Relating clinical and neurophysiological assessment of spasticity by machine learning

Spasticity following spinal cord injury (SCI) is most often assessed clinically using a five point Ashworth Score (AS). A more objective assessment of altered motor control may be achieved by using a comprehensive protocol based on a surface electromyographic (sEMG) activity recorded from thigh and...

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Bibliographic Details
Main Authors: Zupan, B., Stokic, D.S., Bohanec, M., Priebe, M.M., Sherwood, A.M.
Format: Conference Proceeding
Language:English
Subjects:
Data analysis
Hip
Knee
Learning systems
Leg
Machine learning
Motor drives
Muscles
Spinal cord injury
Thigh
Online Access:Request full text
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Summary:Spasticity following spinal cord injury (SCI) is most often assessed clinically using a five point Ashworth Score (AS). A more objective assessment of altered motor control may be achieved by using a comprehensive protocol based on a surface electromyographic (sEMG) activity recorded from thigh and leg muscles. However, the relation between clinical and neurophysiological assessments is still unknown. We employ three different classification methods to investigate this relationship. The experimental results indicate that if the appropriate set of sEMG features is used, the neurophysiological assessment is related to clinical findings and can be used to predict the AS. A comprehensive and objective sEMG assessment may be proven useful for the assessment of interventions and follow up of SCI patients.
ISSN:1063-7125
DOI:10.1109/CBMS.1997.596432