Statistical model applied to motor evoked potentials analysis

Ying Ma, Nitish V. Thakor, Xiaofeng Jia

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

Abstract

Motor evoked potentials (MEPs) convey information regarding the functional integrity of the descending motor pathways. Absence of the MEP has been used as a neurophysiological marker to suggest cortico-spinal abnormalities in the operating room. Due to their high variability and sensitivity, detailed quantitative studies of MEPs are lacking. This paper applies a statistical method to characterize MEPs by estimating the number of motor units and single motor unit potential amplitudes. A clearly increasing trend of single motor unit potential amplitudes in the MEPs after each pulse of the stimulation pulse train is revealed by this method. This statistical method eliminates the effects of anesthesia, and provides an objective assessment of MEPs. Consequently this statistical method has high potential to be useful in future quantitative MEPs analysis.

Original languageEnglish (US)
Title of host publication33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS 2011
Pages2001-2004
Number of pages4
DOIs
StatePublished - 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

Publication series

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

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

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Statistical model applied to motor evoked potentials analysis'. Together they form a unique fingerprint.

Cite this