Jose Zariffa, University Health Network, Toronto Rehabilitation Institute, Toronto, Canada

Point-of-care prediction of muscle responsiveness to functional electrical stimulation therapy

Funded in: 2019, 2020, 2021

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Problem: Reduced arm and hand function have a significant impact on independence and quality of life after spinal cord injury
Target: Diagnostic method in the clinic to quickly and easily screen muscles for evaluation of responsiveness to FES-T
Goal: Predict recovery profile of each muscle and provide personalized therapy planning in FES-T

Reduced arm and hand function have a significant impact on independance and quality of life after spinal cord injury. Functional electrical stimulation therapy (FES-T) is a very promising treatment that has been shown to produce meaningful improvements in reaching and grasping function after neurological injuries. However, not all paralyzed muscles respond equally well to the therapy. Currently, therapists cannot predict which muscles will respond, limiting their ability to create a personalized therapy plan that can maximize outcomes while making the best use of the limited treatment time available.

The objective of this project is to develop a diagnostic method that will allow therapists to quickly and easily screen muscles in the clinic, in order to predict how they will respond to FES-T.

The specific aims are to:
1.) Track and describe how individual muscles recover strength over time during FES-T.
2.) Describe and categorize distinct patterns of muscle electrical activity before therapy starts.
3.) Combine 1 and 2 to identify features of a muscle’s electrical activity that predict how it will respond to FES-T.

30 participants with cervical spinal cord injury will take part in the study, which will be undertaken in collaboration with a clinic delivering FES-T. Muscles receiving training will undergo a detailed electrophysiological examination before the start of therapy and will then be tracked for strength recovery of the course of 20-40 sessions. Lastly, signal processing and machine learning techniques will be applied to the electrophysiological data to predict the recovery profile of each muscle.

The significance of this work will be to provide personalized therapy planning in FES-T, leading to more effective use of healthcare resource as well as improved outcomes.