title: Relating clinical and neurophysiological assessment of spasticity by machine learning.
abstract: 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 relationship between the clinical and neurophysiological assessments is still unknown. In this paper 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 sEMG assessment may be proven useful as an objective method of evaluating the effectiveness of various interventions and for follow-up of SCI patients.
authors.affiliation: Department of Intelligent Systems, Jozef Stefan Institute, Ljubljana, Slovenia. blaz.zupan@ijs.si
authors.forename: B
authors.initials: B
authors.lastname: Zupan
authors.forename: D S
authors.initials: DS
authors.lastname: Stokić
authors.forename: M
authors.initials: M
authors.lastname: Bohanec
authors.forename: M M
authors.initials: MM
authors.lastname: Priebe
authors.forename: A M
authors.initials: AM
authors.lastname: Sherwood
mesh_terms: D001185:Artificial Intelligence; D004576:Electromyography / Q000379:methods; D006801:Humans; D009128:Muscle Spasticity / Q000145:classification*; D009460:Neurologic Examination / Q000379:methods; D013119:Spinal Cord Injuries / Q000150:complications*