Nonlinear surface EMG analysis to detect changes of motor unit conduction velocity and synchronization

Area of Science:

• Neuromuscular physiology
• Biomedical signal processing

Background:

• Surface electromyography (EMG) signal characteristics reflect neuromuscular system modifications.
• Traditional spectral analysis (centroid, median frequency) assesses muscle function.
• Nonlinear methods like Recurrence Quantification Analysis (RQA) show promise for detecting muscle status changes.

Purpose of the Study:

• To investigate the impact of motor unit synchronization and conduction velocity (CV) on EMG spectral variables and RQA metrics (%Rec, %Det).
• To compare the sensitivity of RQA and spectral analysis in assessing muscle fatigue.

Main Methods:

• Utilized a simulation model to manipulate motor unit synchronization and mean CV.
• Conducted experimental surface EMG recordings during isometric fatiguing contractions of the biceps brachii.
• Analyzed EMG spectral variables and RQA parameters (%Rec, %Det).

Main Results:

• Both spectral variables and RQA metrics (%Rec, %Det) are influenced by CV and motor unit synchronization.
• Spectral variables strongly correlate with %Det, providing similar information about muscle properties.
• RQA metrics (%Det, %Rec) exhibit larger variations than spectral variables in response to muscle status changes.

Conclusions:

• RQA is validated as a sensitive method for fatigue assessment.
• RQA offers potential advantages over classic spectral analysis due to higher sensitivity to muscle status alterations.