Spatial analysis of muscular activations in stroke survivors

Ghulam Rasool, Babak Afsharipour, Nina L. Suresh, Xiaogang Hu, William Zev Rymer

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

5 Scopus citations

Abstract

We investigated the spatial patterns of electrical activity in stroke-affected muscles using the high density surface electromyogram (sEMG) grids. We acquired 128-channel sEMG signals from the impaired as well as contralateral Biceps Brachii (BB) muscles of stroke survivors and from healthy participants at various force levels from 20 to 60% of maximum voluntary contraction in an isometric non-fatiguing recording protocol. We found the spatial sEMG pattern to be consistent across force levels in healthy and stroke subjects. However, once compared across sides (left vs right in healthy and impaired vs. contralateral in stroke) we found stroke-affected sides to be significantly different in distribution pattern of sEMG from the contralateral side. The sEMG activity areas were significantly shrunk on the affected sides indicating muscle atrophy due to stroke.

Original languageEnglish (US)
Title of host publication2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6058-6061
Number of pages4
ISBN (Electronic)9781424492718
DOIs
StatePublished - Nov 4 2015
Externally publishedYes
Event37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015 - Milan, Italy
Duration: Aug 25 2015Aug 29 2015

Publication series

NameProceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
Volume2015-November
ISSN (Print)1557-170X

Conference

Conference37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Country/TerritoryItaly
CityMilan
Period8/25/158/29/15

All Science Journal Classification (ASJC) codes

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

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