conditions.

Surface Electromyography-Based Muscle Fatigue Analysis Using Binary and Weighted Visibility Graph Features

2020 ◽  
pp. 2150016
Author(s):  
Navaneethakrishna Makaram ◽  
P. A. Karthick ◽  
Venugopal Gopinath ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Sports Medicine ◽  
Biceps Brachii ◽  
Information Gain ◽  
Graph Transformation ◽  
Visibility Graph ◽  
Invasive Technique ◽  
Learning Machines ◽  
Significant Difference

Surface electromyography (sEMG) is a non-invasive technique to assess the electrical activity of contracting skeletal muscles. sEMG-based muscle fatigue detection plays a key role in sports medicine, ergonomics and rehabilitation. These signals are random, multicomponent, nonlinear and the degree of fluctuations is higher in dynamic contractions. Hence, the extraction of reliable biomarkers remains a challenging task. In this work, an attempt has been made to differentiate non-fatigue, and fatigue conditions using nonlinear techniques, namely, binary and weighted Visibility Graph (VG) features. For this, signals are recorded from the biceps brachii muscle of 52 healthy adult volunteers. These signals are preprocessed, and the contractions associated with the non-fatigue and fatigue conditions are segmented. The graph transformation is performed, and first-order and second-order statistics, along with entropy measures, are extracted from the degree distribution. Parametric and non-parametric machine learning methods are applied for the classification. The results show that the proposed VG approach is able to capture the fluctuations of the signals in non-fatigue and fatigue conditions. Further, all extracted features exhibit a significant difference with [Formula: see text] [Formula: see text]. Maximum accuracy of 89.1% is achieved with information gain selected features and extreme learning machines classifier. Additionally, weighted VG features perform better than the binary version with a difference in the accuracy of 5%. It appears that the proposed approach could be used in real-time implementation for the monitoring of muscle fatigue conditions.

scholarly journals Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 529 ◽  
Author(s):  
Susanna Rampichini ◽  
Taian Martins Vieira ◽  
Paolo Castiglioni ◽  
Giampiero Merati
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Literature Search ◽  
Complexity Analysis ◽  
Rehabilitation Medicine ◽  
Extensive Literature ◽  
Recurrence Plots ◽  
Semg Signal ◽  
Extensive Literature Search ◽  
The Relationship

The surface electromyography (sEMG) records the electrical activity of muscle fibers during contraction: one of its uses is to assess changes taking place within muscles in the course of a fatiguing contraction to provide insights into our understanding of muscle fatigue in training protocols and rehabilitation medicine. Until recently, these myoelectric manifestations of muscle fatigue (MMF) have been assessed essentially by linear sEMG analyses. However, sEMG shows a complex behavior, due to many concurrent factors. Therefore, in the last years, complexity-based methods have been tentatively applied to the sEMG signal to better individuate the MMF onset during sustained contractions. In this review, after describing concisely the traditional linear methods employed to assess MMF we present the complexity methods used for sEMG analysis based on an extensive literature search. We show that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently. However, we also show that more work remains to be done to compare the complexity indices in terms of reliability and sensibility; to optimize the choice of embedding dimension, time delay and threshold distance in reconstructing the phase space; and to elucidate the relationship between complexity estimators and the physiologic phenomena underlying the onset of MMF in exercising muscles.

Skeletal Muscle Fatigue State Evaluation with Ultrasound Image Entropy

2020 ◽  
Vol 42 (6) ◽  
pp. 235-244
Author(s):  
Pan Li ◽  
Xuebing Yang ◽  
Guanjun Yin ◽  
Jianzhong Guo
Keyword(s):  
Skeletal Muscle ◽  
Muscle Fatigue ◽  
Biceps Brachii ◽  
Ultrasound Image ◽  
Voluntary Contraction ◽  
Ultrasound Images ◽  
Semg Signal ◽  
Skeletal Muscle Fatigue ◽  
Image Entropy ◽  
State Evaluation

