Fatigue estimation using a novel multi-fractal detrended fluctuation analysis-based approach.
TL;DR: This approach exploits the statistical self-similarity and long-range correlation of surface electromyography signals at different time scales in which the myoelectric manifestation of fatigue is more significant compared to the influence of varying force, muscle length and innervation zone.
About: This article is published in Journal of Electromyography and Kinesiology.The article was published on 2010-06-01. It has received 22 citations till now. The article focuses on the topics: Detrended fluctuation analysis.
Citations
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TL;DR: It is shown that some of these indices, like those derived from recurrence plots, from entropy or fractal analysis, can detect MMF efficiently and that more work remains to be done to compare the complexity indices in terms of reliability and sensibility.
Abstract: 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.
65 citations
Cites background or methods from "Fatigue estimation using a novel mu..."
...The multifractal DFA approach was used also to evaluate whether the effects of fatigue on the EMG signal could be estimated with greater accuracy than that of conventional indices of EMG such as the MDF of the sEMG power spectrum [100]....
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...Several papers focusing on the complex behavior of EMG demonstrated so far that the EMG signal is non-linear in nature and expresses the features of a low dimension chaotic system [72,100,104]....
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...The multifractal DFA has been applied to the biceps brachii contraction, and it was observed that the sEMG signal is mono- and multifractal in different time scales, with “several fractal-scaling breaks” [100]....
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..., in those neuromuscular disease where a reduction of the number of motoneurons occurs and the action potential of the residual motor units changes in shape and duration) [100]....
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TL;DR: It is suggested that FD analysis could be used as a complementary variable providing further information on central mechanisms with respect to CV in fatiguing contractions, suggesting a greater increase in motor unit synchronization with ageing.
Abstract: Over the past decade, linear and nonlinear surface electromyography (EMG) variables highlighting different components of fatigue have been developed. In this study, we tested fractal dimension (FD) and conduction velocity (CV) rate of changes as descriptors, respectively, of motor unit synchronization and peripheral manifestations of fatigue. Sixteen elderly (69 ± 4 years) and seventeen young (23 ± 2 years) physically active men (almost 3-5 h of physical activity per week) executed one knee extensor contraction at 70% of a maximal voluntary contraction for 30 s. Muscle fiber CV and FD were calculated from the multichannel surface EMG signal recorded from the vastus lateralis and medialis muscles. The main findings were that the two groups showed a similar rate of change of CV, whereas FD rate of change was higher in the young than in the elderly group. The trends were the same for both muscles. CV findings highlighted a non-different extent of peripheral manifestations of fatigue between groups. Nevertheless, FD rate of change was found to be steeper in the elderly than in the young, suggesting a greater increase in motor unit synchronization with ageing. These findings suggest that FD analysis could be used as a complementary variable providing further information on central mechanisms with respect to CV in fatiguing contractions.
47 citations
TL;DR: It was found that taking the natural logarithm of NSM and WI, although reducing the parameters' sensitivity to fatigue, increased SVR scores by reducing variability.
47 citations
TL;DR: Simulation results indicated that changes in motoneuron inhibition and firing rates alone could not directly account for increased beta-band coherence postfatigue, and the observed increase is more likely to arise from an increase in the strength of correlated inputs to MUs as the muscle fatigues.
Abstract: Synchronization between the firing times of simultaneously active motor units (MUs) is generally assumed to increase during fatiguing contractions. To date, however, estimates of MU synchronization have relied on indirect measures, derived from surface electromyographic (EMG) interference signals. This study used intramuscular coherence to investigate the correlation between MU discharges in the first dorsal interosseous muscle during and immediately following a submaximal fatiguing contraction, and after rest. Coherence between composite MU spike trains, derived from decomposed surface EMG, were examined in the delta (1-4 Hz), alpha (8-12 Hz), beta (15-30 Hz), and gamma (30-60 Hz) frequency band ranges. A significant increase in MU coherence was observed in the delta, alpha, and beta frequency bands postfatigue. In addition, wavelet coherence revealed a tendency for delta-, alpha-, and beta-band coherence to increase during the fatiguing contraction, with subjects exhibiting low initial coherence values displaying the greatest relative increase. This was accompanied by an increase in MU short-term synchronization and a decline in mean firing rate of the majority of MUs detected during the sustained contraction. A model of the motoneuron pool and surface EMG was used to investigate factors influencing the coherence estimate. Simulation results indicated that changes in motoneuron inhibition and firing rates alone could not directly account for increased beta-band coherence postfatigue. The observed increase is, therefore, more likely to arise from an increase in the strength of correlated inputs to MUs as the muscle fatigues.
