Determination of muscle fatigue using dynamically embedded signals

Area of Science:

• Biomedical Engineering
• Surgical Technology
• Neuromuscular Physiology

Background:

• Muscle fatigue is a critical factor affecting precision in microsurgery.
• Electromyography (EMG) is widely used to assess muscle activity.
• Existing methods for EMG analysis of muscle fatigue have limitations.

Purpose of the Study:

• To introduce and evaluate a new method for analyzing muscle fatigue using EMG signals.
• To compare the robustness of the new dynamic embedding (DE) method against traditional mean frequency analysis.
• To assess the applicability of the DE method for muscles used in microsurgery.

Main Methods:

• Dynamically embedding EMG signals from a single muscle channel into a matrix.
• Calculating muscle fatigue entropy from the singular values of the dynamic embedding (DE) matrix.
• Comparing the DE method with traditional power spectral density mean frequency shifts using linear regression and coefficient of variation analysis.

Main Results:

• The dynamic embedding (DE) method quantifies muscle fatigue through signal entropy.
• Linear regression analysis showed the DE method's performance.
• The DE method demonstrated lower variability in its coefficients of variation compared to the mean frequency method, indicating greater robustness.

Conclusions:

• The dynamic embedding (DE) method offers a more robust approach to analyzing muscle fatigue from EMG signals.
• This complexity-based method shows promise for improving the assessment of muscle fatigue in microsurgical contexts.
• The findings suggest potential for enhanced surgeon performance monitoring and training.