19-0062 Algorithm to remove interference from electromyography signals

Automatically detect and remove motion artifacts and power line noise from HD-EMG signals.Improved denoising compared to a conventional filter method through less distortion of the original EMG signals and higher yield of extracted motor unit spike tra…
  • Automatically detect and remove motion artifacts and power line noise from HD-EMG signals.
  • Improved denoising compared to a conventional filter method through less distortion of the original EMG signals and higher yield of extracted motor unit spike trains.

Abstract

Electromyography (EMG) is a noninvasive technique that can capture muscle activities with electrodes placed on the skin and has been widely used for research and clinical purposes, including diagnostics and rehabilitation. Recent technology advances have led to the development of high-density EMG (HD-EMG), which enhances the identification of motor tasks, reveals the distribution change of muscle activities during stimulation-induced fatigue, and furthers muscle anatomical insights. Interference is a common concern with HD-EMG. This can be caused by motion artifacts and power line noise. Several techniques have been developed to reduce interference, but all face limitations.

Researchers in the Department of Biomedical Engineering has developed an algorithm based on independent component analysis to automatically detect and remove motion artifacts and power line noise from HD-EMG signals. Independent power line noise is detected and processed with a notch filter, while motion artifacts are identified and filtered with a high-pass filter and amplitude inhibition method. Studies using synthetic EMG signals were used to evaluate the interference removal algorithm. This demonstrated improved denoising compared to a conventional filter method through less distortion of the original EMG signals and higher yield of extracted motor unit spike trains (>95% detection accuracy).

Website

https://unc.flintbox.com/technologies/D49B35A77EAB41D7B8E5979018CAC153

Advantages

  • Automatic detection and removal of motion artifacts and power line noise
  • The high degree of restoring EMG information
  • Increased yield of EMG decomposition even in the presence of severe artifacts

Potential Applications

Research and clinical EMG studies. Myoelectric control of rehabilitation or assistive devices.

Contact Information

Name: Matthew Howe

Email: matthew.howe@unc.edu

Phone: 919.966.3929