Using Surface Electromyographic Signal to Assess Fractal Dimension from Biceps Brachii Muscle based on Different Elbow Joint Angles

Abstract

Analysis of the complexity and variability from the biomedical physiological time series data raises significant interest as a promising and sensitive marker of abnormality or impairment assessment in muscle physiology, especially in electromyography (EMG) signal. This paper aimed to measure subject-specific (i.e., individual) fractal dimension as a quantitative measure of complexity of EMG signal (i.e., detecting long-range correlations in noisy signal) from upper limb bicep brachii (BB) muscle during five elbow joint angles movement (at 0°, 30°, 60°, 90° and 120°). The EMG signal was recorded from ten healthy (mean±SD age: 22.4±1.5 years) participants using wearable sensor. The fractal scaling (α-values) of the EMG time series was assessed using a non-linear technique called detrended fluctuation analysis (DFA). Majority of the results show that DFA α-values at each angle exhibit anti-correlated (i.e., DFA α < 0.05) behavior. Few results show positive correlation (i.e., DFA α between 0.53 to 0.77), but none of the α values have 1.0 (strongly correlated/pink noise). No significant difference exits between the elbow angles except one case, i.e., 0° vs. 30° (p < 0.05). This DFA-based complexity measuring results from EMG signal holds promise for rehabilitation of control of upper limb muscle activation patterns.