Muscle Fatigue Analysis by Visualization of Dynamic Surface EMG Signals Using Markov Transition Field.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Signal Processing

Background:

• Muscle fatigue analysis is crucial for diagnosing neuromuscular disorders.
• Non-linear probabilistic methods, like Markov transition fields (MTF), offer insights into neuromuscular system dynamics via surface electromyography (sEMG) signal transitions.

Purpose of the Study:

• To propose and evaluate a novel method for visualizing sEMG signals using MTF under muscle fatigue conditions.
• To assess the utility of MTF-encoded sEMG data in analyzing neuromuscular system complexity during fatigue.

Main Methods:

• sEMG signals were collected from 45 healthy participants during biceps curl exercise.
• Signals were filtered, segmented, and transformed into MTF images using Markov transition matrices.
• Average self-transition probability was extracted and compared between non-fatigue and fatigue states.

Main Results:

• A statistically significant difference (p < 0.001) was found in the average self-transition probability between non-fatigue and fatigue segments.
• An increase in average self-transition probability correlated with a decrease in sEMG signal complexity under fatigue.
• Encoding sEMG signals into MTF images proved effective for analyzing neuromuscular system complexity.

Conclusions:

• The proposed MTF-based visualization method effectively captures changes in sEMG signals during muscle fatigue.
• This approach provides a valuable tool for assessing muscle fatigue and may aid in diagnosing various myoneural conditions.