Fractal dimension of electromyographic signals recorded with surface electrodes during isometric contractions is linearly correlated with muscle activation

Abstract

Emerging methods from nonlinear dynamical systems theory are beginning to be applied to the issue of quantifying the complexity of physiologic processes and anatomic structure^.^'^*^ These techniques can be used to transform the time-varying process of muscle activation into a complex geometric structure embodying this process to which the methods of "fractal geometry" may be applied.8 A "fractal" is a "self-similar" object with a nested structure in which characteristic features recur on different spatial scales. A simple law of construction thus produces a complex structure through iterative application. Mandelbrot? has argued that fractal structure is ubiquitous throughout nature.One of the ways to describe quantitatively a fractal object is by computing its "dimension." The

Nonlinear Analysis of Surface EMG Signals

“…It is not enough that only one fractal dimension is used to describe the selfaffine fractal time series. The multifractal dimensions have been proposed to describe this kind of the time series [67][68][69][70][71][72].…”

Recognition of Facial Movements and Hand Gestures Using Surface Electromyogram(sEMG) for HCI Based Applications

“…Biosignals such as sEMG are a result of the summation of identical motor units that travel through tissues and undergo spectral and magnitude compression. Anmuth et al [9] determined that fractal dimension was linearly related to the activation of the muscle measured as a fraction of maximum voluntary contraction. They also observed a linear relationship between the fractal dimension and the flexion-extension speeds and load.…”

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis: a review

“…Researchers have studied fractal of sEMG to characterize normal and pathological signals [2]. To better represent the properties of sEMG signal, fractal properties of sEMG has been proposed [4,43,115].…”

“…Burst within burst behaviour of sEMG in time has the property that patterns observed at one sampling rate are statistically similar to patterns observed at lower sampling rates. These nested patterns suggest that sEMG has self-similarity [4]. Researchers have studied fractal of sEMG to characterize normal and pathological signals [2].…”