Nonlinear analysis of EMG signals - a chaotic approach

This paper aims to present a systematic characterisation of the electromyogram (EMG) signal using a nonlinear chaotic approach. EMG signals from 10 muscles in the leg during walking and maximum voluntary contraction (MVC) were obtained and pre-processed using wavelet based denoising techniques. All signals were tested for non-linearity, stationarity and determinism. Chaotic characterization was done by calculating invariants such as correlation dimension (D2), Lyapunov spectrum (lambda1) and Kaplan-Yorke dimension (D(KY)). The EMG signals were non-linear and short-term stationary. Determinism and structure was found in the phase-space by studying the recurrence plots. Based on the values of the chaotic invariants, EMG signals were found to exhibit signs of chaotic behaviour with a dimension between 2 and 3 for walking and 3 and 4 for MVC data.