scholarly journals The Refined Composite Downsampling Permutation Entropy Is a Relevant Tool in the Muscle Fatigue Study Using sEMG Signals

Entropy ◽  
2021 ◽  
Vol 23 (12) ◽  
pp. 1655
Author(s):  
Philippe Ravier ◽  
Antonio Dávalos ◽  
Meryem Jabloun ◽  
Olivier Buttelli
Keyword(s):  
Biceps Brachii ◽  
Structural Information ◽  
Spectral Band ◽  
Electric Activity ◽  
Real Data ◽  
Voluntary Contraction ◽  
Permutation Entropy ◽  
Complexity Measures ◽  
Ordinal Patterns ◽  
Semg Signals

Surface electromyography (sEMG) is a valuable technique that helps provide functional and structural information about the electric activity of muscles. As sEMG measures output of complex living systems characterized by multiscale and nonlinear behaviors, Multiscale Permutation Entropy (MPE) is a suitable tool for capturing useful information from the ordinal patterns of sEMG time series. In a previous work, a theoretical comparison in terms of bias and variance of two MPE variants—namely, the refined composite MPE (rcMPE) and the refined composite downsampling (rcDPE), was addressed. In the current paper, we assess the superiority of rcDPE over MPE and rcMPE, when applied to real sEMG signals. Moreover, we demonstrate the capacity of rcDPE in quantifying fatigue levels by using sEMG data recorded during a fatiguing exercise. The processing of four consecutive temporal segments, during biceps brachii exercise maintained at 70% of maximal voluntary contraction until exhaustion, shows that the 10th-scale of rcDPE was capable of better differentiation of the fatigue segments. This scale actually brings the raw sEMG data, initially sampled at 10 kHz, to the specific 0–500 Hz sEMG spectral band of interest, which finally reveals the inner complexity of the data. This study promotes good practices in the use of MPE complexity measures on real data.

Intramuscular and surface electromyogram changes during muscle fatigue

1986 ◽  
Vol 60 (4) ◽  
pp. 1179-1185 ◽  
Author(s):  
T. Moritani ◽  
M. Muro ◽  
A. Nagata
Keyword(s):  
Muscle Fatigue ◽  
Biceps Brachii ◽  
Surface Emg ◽  
Firing Frequency ◽  
Voluntary Contraction ◽  
Mean Power ◽  
Mean Power Frequency ◽  
Surface Electromyogram ◽  
Spike Amplitude ◽  
Frequency Power

Twelve male subjects were tested to determine the effects of motor unit (MU) recruitment and firing frequency on the surface electromyogram (EMG) frequency power spectra during sustained maximal voluntary contraction (MVC) and 50% MVC of the biceps brachii muscle. Both the intramuscular MU spikes and surface EMG were recorded simultaneously and analyzed by means of a computer-aided intramuscular spike amplitude-frequency histogram and frequency power spectral analysis, respectively. Results indicated that both mean power frequency (MPF) and amplitude (rmsEMG) of the surface EMG fell significantly (P less than 0.001) together with a progressive reduction in MU spike amplitude and firing frequency during sustained MVC. During 50% MVC there was a significant decline in MPF (P less than 0.001), but this decline was accompanied by a significant increase in rmsEMG (P less than 0.001) and a progressive MU recruitment as evidenced by an increased number of MUs with relatively large spike amplitude. Our data suggest that the surface EMG amplitude could better represent the underlying MU activity during muscle fatigue and the frequency powers spectral shift may or may not reflect changes in MU recruitment and rate-coding patterns.

