StimEMG: An Electromyogram Recording System With Real-Time Removal of Time-Varying Electrical Stimulation Artifacts

A closed-loop Functional Electrical Stimulation (FES) system that incorporates electromyogram (EMG) signal feedback provides more effective assistance to paralytic patients in maintaining and recovering their motor abilities. However, the closed-loop FES system with real-time adjustment of stimulation parameters tends to introduce time-varying stimulation artifacts in EMG signals, challenging the removal of stimulation artifacts that aims at more accurate monitoring of muscle contraction status. Therefore, an EMG acquisition system that embeds a stimulation artifact generation (SAG) circuit and the Recursive Least Squares (RLS) adaptive filter was developed in this study and named StimEMG. The SAG-RLS strategy was tested using the simulated contaminated EMG signals and the StimEMG system was tested in an experimental study with 8 subjects. Both the simulation and the experimental study showed that the SAG-RLS method obtained a higher correlation (R ${}^{{2}}\text {)}$ between the denoised EMG and the corresponding clean EMG or EMG segments compared with the current Gram-Schmidt-based (GSB) method (simulation study, $0.98\pm 0.0044$ v.s. $0.65\pm 0.3217$ ; experimental study, $0.99\pm 0.0024$ v.s. $0.52\pm 0.2105$ ). Meanwhile, the SAG-RLS method can suppress stimulation artifact more effectively, resulting a higher signal-to-noise ratio (simulation study: $12.83\pm 2.1745$ v.s. $1.54\pm 1.3106$ ) and higher noise rejection ratio (experimental study: $2.32\pm 0.7046$ v.s. $1.92\pm 0.8014$ ). The significantly improved performance is speculated to result from the ability of the SAG unit to precisely and timely capture the variation of the stimulation artifacts caused by the change of stimulation parameters, unlike previous methods relying on the stability of the characteristic of stimulation artifacts in the contaminated EMG signals. The developed StimEMG system provides a robust EMG acquisition module for the closed-loop FES system.

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Publication Details

Journal
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tnsre.2025.3555572
Citations
1
Primary Topic
Muscle activation and electromyography studies
Type
article
Field-Weighted Citation Impact
0.67

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article

StimEMG: An Electromyogram Recording System With Real-Time Removal of Time-Varying Electrical Stimulation Artifacts

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1 citations
IEEE Transactions on Neural Systems and Rehabilitation Engineering
Muscle activation and electromyography studies
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article

StimEMG: An Electromyogram Recording System With Real-Time Removal of Time-Varying Electrical Stimulation Artifacts

Yang Zheng, Henry Shin, Run Ji, Rui Yuan, Jijun Zhao
article en
1 citations

Abstract

A closed-loop Functional Electrical Stimulation (FES) system that incorporates electromyogram (EMG) signal feedback provides more effective assistance to paralytic patients in maintaining and recovering their motor abilities. However, the closed-loop FES system with real-time adjustment of stimulation parameters tends to introduce time-varying stimulation artifacts in EMG signals, challenging the removal of stimulation artifacts that aims at more accurate monitoring of muscle contraction status. Therefore, an EMG acquisition system that embeds a stimulation artifact generation (SAG) circuit and the Recursive Least Squares (RLS) adaptive filter was developed in this study and named StimEMG. The SAG-RLS strategy was tested using the simulated contaminated EMG signals and the StimEMG system was tested in an experimental study with 8 subjects. Both the simulation and the experimental study showed that the SAG-RLS method obtained a higher correlation (R ${}^{{2}}\text {)}$ between the denoised EMG and the corresponding clean EMG or EMG segments compared with the current Gram-Schmidt-based (GSB) method (simulation study, $0.98\pm 0.0044$ v.s. $0.65\pm 0.3217$ ; experimental study, $0.99\pm 0.0024$ v.s. $0.52\pm 0.2105$ ). Meanwhile, the SAG-RLS method can suppress stimulation artifact more effectively, resulting a higher signal-to-noise ratio (simulation study: $12.83\pm 2.1745$ v.s. $1.54\pm 1.3106$ ) and higher noise rejection ratio (experimental study: $2.32\pm 0.7046$ v.s. $1.92\pm 0.8014$ ). The significantly improved performance is speculated to result from the ability of the SAG unit to precisely and timely capture the variation of the stimulation artifacts caused by the change of stimulation parameters, unlike previous methods relying on the stability of the characteristic of stimulation artifacts in the contaminated EMG signals. The developed StimEMG system provides a robust EMG acquisition module for the closed-loop FES system.

IEEE Transactions on Neural Systems and Rehabilitation EngineeringVol. 33
Ministry of Civil Affairs (CN), National Research Center for Rehabilitation Technical Aids (CN), Xi'an Jiaotong University (CN)
National Natural Science Foundation of China
Openalex Percentile: Top 33%
Muscle activation and electromyography studies
0.67
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