Dynamics of patellar tendon reflex in spastic multiple sclerosis patients

Li-Qun Zhang*, Guangzhi Wang, James A Sliwa, William Z Rymer

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations


Patellar tendon reflexes were studied in fourteen normal subjects and ten multiple sclerosis (MS) patients. An instrumented hammer was used to tap the patellar tendon and record the tapping force, while the quadriceps EMG and knee extension torque were recorded isometrically as the reflex responses. The relationship between tapping force and reflex torque was characterized using several system measures: the tendon reflex gain (Gtr), contraction rate (Rc), half-relaxation rate (Rhr), contraction time (tc), half-relaxation time (thr), and reflex loop delay (td). Gtr, Rc, and Rhr of the MS patients were significantly higher than their counterparts in normal controls (p<0.001 for all cases), td of the MS patients was significantly shorter than that of the normal group (p<0.005). Gtr, Rc, and Rhr values correlated more closely with clinical measures of spasticity (Ashworth scale and tendon reflex scale) than did pure output measures (peak reflex torque and quadriceps EMG signals). Tendon reflexes were more repeatable in the MS population than in normal controls. The higher correlation to clinical measures and lower variation of Gtr, Rc, and Rhr indicate the potential values of these dynamic system measures in clinical practice as a more accurate and consistent approach towards quantification of the tendon jerk and spasticity.

Original languageEnglish (US)
Pages (from-to)2313-2316
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
StatePublished - Dec 1 1998
EventProceedings of the 1998 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 6) - Hong Kong, China
Duration: Oct 29 1998Nov 1 1998

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics


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