Re-Evaluating Electromyogram–Force Relation in Healthy Biceps Brachii Muscles Using Complexity Measures

Abstract

Abstract:The objective of this study is to re-evaluate the relation between surface electromyogram (EMG) and muscle contraction torque in biceps brachii (BB) muscles of healthy subjects using two different complexity measures. Ten healthy subjects were recruited and asked to complete a series of elbow flexion tasks following different isometric muscle contraction levels ranging from 10% to 80% of maximum voluntary contraction (MVC) with each increment of 10%. Meanwhile, both the elbow flexion torque and surface EMG data from the muscle were recorded. The root mean square (RMS), sample entropy (SampEn) and fuzzy entropy (FuzzyEn) of corresponding EMG data were analyzed for each contraction level, and the relation between EMG and muscle torque was accordingly quantified. The experimental results showed a nonlinear relation between the traditional RMS amplitude of EMG and the muscle torque. By contrast, the FuzzyEn of EMG exhibited an improved linear correlation with the muscle torque than the RMS amplitude of EMG, which indicates its great value in estimating BB muscle strength in a simple and straightforward manner. In addition, the SampEn of EMG was found to be insensitive to the varying muscle torques, almost presenting a flat trend with the increment of muscle force. Such a character of the SampEn implied its potential application as a promising surface EMG biomarker for examining neuromuscular changes while overcoming interference from muscle strength.

Complexity Analysis of Surface Electromyography for Assessing the Myoelectric Manifestation of Muscle Fatigue: A Review

“…Additionally, FuzzyEn was independent of the tolerance r introducing the concept of fuzzy membership functions for determining the degree of similarity between patterns. Therefore, the similarity between G ( i ) and G ( j ) is quantified by a fuzzy continuous and convex function [ 129 , 132 ]: with [ 121 , 131 , 133 , 134 ] Finally, where C m ( r ) is the average of for all the vectors G ( i ).…”

“…Despite the different studies using ApEn on EMG signals, its consistency and reliability have recently been questioned [ 72 ]. Zhou et al employed SampEn and FuzzyEn to interpret sEMG collected at different intensity levels of contraction and found a very weak correlation between SampEn and muscle torque while FuzzyEn showed a direct positive correlation with the effort [ 134 ]. These authors concluded that FuzzyEn could be a useful alternative to force estimation whereas SampEn might be determined as a biomarker of EMG able to overcome interference due to changing muscular contractions intensity.…”

A Novel Interpretation of Sample Entropy in Surface Electromyographic Examination of Complex Neuromuscular Alternations in Subacute and Chronic Stroke

“…The subject subjectively determined all force levels, which were estimated roughly in terms of the MVC percentage via the EMG amplitude. This protocol has been frequently adopted in clinical practice and applied in previous studies [30], [35]. The subject performed a stable isometric muscle contraction at each force level for at least 3 s. As a result, the recorded surface EMG from the single trial exhibited graded EMG interference patterns.…”

“…It also reflects the overall characteristics of the signal with a simple but effective procedure and high consistency to analyze biomedical signals. As a result, advances in SampEn in analyzing short physiological time series including surface EMG has been demonstrated in many previous studies [24], [26], [30]. In one example, SampEn was used to examine the biceps brachii muscles in patients with Parkinson’s disease [31], [32].…”

Slope Entropy: A New Time Series Complexity Estimator Based on Both Symbolic Patterns and Amplitude Information

“… The scientific and medical interest on Electromyograms (EMGs) and entropy measures is raising due to the recent availability of inexpensive continuous portable monitoring devices and the insight they provide into a number of important pathologies and motor disorders. They have been used to assess Parkinson’s disease [ 54 ], the neuromuscular impact of strokes [ 55 ], and muscular performance [ 56 , 57 ], to name just a few. The well–known site of Physionet [ 50 ] provides examples of EMGs, which we have used in previous classification studies, easily separable [ 22 ].…”