Evaluation of the low-frequency components in surface electromyography.

Area of Science:

• Biomedical Engineering
• Neuroscience
• Physiology

Background:

• Surface electromyogram (SEMG) measures skeletal muscle electrical activity noninvasively.
• Thoracic SEMG is often contaminated by electrocardiographic (ECG) signals, particularly in the 5-50 Hz range.
• Current high-pass linear filtering (LF) removes SEMG components below 20 Hz, potentially losing valuable data.

Purpose of the Study:

• To introduce the Segmented Beat Modulation Method (SBMM) for cleaning SEMG from ECG interference.
• To evaluate SBMM's effectiveness compared to traditional LF filtering.
• To preserve low-frequency SEMG components crucial for motor unit analysis.

Main Methods:

• Acquisition of SEMG from the left rectus abdominis.
• Application of both SBMM and standard LF filtering (20 Hz cut-off) to SEMG data.
• Comparative analysis of the filtered SEMG signals.

Main Results:

• SBMM demonstrated superior ECG interference removal compared to LF filtering.
• LF filtering retained unwanted ECG components between 20-50 Hz.
• SBMM preserved significant low-frequency SEMG components (up to 20%), potentially related to motor unit firing rates.

Conclusions:

• SBMM is an effective tool for purifying SEMG signals from ECG contamination.
• The SBMM method retains essential low-frequency SEMG components often lost with conventional filtering techniques.
• Preserving these low-frequency components enhances the analysis of muscle electrical activity.