B. Zupan, Jozef Stefan Inst., Ljubljana Univ., Slovenia
D.S. Stokic, Jozef Stefan Inst., Ljubljana Univ., Slovenia
M. Bohanec, Jozef Stefan Inst., Ljubljana Univ., Slovenia
M.M. Priebe, Jozef Stefan Inst., Ljubljana Univ., Slovenia
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.
Index Terms:
medical expert systems; neurophysiological assessment; spasticity; machine learning; spinal cord injury; five point Ashworth Score; altered motor control; comprehensive protocol; surface electromyographic activity; leg muscles; thigh; classification methods; sEMG features; clinical findings; AS prediction; sEMG assessment; SCI patients
Citation:
B. Zupan, D.S. Stokic, M. Bohanec, M.M. Priebe, A.M. Sherwood, "Relating clinical and neurophysiological assessment of spasticity by machine learning," cbms, pp.190, 10th IEEE Symposium on Computer-Based Medical Systems (CBMS'97), 1997