Dynamic contraction and fatigue analysis in biceps brachii muscles using synchrosqueezed wavelet transform and singular value features

2021 ◽  
pp. 095441192110480
Author(s):  
Lakshmi M Hari ◽  
Gopinath Venugopal ◽  
Swaminathan Ramakrishnan
Keyword(s):  
Wavelet Transform ◽  
Biceps Brachii ◽  
Singular Value ◽  
Time Frequency ◽  
Singular Value Decomposition Method ◽  
Dynamic Contractions ◽  
Synchrosqueezed Wavelet Transform ◽  
Value Decomposition ◽  
Neuromuscular Conditions ◽  
Semg Signals

In this study, the dynamic contractions and the associated fatigue condition in biceps brachii muscle are analysed using Synchrosqueezed Wavelet Transform (SST) and singular value features of surface Electromyography (sEMG) signals. For this, the recorded signals are decomposed into time-frequency matrix using SST. Two analytic functions namely Morlet and Bump wavelets are utilised for the analysis. Singular Value Decomposition method is applied to this time-frequency matrix to derive the features such as Maximum Singular Value (MSV), Singular Value Entropy (SVEn) and Singular Value Energy (SVEr). The results show that both these wavelets are able to characterise nonstationary variations in sEMG signals during dynamic fatiguing contractions. Increase in values of MSV and SVEr with the progression of fatigue denotes the presence of nonstationarity in the sEMG signals. The lower values of SVEn with the progression of fatigue indicate the randomness in the signal. Thus, it appears that the proposed approach could be used to characterise dynamic muscle contractions under varied neuromuscular conditions.

scholarly journals Corticospinal excitability of the biceps brachii is shoulder position dependent

2017 ◽  
Vol 118 (6) ◽  
pp. 3242-3251 ◽  
Author(s):  
Brandon Wayne Collins ◽  
Edward W. J. Cadigan ◽  
Lucas Stefanelli ◽  
Duane C. Button
Keyword(s):  
Evoked Potentials ◽  
Maximal Voluntary Contraction ◽  
Biceps Brachii ◽  
Motor Evoked Potentials ◽  
Voluntary Contraction ◽  
Corticospinal Excitability ◽  
State Dependent ◽  
Shoulder Flexion ◽  
Erb’S Point ◽  
Shoulder Position

The purpose of this study was to examine the effect of shoulder position on corticospinal excitability (CSE) of the biceps brachii during rest and a 10% maximal voluntary contraction (MVC). Participants ( n = 9) completed two experimental sessions with four conditions: 1) rest, 0° shoulder flexion; 2) 10% MVC, 0° shoulder flexion; 3) rest, 90° shoulder flexion; and 4) 10% MVC, 90° shoulder flexion. Transcranial magnetic, transmastoid electrical, and Erb’s point stimulation were used to induce motor-evoked potentials (MEPs), cervicomedullary MEPs (CMEPs), and maximal muscle compound potentials (Mmax), respectively, in the biceps brachii in each condition. At rest, MEP, CMEP, and Mmax amplitudes increased ( P < 0.01) by 509.7 ± 118.3%, 113.3 ± 28.3%, and 155.1 ± 47.9%, respectively, at 90° compared with 0°. At 10% MVC, MEP amplitudes did not differ ( P = 0.08), but CMEP and Mmax amplitudes increased ( P < 0.05) by 32.3 ± 10.5% and 127.9 ± 26.1%, respectively, at 90° compared with 0°. MEP/Mmax increased ( P < 0.01) by 224.0 ± 99.1% at rest and decreased ( P < 0.05) by 51.3 ± 6.7% at 10% MVC at 90° compared with 0°. CMEP/Mmax was not different ( P = 0.22) at rest but decreased ( P < 0.01) at 10% MVC by 33.6 ± 6.1% at 90° compared with 0°. EMG increased ( P < 0.001) by 8.3 ± 2.0% at rest and decreased ( P < 0.001) by 21.4 ± 4.4% at 10% MVC at 90° compared with 0°. In conclusion, CSE of the biceps brachii was dependent on shoulder position, and the pattern of change was altered within the state in which it was measured. The position-dependent changes in Mmax amplitude, EMG, and CSE itself all contribute to the overall change in CSE of the biceps brachii. NEW & NOTEWORTHY We demonstrate that when the shoulder is placed into two common positions for determining elbow flexor force and activation, corticospinal excitability (CSE) of the biceps brachii is both shoulder position and state dependent. At rest, when the shoulder is flexed from 0° to 90°, supraspinal factors predominantly alter CSE, whereas during a slight contraction, spinal factors predominantly alter CSE. Finally, the normalization techniques frequently used by researchers to investigate CSE may under- and overestimate CSE when shoulder position is changed.

