Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 30172
New Wavelet Indices to Assess Muscle Fatigue during Dynamic Contractions

Authors: González-Izal M., Rodríguez-Carreño I, Mallor-Giménez F, Malanda A, Izquierdo M

Abstract:

The purpose of this study was to evaluate and compare new indices based on the discrete wavelet transform with another spectral parameters proposed in the literature as mean average voltage, median frequency and ratios between spectral moments applied to estimate acute exercise-induced changes in power output, i.e., to assess peripheral muscle fatigue during a dynamic fatiguing protocol. 15 trained subjects performed 5 sets consisting of 10 leg press, with 2 minutes rest between sets. Surface electromyography was recorded from vastus medialis (VM) muscle. Several surface electromyographic parameters were compared to detect peripheral muscle fatigue. These were: mean average voltage (MAV), median spectral frequency (Fmed), Dimitrov spectral index of muscle fatigue (FInsm5), as well as other five parameters obtained from the discrete wavelet transform (DWT) as ratios between different scales. The new wavelet indices achieved the best results in Pearson correlation coefficients with power output changes during acute dynamic contractions. Their regressions were significantly different from MAV and Fmed. On the other hand, they showed the highest robustness in presence of additive white gaussian noise for different signal to noise ratios (SNRs). Therefore, peripheral impairments assessed by sEMG wavelet indices may be a relevant factor involved in the loss of power output after dynamic high-loading fatiguing task.

Keywords: Median Frequency, EMG, wavelet transform, muscle fatigue

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1328354

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1488

References:


[1] Farina D. "Interpretation of the surface electromyogram in dynamic contractions". Exerc.Sport Sci.Rev. 2006, vol.34, no. 3, pp. 121-127.
[2] Farina D, Merletti R, Enoka RM. "The extraction of neural strategies from the surface EMG". J.Appl Physiol 2004, vol. 4, pp. 1486-1495.
[3] Komi PV, Tesch P. "EMG frequency spectrum, muscle structure, and fatigue during dynamic contractions in man". Eur.J.Appl Physiol Occup.Physiol 1979, vol. 1, pp. 41-50.
[4] Bigland-Ritchie B, Johansson R, Lippold OC, Woods JJ. Contractile speed and EMG changes during fatigue of sustained maximal voluntary contractions. J.Neurophysiol. 1983 Jul;50(1):313-24.
[5] Cheng AJ, Rice CL. Fatigue and recovery of power and isometric torque following isotonic knee extensions. J.Appl.Physiol 2005 Oct;99(4):1446-52.
[6] Klass M, Guissard N, Duchateau J. Limiting mechanisms of force production after repetitive dynamic contractions in human triceps surae. J.Appl.Physiol 2004 Apr;96(4):1516-21.
[7] Linnamo V, Hakkinen K, Komi PV. Neuromuscular fatigue and recovery in maximal compared to explosive strength loading. Eur.J.Appl.Physiol Occup.Physiol 1998;77(1-2):176-81.
[8] Basmajian, JV. and De Luca CJ. Muscles Alive: Their Functions Revealed by Electromyography. 5th ed, Baltimore, MD: Williams and Wilkins, 1985, pp. 201-222.
[9] Chaffin, DB. "Localized muscle fatigue, definition and measurements". J. Occup. Med. 1985, vol. 15, pp. 346-354.
[10] Lindström L., and Petersen I. "Power spectrum analysis of EMG signals and its application". Computer-aided Electromyography, J. E. Desmedt (Ed.). Prog. Clin. Neurophysiol, 1983, vol. 10, pp. 1-51.
[11] Moxham J., Edwards RHT, Aubier M. "Changes in EMG power spectrum (high-to-low ratio) with force fatigue in humans". J. Appl. Physiol. 1982, vol. 53, pp. 1094-1099.
[12] Dimitrov GV, Arabadzhiev TI, Mileva KN, Bowtell JL, Crichton N, and Dimitrova NA. "Muscle fatigue during dynamic contractions assessed by new spectral indices". Med Sci Sports Exerc 2006, vol. 38, no. 11, pp. 1971-1979.
[13] Akay M. Detection and estimation methods of biomedical signals. New York: Academic Press, 1996.
[14] Cuiwei L, Chongxun Z, Changfen T. "Detection of ECG characteristic points using wavelet transforms". IEEE Trans Biomed Eng 1995, vol. 42, pp. 21-28.
[15] Fang J, Agarwall GC, Shahani BT. "Decomposition of multiunit electromyographic signals". IEEE Trans Biomed Eng 1999, vol. 46, pp. 685-697.
[16] al-Fahoum AS, Howitt I. "Combined wavelet transformation and radial basis neural networks for classifying life-threatening cardiac arrhythmias". Med Biol Eng Comput 1999, vol. 37, pp.566-573.
[17] Rodríguez I, Gila L, Malanda A, Gurtubay I, Mallor F, Gómez S, Navallas J, Rodríguez J. "Motor unit action potential duration, II: a new automatic measurement method based on the wavelet transform". J Clin Neurophysiol 2007, vol. 24, pp. 59-69.
[18] Geva AB, Kerem DH. "Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering". IEEE Trans Biomed Eng 1998, vol. 45, pp. 1205-1216.
[19] Gurtubay IG, Alegre M, Labarga A, Malanda A, Iriarte J, Artieda J. "Gamma band activity in an auditory oddball paradigm studied with the wavelet transform". Clin Neurophysiol 2001, vol. 112, pp.1219-1228.
[20] Englehart K., Hudgins B., Parker P. and Stevenson M. "Time-frequency representation for classification of the transient myoelectric signal", in Proc. 20th Ann. In.l Conf. on Engineering in Medicine and Biology Society, 1998.
[21] Englehart K. "Signal Representation for Classification of the Transient Myoelectric Signal". Ph.D. dissertation. University of New Brunswick, Canada, 1998.
[22] Englehart K., Hudgins B., Parker P. "A Wavelet -Based Continuous Classification Scheme for Multifucntion Myoelectric Control". IEEE Transactions on Biomedical Engineering 2001, vol. 48, No. 3, pp. 302- 311.
[23] Rodríguez I, Vuskovic M. "Wavelet transform moments for feature extraction from temporal signals". Informatics in Control, Automation and Robotics II 2007, pp. 235-242.
[24] Sparto PJ, Parnianpour M., Barria EA, Jagadeesh JM. "Wavelet analysis of electromyography for back muscle fatigue during isokinetic constanttorque exertions". Spine 1999, vol. 24, pp. 1791-1798.
[25] Bonato P, Roy SH, Knaflitz M, De Luca CJ. "Time-frequency parameters of the surface myoelectric signal for assessing muscle fatigue during cyclic dynamic contractions". IEEE Trans.Biomed.Eng 2001, vol. 48, no.7, pp. 745-753.
[26] Karlsson S, Yu J, Akay M. "Time-frequency analysis of myoelectric signals during dynamic contractions: a comparative study". IEEE Trans.Biomed.Eng 2000, vol. 47, no. 2, pp. 228-238.
[27] Strang G. and Nguyen T. Wavelets and filter banks. Wellesley- Cambridge Press, 1996.
[28] Mallat S. "Characterization of signals from multiscale edges". IEEE Trans Pattern Anal Machine Intell 1992, vol.14, pp. 710-732.
[29] M. Zecca, S. Micera, M. C. Carrozza, and P. Dario. "Control of Multifunctional Prosthetic Hands by Processing the Electromyographic Signal." Critical Reviews in Biomedical Engineering 2002, vol. 30, pp. 459-485.