Fourier and Wavelet Spectral Analysis of EMG Signals in 1-km Cycling Time-Trial

Marcelo Bigliassi, Paulo Rogério Scalassara, Thiago Ferreira Dias Kanthack, Taufik Abrão, Antonio Carlos de Moraes, Leandro Ricardo Altimari

Department of Electrical Engineering, CTU-State University of Londrina, Londrina, Brazil.
Department of Electrical Engineering, Federal Technological University of Paraná, Cornélio Procópio, Brazil.
Group of Study and Research in Neuromuscular System and Exercise, CEFE-State University of Londrina, Londrina, Brazil.
Laboratory of Electromyography Studies, FEF-State University of Campinas, Campinas, Brazil.

DOI: 10.4236/am.2014.513181                

Abstract

Frequency domain analyses in electromyographic (EMG) signals are frequently applied to assess muscle fatigue and similar variables. Moreover, Fourier-based approaches are typically used for investigating these procedures. Nonetheless, Fourier analysis assumes the signal as stationary which is unlikely during dynamic contractions. As an alternative method, wavelet-based treatments do not assume this pattern and may be considered as more appropriate for joint time-frequency domain analysis. Based on the previous statements, the purpose of the present study was to compare the application of Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) to assess muscle fatigue in dynamic exercise of a 1-km of cycling (time-trial condition). The results of this study indicated that CWT and STFT analyses have provided similar fatigue estimates (slope) (p> 0.05). However, CWT application represents lesser dispersion (p< 0.05) for vastus medialis (189.9 ± 82.1 for STFT vs 148.6 ± 60.2 for CWT) and vastus lateralis (151.6 ± 49.6 for STFT vs 103.5 ± 27.9 for CWT). In conclusion, despite the EMG signal did not change (p> 0.05) according to different methods, it is important to note that these responses seem to show greater values for CWT compared to STFT for 2 superficial muscles. Thereby, we are capable of considering CWT as a reliable and useful method to take into consideration when non-stationary or oscillating exercise models are evaluated.

