Author(s)

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.

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.

Exercise, Dynamic Exercise, Wavelet Analysis, Fourier Analysis

Bigliassi, M. , Scalassara, P. , Kanthack, T. , Abrão, T. , Moraes, A. and Altimari, L. (2014) Fourier and Wavelet Spectral Analysis of EMG Signals in 1-km Cycling Time-Trial. Applied Mathematics, 5, 1878-1886. doi: 10.4236/am.2014.513181.