Analysis of EMG Signals Based on Wavelet Transform – A Review

Wavelet-based signal processing has become commonplace in the signal processing community over the past decade.Wavelet analysis is often very effective because it provides a simple approach for dealing with local aspects of a signal. Electromyography (EMG) signals can be used for clinical/biomedical applications, Evolvable Hardware Chip (EHW) development, and modern human computer interaction. EMG signals acquired from muscles require advanced methods for detection, decomposition, processing, and classification. One of the most important applications of wavelets is removal of noise from signals called denoising accomplished by thresholding wavelet coefficients in order to separate signal from noise. The purpose of this paper is to highlight the use of Wavelet transform (WT) for EMG signal analysis. A comparison study is also given to show performance of various EMG signal analysis methods over wavelet. This paper provides researchers a good understanding of EMG signal and its analysis procedures. Keywords— sEMG, wavelet transform, STFT, Fourier analysis, denoising, decomposition.