Keywords

Exercise, Dynamic Exercise, Wavelet Analysis, Fourier Analysis

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	De Luca, C.J., Adam, A., Wotiz, R., Gilmore, L.D. and Nawab, S.H. (2006) Decomposition of Surface EMG Signals. Journal of Neurophysiology, 96, 1646-1657. http://dx.doi.org/10.1152/jn.00009.2006
[2]	Allen, D.G., Lamb, G.D. and Westerblad, H. (2008) Skeletal Muscle Fatigue: Cellular Mechanisms. Physiological Reviews, 88, 287-332. http://dx.doi.org/10.1152/physrev.00015.2007
[3]	Gandevia, S.C. (2001) Spinal and Supraspinal Factors in Human Muscle Fatigue. Physiological Reviews, 81, 1725-1789.
[4]	Abbiss, C.R. and Laursen, P.B. (2005) Models to Explain Fatigue during Prolonged Endurance Cycling. Sports Medicine, 35, 865-898. http://dx.doi.org/10.2165/00007256-200535100-00004
[5]	Ng, J.K., Richardson, C.A., Kippers, V., Parnianpour, M. and Bui, B.H. (1996) Clinical Applications of Power Spectral Analysis of Electromyographic Investigations in Muscle Function. Manual Therapy, 1, 99-103. http://dx.doi.org/10.1054/math.1996.0257
[6]	De Luca, C.J. (1997) The Use of Surface Electromyography in Biomechanics. Journal of Applied Biomechanics, 13, 135-163.
[7]	Sparto, P.J., Parnianpour, M., Barria, E.A. and Jagadeesh, J.M. (1999) Wavelet Analysis of Electromyography for Back Muscle Fatigue Detection during Isokinetic Constant-Torque Exertions. Spine (Phila Pa 1976), 24, 1791-1798.
[8]	Bonato, P., Roy, S.H., Knaflitz, M. and De Luca, C.J. (2001) Time-Frequency Parameters of the Surface Myoelectric Signal for Assessing Muscle Fatigue during Cyclic Dynamic Contractions. IEEE Transactions on Biomedical Engineering, 48, 745-753. http://dx.doi.org/10.1109/10.930899
[9]	Hostens, I., Seghers, J., Spaepen, A. and Ramon, H. (2004) Validation of the Wavelet Spectral Estimation Technique in Biceps Brachii and Brachioradialis Fatigue Assessment during Prolonged Low-Level Static and Dynamic Contractions. Journal of Electromyography and Kinesiology, 14, 205-215. http://dx.doi.org/10.1016/S1050-6411(03)00101-9
[10]	So, R.C., Ng, J.K., Lam, R.W., Lo, C.K. and Ng, G.Y. (2009) EMG wavelet analysis of quadriceps muscle during repeated knee extension movement. Medicine & Science in Sports & Exercise, 41, 788-796. http://dx.doi.org/10.1249/MSS.0b013e31818cb4d0
[11]	Barria, E.A. and Jagadeesh, J.M. (1994) Multiresolution Estimation of Motion Using the Wavelet Transform. SPIE the International Society for Optical Engineering, 2303, 542-553.
[12]	Kremenic, I.J., Glace, B.W. and McHugh, M.P. (2002) Fourier- vs. Wavelet-Based Time-Frequency Analysis of Fatiguing Quadriceps Contractions. Medicine & Science in Sports & Exercise, 34, S261. http://dx.doi.org/10.1097/00005768-200205001-01459
[13]	Weir, J.P., Heuszel, E., Witte, T., Worley, K. and Zens, M. (2003) Electromyographic Assessment of Fatigue: Fourier vs. Wavelet Based Estimates. Medicine & Science in Sports & Exercise, 35, S145. http://dx.doi.org/10.1097/00005768-200305001-00804
[14]	Beck, T.W., Housh, T.J., Johnson, G.O., Weir, J.P., Cramer, J.T., Coburn, J.W. and Malek, M.H. (2005) Comparison of Fourier and Wavelet Transform Procedures for Examining the Mechanomyographic and Electromyographic Frequency Domain Responses during Fatiguing Isokinetic Muscle Actions of the Biceps Brachii. Journal of Electromyography & Kinesiology, 15, 190-199. http://dx.doi.org/10.1016/j.jelekin.2004.08.007
[15]	Da Silva, R.A., Lariviere, C., Arsenault, A.B., Nadeau, S. and Plamondon, A. (2008) The Comparison of Wavelet- and Fourier-Based Electromyographic Indices of Back Muscle Fatigue during Dynamic Contractions: Validity and Reliability Results. Electromyography and Clinical Neurophysiology, 48, 147-162.
[16]	Dantas, J.L., Camata, T.V., Brunetto, M.A.O.C., Moraes, A.C., Abrao, T. and Altimari, L.R. (2010) Fourier (STFT) and Wavelet (db4) spectral analysis of EMG signals in isometric and dynamic maximal effort exercise. IEEE Engineering in Medicine and Biology Society Conference, 1, 5979-5982.
[17]	Vitor-Costa, M., Pereira, L.A., Oliveira, R.S., Pedro, R.E., Camata, T.V., Abrao, T., Brunetto, M.A.O.C. and Altimari, L.R. (2010) Fourier (STFT) and Wavelet (db4) Spectral Analysis of EMG Signals in Maximal Cosntant Load Dynamic Exercise. IEEE Engineering in Medicine and Biology Society Conference, 1, 4622-4625.
[18]	Camata, T.V., Dantas, J.L., Abrao, T., Brunetto, M.A.O.C., Moraes, A.C. and Altimari, L.R. (2010) Fourier (STFT) and Wavelet (db4) Spectral Analysis of EMG Signals in Supramaximal Constant Load Dymic Exercise. IEEE Engineering in Medicine and Biology Society Conference, 1, 1364-1367.
[19]	Van Ingen Schenau, G.J., Dorssers, W.M., Welter, T.G., Beelen, A., De Groot, G. and Jacobs, R. (1995) The Control of Mono-Articular Muscles in Multijoint Leg Extensions in Man. Journal of Physiology, 484, 247-254.
[20]	Hermens, H.J., Freriks, B., Disselhorst-Klug, C. and Rau, G. (2000) Development of Recommendations for SEMG Sensors and Sensor Placement Procedures. Journal of Electromyography and Kinesiology, 10, 361-374. http://dx.doi.org/10.1016/S1050-6411(00)00027-4
[21]	Karlsson, S., Yu, J. and Akay, M. (2000) Time-Frequency Analysis of Myoelectric Signals during Dynamic Contractions: A Comparative Study. IEEE Transactions on Biomedical Engineering, 47, 228-238.