A Lempel-Ziv complexity measure for muscle fatigue estimation

Area of Science:

• Biomedical Engineering
• Signal Processing
• Neuroscience

Background:

• Surface electromyography (sEMG) signals are crucial for understanding muscle activity.
• Analyzing sEMG signals often requires complex computational methods and assumptions about signal stationarity.
• Existing complexity measures may not fully capture the dynamics of biological signals during fatiguing tasks.

Purpose of the Study:

• To introduce a novel Lempel-Ziv complexity measure for the analysis of sEMG signals.
• To enhance the Lempel-Ziv measure for better suitability with biological signal analysis.
• To evaluate the proposed measure's effectiveness in tracking muscle fatigue.

Main Methods:

• Development of a ternary Lempel-Ziv complexity measure.
• Application of the measure to sEMG data collected during a muscle fatigue experiment.
• Comparison of the Lempel-Ziv measure's performance against the conventional median frequency.

Main Results:

• The ternary Lempel-Ziv measure demonstrated a higher correlation with increasing muscle fatigue levels.
• The proposed measure outperformed the conventional median frequency in tracking fatigue.
• The Lempel-Ziv measure is computationally simple and avoids assumptions of signal stationarity and power spectrum estimation.

Conclusions:

• The ternary Lempel-Ziv complexity measure offers a robust and efficient tool for sEMG analysis.
• This novel approach provides a valuable alternative for monitoring muscle fatigue.
• The measure's simplicity and reduced assumptions make it practical for various applications.