Analysis of progression of fatigue conditions in biceps brachii muscles using surface electromyography signals and complexity based features

Abstract

Muscle fatigue is a neuromuscular condition where muscle performance decreases due to sustained or intense contraction. It is experienced by both normal and abnormal subjects. In this work, an attempt has been made to analyze the progression of muscle fatigue in biceps brachii muscles using surface electromyography (sEMG) signals. The sEMG signals are recorded from fifty healthy volunteers during dynamic contractions under well defined protocol. The acquired signals are preprocessed and segmented in to six equal parts for further analysis. The features, such as activity, mobility, complexity, sample entropy and spectral entropy are extracted from all six zones. The results are found showing that the extracted features except complexity feature have significant variations in differentiating non-fatigue and fatigue zone respectively. Thus, it appears that, these features are useful in automated analysis of various neuromuscular activities in normal and pathological conditions.

Fatigue-Mediated Loss of Complexity is Contraction-Type Dependent in Vastus Lateralis Electromyographic Signals

“…The present findings showed that fatigue results in a significant loss in sEMG complexity. Consistently, previous research has reported decreases in neuromuscular complexity under fatigued conditions in a variety of neuromuscular outputs such as sEMG [16,17] and muscle torque time series [26,27]. Changes in the complexity of the dynamics of a system occur due to changes in the number of active constituents in a system [1].…”

“…Previous studies on muscle torque complexity have reported α values similar to those observed for sEMG, though notably different SampEn values (~0.5) [26,27]. Other investigations on sEMG complexity, however, reported SampEn values similar to those observed [4,17]. This may be due to potential differences between sEMG and muscle torque complexity.…”

“…This may be due to potential differences between sEMG and muscle torque complexity. For example, Suda et al [17] reported similar findings in low intensity isometric contractions with SampEn values of approximately 1.5–2.0 and 0.05–0.20 for sEMG and muscle torque, respectively. This may also result from the fact that SampEn takes into account a single time scale (embedding dimension) and is highly dependent on this parameter [25], while α takes into account multiple scales.…”

“…In the context of the neuromuscular system, the constituents are motor units and thus mechanisms that effect the activation and the interactions of which are likely candidates for regulatory mechanisms. Factors related to motor unit activation such as fatigue [16,17,18,19], intensity/load, and velocity [20,21] have been observed to affect the complexity of sEMG signals. These findings suggest a role for motor unit recruitment and discharge rates in neuromuscular complexity, although further research is warranted.…”

Analysis of Surface Emg Signals Under Fatigue and Non-Fatigue Conditions Using B-Distribution Based Quadratic Time Frequency Distribution

“…The high value of spectral entropy corresponds to more complex and less regular property. 8,9 Spectral entropy is modified to obtain the instantaneous spectral entropy and is given as…”