Muscle categorization using quantitative needle electromyography: A 2-stage Gaussian mixture model based approach

Meena Abdelmaseeh, Pascal Poupart, Benn Smith, Daniel Stashuk

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

1 Scopus citations

Abstract

Needle Electromyography, in combination with nerve conduction studies, is the gold standard methodology for assessing the neurophysiologic effects of neuromuscular diseases. Muscle categorization is typically based on visual and auditory assessment of the morphology and activation patterns of its constituent motor units. A procedure which is highly dependent on the skills and level of experience of the examiner. This motivates the development of automated or semi-automated categorization techniques. This paper describes a 2-stage Gaussian mixture model based approach. In the first stage, a muscle is classified as neurogenic or myopathic. The second stage uses a classifier specific to each disease category to confirm or refute the disease involvement. A total of 2556 motor unit potentials sampled from 48 normal, 30 neurogenic and 20 myopathic tibialis anterior muscles were utilized for this study. The proposed approach showed an average accuracy of 91.25%, which is higher than the compared linear and non-linear multi-class schemas. In addition to improved accuracy, the 2-stage approach is more suitable for the muscle categorization, because it has a hierarchical decision structure similar to current clinical practice, and its output can be interpreted as a measure of confidence.

Original languageEnglish (US)
Title of host publicationProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Pages548-553
Number of pages6
DOIs
StatePublished - Dec 1 2012
Event11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012 - Boca Raton, FL, United States
Duration: Dec 12 2012Dec 15 2012

Publication series

NameProceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Volume1

Other

Other11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
CountryUnited States
CityBoca Raton, FL
Period12/12/1212/15/12

Keywords

  • 2-stage approach
  • Decomposition based quantitative EMG
  • Gaussian Mixture Model
  • Muscle categorization
  • Needle EMG

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Education

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    Abdelmaseeh, M., Poupart, P., Smith, B., & Stashuk, D. (2012). Muscle categorization using quantitative needle electromyography: A 2-stage Gaussian mixture model based approach. In Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012 (pp. 548-553). [6406621] (Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012; Vol. 1). https://doi.org/10.1109/ICMLA.2012.100