Fourier and wavelet spectral analysis of EMG signals in isometric and dynamic maximal effort exercise

Abstract

Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency domain analysis. The purpose of this study was to compare Short-Time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT) in assessing muscle fatigue in isometric and dynamic exercise. The results of this study indicate that CWT and STFT analyses give similar fatigue estimates (slope of median frequency) in isometric and dynamic exercise (P>0.05). However, the results of the variance was lower for both types of exercise in CWT compared to STFT (P < 0.05) indicating more variability in the EMG signal analysis using STFT. Thus, the stationarity assumption may not be the sole factor responsible for affecting the Fourier based estimates.

Análise espectral do sinal EMG de exercício incremental em ciclistas e não ciclistas usando as transformadas de Fourier e Wavelet

“…Our results corroborate findings of other studies that found no significant differences between the spectral parameters, estimated by FFT and WT, obtained in constant load cycling exercises at submaximal 17 , maximum 18 , or supramaximal 19 intensities, in the comparison between dynamic and isometric exercises 20 and also in dynamic back-muscle exercises 9 as well.…”

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

“…Some studies compared the use of CWT and STFT in different exercises protocols [7] [9] [15] [16]. Both techniques seem to provide similar information regarding muscular fatigue, when EMG fatigue indices are assessed in dynamic exercise systematically controlling the movement speed, angle and force.…”

Influence of Different Strategies of Treatment Muscle Contraction and Relaxation Phases on EMG Signal Processing and Analysis During Cyclic Exercise

“…However, for this study in addition to the RMS also analyzed spectral parameters. To spectral analyses or frequency domain, was obtained the parameters from median frequency (MF), variance and slope, those values were determined using Wavelet Daubechies db4 (DWT) [6,8]. Was considered the analyses of EMG signal in the contraction phase (bursts) and during all signal (bursts + silence).…”

“…To find and use the spectrum, we must find a source that gives us the possibility to produce this figure, when sometimes the Fast Fourier Transform proves as algorithm in a simple calculus to find discrete signals. A series of recommendations are proposed to this technique, since the establishment of sample number, duration intervals, window apply and many aspects as signal stationary is a complicated thing to deal with, which leads us to use a wavelet transform, more appropriate to cyclic activities as cycling and running for example [6][7][8][9].…”