A Method for Suppressing Electrical Stimulation Artifacts from Electromyography

Yurong Li, Jun Chen, Yuan Yang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.

Original languageEnglish (US)
JournalInternational journal of neural systems
DOIs
StateAccepted/In press - Jan 1 2019

Fingerprint

Electromyography
Tailings
Feedback control
Signal processing
Derivatives

Keywords

  • Electromyography
  • M-wave
  • functional electrical stimulation
  • stimulation artifact
  • time-series similarity

ASJC Scopus subject areas

  • Computer Networks and Communications

Cite this

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title = "A Method for Suppressing Electrical Stimulation Artifacts from Electromyography",
abstract = "When surface electromyography (EMG) signal is used in a real-time functional electrical stimulation (FES) system for feedback control, the artifact from electrical stimulation is a key challenge for EMG signal processing. To address this challenge, this study proposes a novel method to suppress stimulation artifacts in the EMG-driven closed-loop FES system. The proposed method is inspired by an experimental study that compares artifacts generated by electrical stimulations with different current intensities. It is found that (1) spikes of stimulation artifacts are susceptible to the current intensity and (2) tailing components are similar under different current intensities. Based on these observations, the proposed method combines the blanking and template subtracting strategies for suppressing stimulation artifact. The length of blanking window for suppressing the stimulation spike is adaptively determined by a spike detection algorithm and the first-order derivative analysis of signal. An autoregressive model is used to estimate the tailing part of stimulation artifact, which is an adaptive template for subtracting the artifact. The proposed method is evaluated on both semi-synthetic and experimental datasets. Verified on the semi-synthetic dataset, the proposed method achieves better performance than the classic blanking method. Validated on the experimental dataset, the proposed method substantially decreases the power of stimulation artifact in the EMG. These results indicate that the proposed method can effectively suppress the stimulation artifact while retains the useful EMG signal for an EMG-driven FES system.",
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A Method for Suppressing Electrical Stimulation Artifacts from Electromyography. / Li, Yurong; Chen, Jun; Yang, Yuan.

In: International journal of neural systems, 01.01.2019.

Research output: Contribution to journalArticle

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