Wavelet analysis of surface electromyography signals

Abstract

A number of digital signal processing (DSP) techniques are being applied to surface electromyography (SEMG) signals to extract detailed features of the signal. Fast Fourier transform (FFT) is one of the most common methods for analyzing the signal whether it is filtered or not. Another DSP technique is referred to as wavelet analysis, a method that is gaining more use in analyzing SEMG signals. This research focuses on using the discrete wavelet transform (DWT) and the wavelet package transform (WPT). Both DWT and WPT use analytical wavelets called "mother wavelet" which comes in different sets or "families". Wavelet analysis has the advantage over FFT as it provides the frequency contents of the signal over the time period that is being analyzed. SEMG signals were collected from a muscle under sustained contractions for 4 seconds with different loads. The raw signals were analyzed using FFT, DWT and WPT in LabVIEW(R) using its signal processing toolset. Using wavelet analysis the SEMG signal was decomposed into its frequency content form and then was reconstructed. In this paper the results are presented to show that certain families of mother wavelets of wavelet analysis are more suitable than others for analyzing SEMG signals.