43 citations
Cites methods from "Fatigue estimation using a novel mu..."
...…hypothesis, reporting evidence of a fa- tigue-induced increase in synchronized MU firings using indirect estimates of synchronization derived from surface electromyographic (EMG) interference signals (Beretta-Piccoli et al. 2015; Holtermann et al. 2009; Talebinejad et al. 2010; Webber et al. 1995)....
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...…methods based on nonlinear analysis of the surface EMG signal, which captures a larger representative sample of MU activity, have consistently inferred that MU synchronization increases with fatigue (Beretta-Piccoli et al. 2015; Holtermann et al. 2009; Talebinejad et al. 2010; Webber et al. 1995)....
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...This could also explain why methods based on nonlinear analysis of the surface EMG signal, which captures a larger representative sample of MU activity, have consistently inferred that MU synchronization increases with fatigue (Beretta-Piccoli et al. 2015; Holtermann et al. 2009; Talebinejad et al. 2010; Webber et al. 1995)....
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TL;DR: An integrated feature extraction method based on the variational mode decomposition (VMD) and multi-fractal detrended fluctuation analysis (MFDFA) is proposed for a fault diagnosis for a reciprocating compressor valve, which verifies the superiority of the proposed method.
Abstract: Aiming at the nonlinearity, nonstationarity and multi-component coupling characteristics of reciprocating compressor vibration signals, an integrated feature extraction method based on the variational mode decomposition (VMD) and multi-fractal detrended fluctuation analysis (MFDFA) is proposed for a fault diagnosis for a reciprocating compressor valve. Firstly, to eliminate the noise interference, a novel VMD method with superior anti-interference performance was utilized to obtain several components of the quasi-orthogonal band-limited intrinsic mode function (BLIMF) from a strong non-stationarity vibration signal, and a consistent number K of BLIMFs was selected based on a novel criterion for all fault states. Secondly, the MFDFA method, which can describe the multi-fractal structure feature of non-stationary time series, was applied to analyze each BLIMF component, and the parameters of MFDFA were employed as the eigenvectors to reflect the structure characteristics and local scale behavior of the vibration signal. Then, the principal component analysis (PCA) was introduced to refine the eigenvectors for a higher recognition efficiency and accuracy. Finally, the vibration signals of four types of reciprocating compressor valve faults were analyzed by this method, and the faults were identified correctly by pattern classifiers of BTSVM and CNN. Further results comparison with other feature extraction methods verifies the superiority of the proposed method.
14 citations
Cites methods from "Fatigue estimation using a novel mu..."
...Compared with conventional multi-fractal method, it can eliminate sequence trend terms by DFA, which fully reveal the multi-fractal features hidden in non-stationary time series, and estimate the multi-fractal spectrum accurately [18]....
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References
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TL;DR: This work analyzes two classes of controls consisting of patchy nucleotide sequences generated by different algorithms--one without and one with long-range power-law correlations, finding that both types of sequences are quantitatively distinguishable by an alternative fluctuation analysis method.
Abstract: Long-range power-law correlations have been reported recently for DNA sequences containing noncoding regions We address the question of whether such correlations may be a trivial consequence of the known mosaic structure ("patchiness") of DNA We analyze two classes of controls consisting of patchy nucleotide sequences generated by different algorithms--one without and one with long-range power-law correlations Although both types of sequences are highly heterogenous, they are quantitatively distinguishable by an alternative fluctuation analysis method that differentiates local patchiness from long-range correlations Application of this analysis to selected DNA sequences demonstrates that patchiness is not sufficient to account for long-range correlation properties
4,365 citations
"Fatigue estimation using a novel mu..." refers methods in this paper
...Multi-fractal DFA (Gao and Royshowdhury, 2000; Peng et al., 1994) has been extensively studied for determining the long-range correlation and statistical self-similarity of random processes (Gao et al., 2007; Chen et al., 2002, 2005; Talkner and Weber, 2000)....
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Book•
19 Apr 1996
TL;DR: The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering.
