Multifractal characterization of electromyogram signals

Surface electromyography is a useful means of studying normal and abnormal muscle functions and has many applications, including pathological diagnosis and prosthetic control. Most of the studies of such electromyogram (EMG) signals are based on either the analysis of their stochastic temporal characteristics in the time domain, or the power spectrum characteristics in the frequency domain. In this paper, we present an approach to characterization and feature extraction of EMG signals. This approach is based upon the chaotic behaviour of the EMG signals and the existence of the corresponding strange attractors with low embedding dimensions. The multifractal dimensions of the strange attractors underlying the chaos provide alternative features for analyzing the EMG signals, and describe how the entropy of these strange attractors changes as the hypervolume scales used for calculating the entropy vary.