The Usefulness of Wavelet Transform to Reduce Noise in the SEMG Signal

This chapter presents a usefulness of wavelet transform (WT) algorithm in pre-processing stage of surface electromyography (sEMG) signal analysis particularly in application of noise reduction. The successful pre-processing stage based on wavelet decomposition and denoising algorithm is proposed in this chapter together with the principle, theory, up-todate literature review and experimental results of the wavelet denoising algorithms. Main application of this algorithm is sEMG control systems, notably prosthetic devices or computers. SEMG signal is one of the useful electrophysiological signals. It is measured by surface electrodes that are placed on the skin superimposed on the muscle. Rich useful information has occurred in the muscles subjacent to the skin as a mixture of the whole motor unit action potentials (MUAPs). Such information is also useful in a wide class of clinical and engineering researches which may lead to providing the diagnosis tools of neuromuscular and neurological problems and to providing the control systems of assistive robots and rehabilitation devices (Merletti & Parker, 2004). Generally, in order to use the sEMG as a diagnosis signal or a control signal, a feature is often extracted before performing classification stage due to a lot of information obtained from raw sEMG data and a low computational complexity required in the embedded devices (Boostani & Moradi, 2003). However, the sEMG signals that originate in a wide class of human muscles and activities are definitely contaminated by different types of noise (De Luca, 2002; Reaz et al., 2006). This becomes a main problem to extract certain features and thus the reach to high accurate classification. In the last decade, many research works have been interested in developing better algorithms and improving the existing methods to reduce noises and to estimate the useful sEMG information (De Luca et al., 2010; Mewett et al., 2004; Phinyomark et al., 2011). Generally, noises contaminated in the sEMG signal can be categorized into four major types: ambient noise, motion artifact, inherent instability of the sEMG signal, and inherence in electronic components in the detection and recording equipment (De Luca, 2002). The first three types have specific frequency band and do not fall in the energy band of the sEMG signal. For instance, power-line interference has the frequency component at 50 Hz (or 60 Hz), and motion artifact and instability in nature of sEMG signal have most of their energy in the frequency range of 0 to 20 Hz. Usage of conventional filters, i.e. band-pass filter and band-stop filter, can reduce noises in these types (De Luca et al., 2010). However, the last noise type is a central concern in analysis of the sEMG signal. It is an inherent noise that is