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.

scholarly journals Permutation Entropy: Enhancing Discriminating Power by Using Relative Frequencies Vector of Ordinal Patterns Instead of Their Shannon Entropy

Entropy ◽  
2019 ◽  
Vol 21 (10) ◽  
pp. 1013 ◽  
Author(s):  
David Cuesta-Frau ◽  
Antonio Molina-Picó ◽  
Borja Vargas ◽  
Paula González
Keyword(s):  
Nonlinear Dynamics ◽  
Time Series ◽  
Classification Accuracy ◽  
Markov Models ◽  
Hidden Markov ◽  
Permutation Entropy ◽  
Time Series Classification ◽  
Discriminating Power ◽  
Ordinal Patterns ◽  
Temperature Records

Many measures to quantify the nonlinear dynamics of a time series are based on estimating the probability of certain features from their relative frequencies. Once a normalised histogram of events is computed, a single result is usually derived. This process can be broadly viewed as a nonlinear I R n mapping into I R , where n is the number of bins in the histogram. However, this mapping might entail a loss of information that could be critical for time series classification purposes. In this respect, the present study assessed such impact using permutation entropy (PE) and a diverse set of time series. We first devised a method of generating synthetic sequences of ordinal patterns using hidden Markov models. This way, it was possible to control the histogram distribution and quantify its influence on classification results. Next, real body temperature records are also used to illustrate the same phenomenon. The experiments results confirmed the improved classification accuracy achieved using raw histogram data instead of the PE final values. Thus, this study can provide a very valuable guidance for the improvement of the discriminating capability not only of PE, but of many similar histogram-based measures.

scholarly journals On the Use of Fuzzy and Permutation Entropy in Hand Gesture Characterization from EMG Signals: Parameters Selection and Comparison

2020 ◽  
Vol 10 (20) ◽  
pp. 7144
Author(s):  
Alessandro Mengarelli ◽  
Andrea Tigrini ◽  
Sandro Fioretti ◽  
Stefano Cardarelli ◽  
Federica Verdini
Keyword(s):  
Upper Limb ◽  
Surface Electromyography ◽  
Permutation Entropy ◽  
Hand Gesture ◽  
Complexity Measures ◽  
Robotic Arms ◽  
Feature Sets ◽  
Parameters Selection ◽  
Strongly Connected ◽  
Upper Limb Movements

The surface electromyography signal (sEMG) is widely used for gesture characterization; its reliability is strongly connected to the features extracted from sEMG recordings. This study aimed to investigate the use of two complexity measures, i.e., fuzzy entropy (FEn) and permutation entropy (PEn) for hand gesture characterization. Fourteen upper limb movements, sorted into three sets, were collected on ten subjects and the performances of FEn and PEn for gesture descriptions were analyzed for different computational parameters. FEn and PEn were able to properly cluster the expected numbers of gestures, but computational parameters were crucial for ensuring clusters’ separability and proper gesture characterization. FEn and PEn were also compared with other eighteen classical time and frequency domain features through the minimum redundancy maximum relevance algorithm and showed the best predictive importance scores in two gesture sets; they also had scores within the subset of the best five features in the remaining one. Further, the classification accuracies of four different feature sets presented remarkable increases when FEn and PEn are included as additional features. Outcomes support the use of FEn and PEn for hand gesture description when computational parameters are properly selected, and they could be useful in supporting the development of robotic arms and prostheses myoelectric control.

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