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Instrumentation Amplifier

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Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

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Removing ECG noise from surface EMG signals using adaptive filtering.

Guohua Lu¹, John-Stuart Brittain, Peter Holland

¹Department of Physiology, Anatomy and Genetics, University of Oxford, OX1 3PT, UK.

Neuroscience Letters

|June 30, 2009

Summary

This study introduces an adaptive noise cancellation filter to remove electrocardiogram (ECG) interference from surface electromyograms (EMGs) in cervical dystonia patients. The filter effectively removes cardiac artifacts, improving EMG signal quality for better clinical insights.

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Area of Science:

Biomedical Engineering
Neuroscience
Clinical Electrophysiology

Background:

Surface electromyograms (EMGs) are crucial for studying dystonia's pathophysiology and guiding clinical treatment.
Cardiac artifacts, specifically electrocardiogram (ECG) interference, frequently contaminate surface EMG recordings.
Effective artifact removal is essential for accurate analysis of dystonia-related muscle activity.

Purpose of the Study:

To evaluate the efficacy of a recursive-least-square adaptive noise cancellation filter.
To assess the filter's performance in removing ECG interference from surface EMGs in patients with cervical dystonia.
To determine the influence of various filter parameters on performance.

Main Methods:

Simulated noisy EMG signals were used to quantify filter performance using coherence and signal-to-noise ratio.
The recursive-least-square adaptive filter was applied to surface EMGs from trapezius muscles of cervical dystonia patients.
Parameters including signal-to-noise ratio, forgetting factor, filter order, and regularization factor were analyzed.

Main Results:

The recursive-least-square adaptive filter demonstrated fast convergence, effectively tracking complex dystonic EMG patterns.
The filter successfully removed ECG noise from simulated and patient-recorded EMG signals.
Performance was validated through coherence and signal-to-noise ratio improvements.

Conclusions:

The adaptive filter is a reliable and efficient tool for removing ECG artifacts from surface EMGs.
This method enhances the quality of EMG data in patients with cervical dystonia, facilitating better pathophysiological studies.
The filter's ability to handle varied dystonic contraction patterns makes it broadly applicable.