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Related Experiment Video

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Prediction of Biceps Muscle Electromyogram Signal Using a NARX Neural Network.

Vahid Khodadadi1, Fereidoun Nowshiravan Rahatabad1, Ali Sheikhani1

  • 1Department of Biomedical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Journal of Medical Signals and Sensors
|June 9, 2023
PubMed
Summary

This study demonstrates that a NARX neural network can accurately predict electromyogram (EMG) signals from biceps muscle under nonlinear, chaotic electrical stimulation conditions. This predictive model shows promise for functional electrical stimulation (FES) controller design and disease diagnosis.

Keywords:
Biceps muscleNARX neural network modelRossler modelelectromyographymusculocutaneous nerve

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Computational Biology

Background:

  • Electromyogram (EMG) signal prediction is crucial for developing advanced control systems in functional electrical stimulation (FES).
  • Nonlinear stimulation models present unique challenges for accurate physiological response prediction.
  • The biceps muscle serves as a model system for investigating neuromuscular responses to electrical stimulation.

Purpose of the Study:

  • To compare experimental EMG signal data with predictions from a NARX neural network model.
  • To evaluate the efficacy of a novel nonlinear stimulation model for eliciting muscle responses.
  • To assess the potential of the NARX model for designing FES controllers.

Main Methods:

  • A study involving 10 individuals was conducted, encompassing skin preparation, electrode placement, and single-channel EMG signal recording from the biceps muscle.
  • Nonlinear electrical stimulation was applied to the musculocutaneous nerve using a chaotic equation derived from the Rossler equation.
  • The NARX neural network was trained and validated using 100 synchronized stimulation and EMG response signals.

Main Results:

  • The Rossler equation successfully generated nonlinear and unpredictable conditions for muscle stimulation.
  • The NARX neural network demonstrated a high degree of accuracy in predicting EMG signals under these complex conditions.
  • Experimental data closely matched the predictions generated by the NARX model.

Conclusions:

  • The NARX neural network serves as an effective predictive model for EMG signals in nonlinear stimulation scenarios.
  • The developed model shows significant potential for advancing the design of FES-based control systems.
  • This approach may also contribute to the diagnostic capabilities for certain neuromuscular diseases.