Suppression of stimulus artifact contaminating electrically evoked electromyography
Year: 2014
Type: article
Abstract: BACKGROUND: Electrical stimulation of muscle or nerve is a very useful technique for understanding of muscle activity and its pathological changes for both diagnostic and therapeutic purposes. During electrical stimulation of a muscle, the recorded M wave is often contaminated by a stimulus artifact. The stimulus artifact must be removed for appropriate analysis and interpretation of M waves. OBJECTIVES: The objective of this study was to develop a novel software based method to remove stimulus artifacts contaminating or superimposing with electrically evoked surface electromyography (EMG) or M wave signals. METHODS: The multiple stage method uses a series of signal processing techniques, including highlighting and detection of stimulus artifacts using Savitzky-Golay filtering, estimation of the artifact contaminated region with Otsu thresholding, and reconstruction of such region using signal interpolation and smoothing. The developed method was tested using M wave signals recorded from biceps brachii muscles by a linear surface electrode array. To evaluate the performance, a series of semi-synthetic signals were constructed from clean M wave and stimulus artifact recordings with different degrees of overlap between them. RESULTS: The effectiveness of the developed method was quantified by a significant increase in correlation coefficient and a significant decrease in root mean square error between the clean M wave and the reconstructed M wave, compared with those between the clean M wave and the originally contaminated signal. The validity of the developed method was also demonstrated when tested on each channel's M wave recording using a linear electrode array. CONCLUSIONS: The developed method can suppress stimulus artifacts contaminating M wave recordings.
Source: Neurorehabilitation
DOI: 10.3233/nre-131045
Language: English
Subfield: Biomedical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W1762332620
