Examination of motor unit control properties in stroke survivors using surface EMG decomposition: A preliminary report

Nina l Suresh*, Xiaoyan Li, Ping Zhou, William Z Rymer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The objective of this pilot study was to examine alterations in motor unit (MU) control properties, (i.e. MU recruitment and firing rate) after stroke utilizing a recently developed high-yield surface electromyogram (EMG) decomposition technique. Two stroke subjects participated in this study. A sensor array was used to record surface EMG signals from the first dorsal interosseous (FDI) muscle during voluntary isometric contraction at varying force levels. The recording was performed in both paretic and contralateral muscles using a matched force protocol. Single motor unit activity was extracted using the surface EMG decomposition software from Delsys Inc. The results from the two stroke subjects indicate a reduction in the mean motor unit firing rate and a compression of motor unit recruitment range in paretic muscle as compared with the contralateral muscles. These findings provide further evidence of spinal motoneuron involvement after a hemispheric brain lesion, and help us to understand the complex origins of stroke induced muscle weakness.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages8243-8246
Number of pages4
DOIs
StatePublished - Dec 26 2011
Event33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011 - Boston, MA, United States
Duration: Aug 30 2011Sep 3 2011

Other

Other33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
CountryUnited States
CityBoston, MA
Period8/30/119/3/11

Keywords

  • firing rate
  • motor unit
  • motor unit recruitment
  • stroke
  • surface EMG

ASJC Scopus subject areas

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

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