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Related Experiment Video

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Targeting Transcutaneous Spinal Cord Stimulation Using a Supervised Machine Learning Approach Based on

Eira Lotta Spieker1,2,3, Ardit Dvorani2,3, Christina Salchow-Hömmen1

  • 1Department of Neurology, Charité-Universitätsmedizin Berlin, Charitéplatz 1, 10117 Berlin, Germany.

Sensors (Basel, Switzerland)
|January 26, 2024
PubMed
Summary

Mechanomyography (MMG) using accelerometers offers a simpler way to tune transcutaneous spinal cord stimulation (tSCS) therapy. This new method achieved high accuracy, potentially enabling easier tSCS application in clinics and homes.

Keywords:
accelerationmachine learningmechanomyography (MMG)multiple sclerosis (MS)spinal cord injury (SCI)supervised classificationtranscutaneous spinal cord stimulation (tSCS)

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Area of Science:

  • Biomedical Engineering
  • Neuroscience
  • Rehabilitation Technology

Background:

  • Transcutaneous spinal cord stimulation (tCS) is a promising therapy for spinal cord injuries and multiple sclerosis.
  • Current tCS calibration relies on electromyography (EMG), which is laborious and requires expertise.
  • Simplifying the calibration process is crucial for wider tCS adoption.

Purpose of the Study:

  • To investigate mechanomyography (MMG) as a simpler alternative to EMG for tCS calibration.
  • To assess the accuracy and feasibility of MMG-based tCS parameter tuning.
  • To evaluate the potential of MMG for clinical and home-use tCS applications.

Main Methods:

  • MMG was used to assess muscle activity via accelerometers, replacing EMG sensors.
  • A supervised machine learning classifier was trained on MMG data, using EMG responses as ground truth.
  • The accuracy of MMG-based calibration was compared to the traditional EMG method in healthy subjects and patients.

Main Results:

  • The MMG-based calibration achieved up to 87% accuracy compared to EMG.
  • Identified tCS current amplitudes using MMG were comparable to EMG in 85% of cases.
  • In healthy subjects, 91% of MMG-derived therapy parameters matched EMG outcomes.

Conclusions:

  • MMG offers a viable, accurate, and potentially simpler method for tuning tCS therapy.
  • This approach could significantly streamline tCS calibration, making it more accessible.
  • MMG may facilitate the clinical implementation and home use of tCS treatments.