Muscle fatigue often occurs over a long period of exercise, and it can increase the risk of muscle injury. Evaluating the state of muscle fatigue can avoid unnecessary overtraining and injury of the muscle. Ultrasound imaging can non-invasively visualize muscle tissue in real-time. Image entropy is commonly used to characterize the texture of an image. In this study, we evaluated changes in the ultrasound image entropy (USIE) during the fatigue process. Twelve volunteers performed static sustained contractions of biceps brachii at four different intensities (20%, 30%, 40%, and 50% of maximal voluntary contraction torque). The ultrasound images and surface electromyography (sEMG) signals were acquired during exercise to fatigue. We found that (1) the root-mean-square of the sEMG signal increased, the USIE decreased significantly with time during the sustained contractions; (2) the maximum endurance time (MET) and the decline percentage of USIE were significantly different ( p < .05) among the four contraction intensities; (3) the decline slope of USIE of the same volunteer was basically the same at different contraction intensities. The USIE could be a new method for the evaluation of skeletal muscle fatigue state.

scholarly journals Effects of Force Load, Muscle Fatigue, and Magnetic Stimulation on Surface Electromyography during Side Arm Lateral Raise Task: A Preliminary Study with Healthy Subjects

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Liu Cao ◽  
Ying Wang ◽  
Dongmei Hao ◽  
Yao Rong ◽  
Lin Yang ◽  
...  
Keyword(s):  
Muscle Fatigue ◽  
Surface Electromyography ◽  
Healthy Subjects ◽  
Magnetic Stimulation ◽  
Rest Period ◽  
The Body ◽  
Median Frequency ◽  
Force Load ◽  
Semg Signal ◽  
The Subject

The aim of this study was to quantitatively investigate the effects of force load, muscle fatigue, and extremely low-frequency (ELF) magnetic stimulation on surface electromyography (SEMG) signal features during side arm lateral raise task. SEMG signals were recorded from 18 healthy subjects on the anterior deltoid using a BIOSEMI ActiveTwo system during side lateral raise task (with the right arm 90 degrees away from the body) with three different loads on the forearm (0 kg, 1 kg, and 3 kg; their order was randomized between subjects). The arm maintained the loads until the subject felt exhausted. The first 10 s recording for each load was regarded as nonfatigue status and the last 10 s before the subject was exhausted was regarded as fatigue status. The subject was then given a five-minute resting between different loads. Two days later, the same experiment was repeated on every subject, and this time the ELF magnetic stimulation was applied to the subject’s deltoid muscle during the five-minute rest period. Three commonly used SEMG features, root mean square (RMS), median frequency (MDF), and sample entropy (SampEn), were analyzed and compared between different loads, nonfatigue/fatigue status, and ELF stimulation and no stimulation. Variance analysis results showed that the effect of force load on RMS was significant (p<0.001) but not for MDF and SampEn (bothp>0.05). In comparison with nonfatigue status, for all the different force loads with and without ELF stimulation, RMS was significantly larger at fatigue (allp<0.001) and MDF and SampEn were significantly smaller (allp<0.001).

CHARACTERIZATION OF MOTOR UNIT AT DIFFERENT STRENGTHS WITH MULTI-CHANNEL SURFACE ELECTROMYOGRAPHY

2017 ◽  
Vol 17 (01) ◽  
pp. 1750024
Author(s):  
JINBAO HE ◽  
XINHUA YI ◽  
ZAIFEI LUO
Keyword(s):  
Muscle Contraction ◽  
Surface Electromyography ◽  
Biceps Brachii ◽  
Motor Units ◽  
Spike Triggered Averaging ◽  
Channel Surface ◽  
Waveform Amplitude ◽  
Individual Motor ◽  
Semg Signals

In this study, specific changes in electromyographic characteristics of individual motor units (MUs) associated with different muscle contraction forces are investigated using multi-channel surface electromyography (SEMG). The gradient convolution kernel compensation (GCKC) algorithm is employed to separate individual MUs from their surface interferential electromyography (EMG) signals and provide the discharge instants, which is later used in the spike-triggered averaging (STA) techniques to obtain the complete waveform. The method was tested on experimental SEMG signals acquired during constant force contractions of biceps brachii muscles in five subjects. Electromyographic characteristics including the recruitment number, waveform amplitude, discharge pattern and innervation zone (IZ) are studied. Results show that changes in the action potential of single MU with different contraction force levels are consistent with those for all MUs, and that the amplitude of MU action potentials (MUAPs) provides a useful estimate of the muscle contraction forces.

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