Abstract: From the Publisher:
The main thrust is to provide students with a solid understanding of a number of important and related advanced topics in digital signal processing such as Wiener filters, power spectrum estimation, signal modeling and adaptive filtering. Scores of worked examples illustrate fine points, compare techniques and algorithms and facilitate comprehension of fundamental concepts. Also features an abundance of interesting and challenging problems at the end of every chapter.
2,549 citations
28 Jan 2005
TL;DR: This work focuses on the development of models for Surface EMG Signal Generation based on the principles of Structure--Based SEMG models, which were developed in the context of motor control and Muscle Contraction.
Abstract: Introduction. Contributors. 1 BASIC PHYSIOLOGY AND BIOPHYSICS OF EMG SIGNAL GENERATION (T. Moritani, D. Stegeman, R. Merletti). 1.1 Introduction. 1.2 Basic Physiology of Motor Control and Muscle Contraction. 1.3 Basic Electrophysiology of the Muscle Cell Membrane. References. 2 NEEDLE AND WIRE DETECTION TECHNIQUES (J. V. Trontelj, J. Jabre, M. Mihelin). 2.1 Anatomical and Physiological Background of Intramuscular Recording. 2.2 Recording Characteristics of Needle Electrodes. 2.3 Conventional Needle EMG. 2.4 Special Needle Recording Techniques. 2.5 Physical Characteristics of Needle EMG Signals. 2.6 Recording Equipment. References. 3 DECOMPOSITION OF INTRAMUSCULAR EMG SIGNALS (D. W. Stashuk, D. Farina, K. Sogaard). 3.1 Introduction. 3.2 Basic Steps for EMG Signal Decomposition. 3.3 Evaluation of Performance of EMG Signal Decomposition Algorithms. 3.4 Applications of Results of the Decomposition of an Intramuscular EMG Signal. 3.5 Conclusions. References. 4 BIOPHYSICS OF THE GENERATION OF EMG SIGNALS (D. Farina, R. Merletti, D. F. Stegeman). 4.1 Introduction. 4.2 EMG Signal Generation. 4.3 Crosstalk. 4.4 Relationships between Surface EMG Features and Developed Force. 4.5 Conclusions. References. 5 DETECTION AND CONDITIONING OF THE SURFACE EMG SIGNAL (R. Merletti, H. Hermens). 5.1 Introduction. 5.2 Electrodes: Their Transfer Function. 5.3 Electrodes: Their Impedance, Noise, and dc Voltages. 5.4 Electrode Configuration, Distance, Location. 5.5 EMG Front--End Amplifiers. 5.6 EMG Filters: Specifications. 5.7 Sampling and A/D Conversion. 5.8 European Recommendations on Electrodes and Electrode Locations. References. 6 SINGLE--CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG SIGNAL (E. A. Clancy, D. Farina, G. Filligoi). 6.1 Introduction. 6.2 Spectral Estimation of Deterministic Signals and Stochastic Processes. 6.3 Basic Surface EMG Signal Models. 6.4 Surface EMG Amplitude Estimation. 6.5 Extraction of Information in Frequency Domain from Surface EMG Signals. 6.6 Joint Analysis of EMG Spectrum and Amplitude (JASA). 6.7 Recurrence Quantification Analysis of Surface EMG Signals. 6.8 Conclusions. References. 7 MULTI--CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG (D. Farina, R. Merletti, C. Disselhorst--Klug). 7.1 Introduction. 7.2 Spatial Filtering. 7.3 Spatial Sampling. 7.4 Estimation of Muscle--Fiber Conduction Velocity. 7.5 Conclusions. References. 8 EMG MODELING AND SIMULATION (D. F. Stegeman, R. Merletti, H. J. Hermens). 8.1 Introduction. 8.2 Phenomenological Models of EMG. 8.3 Elements of Structure--Based SEMG Models. 8.4 Basic Assumptions. 8.5 Elementary Sources of Bioelectric Muscle Activity. 8.6 Fiber Membrane Activity Profiles, Their Generation, Propagation, and Extinction. 8.7 Structure of the Motor Unit. 8.8 Volume Conduction. 8.9 Modeling EMG Detection Systems. 8.10 Modeling Motor Unit Recruitment and Firing Behavior. 8.11 Inverse Modeling. 8.12 Modeling of Muscle Fatigue. 8.13 Other Applications of Modeling. 8.14 Conclusions. References. 