Analysis of biceps brachii sEMG signal using Multiscale Fuzzy Approximate Entropy.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Sports Medicine

Background:

• Surface electromyography (sEMG) is crucial for assessing muscle function.
• Muscle fatigue alters sEMG signal characteristics.
• Differentiating fatigue states is vital for clinical applications and performance monitoring.

Purpose of the Study:

• To differentiate between muscle fatigue and non-fatigue conditions using multiscale features of sEMG signals.
• To evaluate the effectiveness of Multiscale Fuzzy Approximate Entropy (MSfApEn) in analyzing sEMG.
• To explore the potential of this method for clinical muscle evaluation.

Main Methods:

• sEMG signals were recorded from the biceps brachii during repetitive dynamic contractions in 50 adults.
• Signals were preprocessed, and the first and last segments were analyzed.
• Multiscale Fuzzy Approximate Entropy (MSfApEn) was computed, extracting features like median, low scale median, and high scale median.

Main Results:

• sEMG signal amplitude increased under fatigue conditions.
• Higher MSfApEn values were observed in non-fatigue states, indicating greater signal complexity.
• Extracted features effectively differentiated between fatigue and non-fatigue conditions with high statistical significance (p < 0.001).

Conclusions:

• Multiscale analysis of sEMG signals, particularly MSfApEn, can effectively distinguish between muscle fatigue and non-fatigue states.
• The method demonstrates potential for objective clinical assessment of muscle conditions.
• Increased signal complexity in non-fatigue states suggests a more dynamic muscle response.