EMG analysis using wavelet functions to determine muscle contraction

Abstract

Modern spectrum analysis has been applied to various bio-medical signal processing. Wavelet transform (WT) has been applied in this research for the analysis of the surface electromyography signal (SEMG). Mean and median frequencies of the SEMG power spectrum were used to indicate changes in muscle contraction during gait. This paper reports on the effectiveness of the wavelet transform applied to the SEMG signal as a means of understanding muscle contractions during gait. Power spectrum analysis on the EMG signal from right rectus femoris muscle was performed using various wavelet functions (WF). With the appropriate choice of the WF, it is possible to analyze SEMG significantly. Results show that WF Daubechies45 presents the most significant changes in SEMG power spectrum compared to the other WFs. Electromyography (EMG) signal represents the electrical activity of muscles. A muscle is composed of many Motor Units (MUs). EMG signals detected directly from the muscle or from the skin by using surface electrodes show a train of motor unit action potentials (MUAP) plus noise. With increasing muscle force, the raw EMG signal shows an increase in the number of MUAP recruited at increasing firing rates. EMG signals are modeled as a zero mean colored noise, which can be characterized by power spectrum density function (1). It is desired to apply a method of power spectrum analysis to study the frequency characteristics of random signals like EMG. In the SEMG, recruitment and increase in the firing frequencies are seen as an amplitude increase of the SEMG signal (2). Changes in the EMG power spectrum are used as an indicator of changes in muscle contraction and muscle fatigue for ergonomic purposes (3). In this research, SEMG are decomposed using Discrete Wavelet Transform (DWT) with various WFs. It is the technique that provides information related to the time- frequency variation of the signal and used for feature enhancement for biosignals. The purpose of this research is to analyze the SEMG power spectral parameters with the various WF during nine trial walk. In this study WFs, Haar, Daubechies (db2, db3, db4, db5, db45) and Symmlet (sym4, sym5) are used for the WT. As a tool for analyzing frequency components of the EMG signals, Fast Fourier Transform (FFT) is considered. A superposed EMG signal can be considered as summation of sine waves with different frequency velocity. The FFT algorithm is described as a decomposition of the EMG signal to its underlined sinus contents. The mean and median frequencies are the most important parameters for analyzing the frequency components of the EMG signal. In this research, mean and median frequency parameters are considered for the EMG power spectrum analysis during a 9 trial walk. Results show that, WF db45 presents the best contrast in the mean and median frequencies while compared to the other WFs. The study also demonstrates that mean and median frequency increases with an increase of muscle contraction.