9 MYOELECTRIC MANIFESTATIONS OF MUSCLE FATIGUE (R. Merletti, A. Rainoldi, D. Farina). 9.1 Introduction. 9.2 Definitions and Sites of Neuromuscular Fatigue. 9.3 Assessment of Muscle Fatigue. 9.4 How Fatigue Is Reflected in Surface EMG Variables. 9.5 Myoelectric Manifestations of Muscle Fatigue in Isometric Voluntary Contractions. 9.6 Fiber Typing and Myoelectric Manifestations of Muscle Fatigue. 9.7 Factors Affecting Surface EMG Variable. 9.8 Repeatability of Estimates of EMG Variables and Fatigue Indexes. 9.9 Conclusions. References. 10 ADVANCED SIGNAL PROCESSING TECHNIQUES (D. Zazula, S. Karlsson, C. Doncarli). 10.1 Introduction. 10.2 Theoretical Background. 10.3 Decomposition of EMG Signals. 10.4 Applications to Monitoring Myoelectric Manifestations of Muscle Fatigue. 10.5 Conclusions. Acknowledgment. References. 11 SURFACE MECHANOMYOGRAM (C. Orizio). 11.1 The Mechanomyogram (MMG): General Aspects during Stimulated and Voluntary Contraction. 11.2 Detection Techniques and Sensors Comparison. 11.3 Comparison between Different Detectors. 11.4 Simulation. 11.5 MMG Versus Force: Joint and Adjunct Information Content. 11.6 MMG Versus EMG: Joint and Adjunct Information Content. 11.7 Area of Application. References. 12 SURFACE EMG APPLICATIONS IN NEUROLOGY (M. J. Zwarts, D. F. Stegeman, J. G. van Dijk). 12.1 Introduction. 12.2 Central Nervous System Disorders and SEMG. 12.3 Compound Muscle Action Potential and Motor Nerve Conduction. 12.4 CMAP Generation. 12.5 Clinical Applications. 12.6 Pathological Fatigue. 12.7 New Avenues: High--Density Multichannel Recording. 12.8 Conclusion. References. 13 APPLICATIONS IN ERGONOMICS (G. M. Hagg, B. Melin, R. Kadefors). 13.1 Historic Perspective. 13.2 Basic Workload Concepts in Ergonomics. 13.3 Basic Surface EMG Signal Processing. 13.4 Load Estimation and SEMG Normalization and Calibration. 13.5 Amplitude Data Reduction over Time. 13.6 Electromyographic Signal Alterations Indicating Muscle Fatigue in Ergonomics. 13.7 SEMG Biofeedback in Ergonomics. 13.8 Surface EMG and Musculoskeletal Disorders. 13.9 Psychological Effects on EMG. References. 14 APPLICATIONS IN EXERCISE PHYSIOLOGY (F. Felici). 14.1 Introduction. 14.2 A Few "Tips and Tricks". 14.3 Time and Frequency Domain Analysis of sEMG: What Are We Looking For? 14.4 Application of sEMG to the Study of Exercise. 14.5 Strength and Power Training. 14.6 Muscle Damage Studied by Means of sEMG. References. 15 APPLICATIONS IN MOVEMENT AND GAIT ANALYSIS (C. Frigo, R. Shiavi). 15.1 Relevance of Electromyography in Kinesiology. 15.2 Typical Acquisition Settings. 15.3 Study of Motor Control Strategies. 15.4 Investigation on the Mechanical Effect of Muscle Contraction. 15.5 Gait Analysis. 15.6 Identification of Pathophysiologic Factors. 15.7 Workload Assessment in Occupational Biomechanics. 15.8 Biofeedback. 15.9 The Linear Envelope. 15.10 Information Enhancement through Multifactorial Analysis. References. 16 APPLICATIONS IN REHABILITATION MEDICINE AND RELATED FIELDS (A. Rainoldi, R. Casale, P. Hodges, G. Jull). 16.1 Introduction. 16.2 Electromyography as a Tool in Back and Neck Pain. 16.3 EMG of the Pelvic Floor: A New Challenge in Neurological Rehabilitation. 16.4 Age--Related Effects on EMG Assessment of Muscle Physiology. 16.5 Surface EMG and Hypobaric Hipoxia. 16.6 Microgravity Effects on Neuromuscular System. References. 17 BIOFEEDBACK APPLICATIONS (J. R. Cram). 17.1 Introduction. 17.2 Biofeedback Application to Impairment Syndromes. 17.3 SEMG Biofeedback Techniques. 17.4 Summary. References. 18 CONTROL OF POWERED UPPER LIMB PROSTHESES (P. A. Parker, K. B. Englehart, B. S. Hudgins). 18.1 Introduction. 18.2 Myoelectric Signal as a Control Input. 18.3 Conventional Myoelectric Control. 18.4 Emerging MEC Strategies. 18.5 Summary. References. Index.
1,078 citations
"Fatigue estimation using a novel mu..." refers background in this paper
...Previous studies show that a simple linear correlation is adequate for quantification of performance of fatigue indices (Merletti and Parker, 2004)....
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...Consequently, the MDN has been well-established as the gold standard for muscle fatigue assessment for sEMG under static conditions (Merletti and Parker, 2004); however, MDN measurements are also influenced by factors other than muscle fatigue, including muscle length and force, which will vary…...
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...Fatigue is an experience of everyday life that is defined as any reduction in the muscle ability to produce force and its assessment is usually associated to an event such as the inability to further perform a task or sustain a contraction (Merletti and Parker, 2004)....
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...shown to influence significantly the sEMG frequency content, and MDN, through spatial filtering (Bonato, 2001; Merletti and Parker, 2004)....
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...Increased amplitude could be attributed to an increased number of firing MUs and/or their discharge rate (Merletti and Parker, 2004) which affects the power-law value at all scales....
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TL;DR: In this article, the effects of three types of non-stationarities often encountered in real data were studied. And the authors compared the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types and showed how the characteristics of these crossovers depend on the fraction and size of the parts cut out from the signal, the concentration of spikes and their amplitudes.
Abstract: Detrended fluctuation analysis ~DFA! is a scaling analysis method used to quantify long-range power-law correlations in signals. Many physical and biological signals are ‘‘noisy,’’ heterogeneous, and exhibit different types of nonstationarities, which can affect the correlation properties of these signals. We systematically study the effects of three types of nonstationarities often encountered in real data. Specifically, we consider nonstationary sequences formed in three ways: ~i! stitching together segments of data obtained from discontinuous experimental recordings, or removing some noisy and unreliable parts from continuous recordings and stitching together the remaining parts—a ‘‘cutting’’ procedure commonly used in preparing data prior to signal analysis; ~ii! adding to a signal with known correlations a tunable concentration of random outliers or spikes with different amplitudes; and ~iii! generating a signal comprised of segments with different properties—e.g., different standard deviations or different correlation exponents. We compare the difference between the scaling results obtained for stationary correlated signals and correlated signals with these three types of nonstationarities. We find that introducing nonstationarities to stationary correlated signals leads to the appearance of crossovers in the scaling behavior and we study how the characteristics of these crossovers depend on ~a! the fraction and size of the parts cut out from the signal, ~b! the concentration of spikes and their amplitudes ~c! the proportion between segments with different standard deviations or different correlations and ~d! the correlation properties of the stationary signal. We show how to develop strategies for preprocessing ‘‘raw’’ data prior to analysis, which will minimize the effects of nonstationarities on the scaling properties of the data, and how to interpret the results of DFA for complex signals with different local characteristics.
839 citations
Journal Article•
TL;DR: The essence and results of pertinent publications are discussed with emphasis on the relationship between the spectral shift of the myoelectric signal, conduction velocity of muscle fibers, pH of the interstitial fluid and blood flow within a muscle.
Abstract: Fatigue may be described as the decline in the ability of an individual to maintain a level of performance. However, the issue of fatigue in man is complex due to the various physiological and psychological phenomena which contribute to it. This article is limited to a discussion appertaining to that fatigue associated with changes in the physiological processes, and specifically that which is caused by sustained or repeated muscle contractions. It has long been known that during muscle contractions the frequency spectrum of the myoelectric signal undergoes a shift. Recently, several analyses and investigations have been reported on the applicability of this phenomenon for supplying objective or noninvasive measurements of localized muscle fatigue. The essence and results of pertinent publications are discussed with emphasis on the relationship between the spectral shift of the myoelectric signal, conduction velocity of muscle fibers, pH of the interstitial fluid and blood flow within a muscle.
797 citations
"Fatigue estimation using a novel mu..." refers background in this paper
...During isometric constant force contractions, muscle conduction velocity decreases with fatigue and this phenomenon is reflected in a decrease of the median frequency (MDN) of the sEMG (De Luca, 1984; Merletti and Parker, 2004)....
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