TY - GEN
T1 - Muscle categorization using quantitative needle electromyography
T2 - 11th IEEE International Conference on Machine Learning and Applications, ICMLA 2012
AU - Abdelmaseeh, Meena
AU - Poupart, Pascal
AU - Smith, Benn
AU - Stashuk, Daniel
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
KW - 2-stage approach
KW - Decomposition based quantitative EMG
KW - Gaussian Mixture Model
KW - Muscle categorization
KW - Needle EMG
UR - http://www.scopus.com/inward/record.url?scp=84873589331&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84873589331&partnerID=8YFLogxK
U2 - 10.1109/ICMLA.2012.100
DO - 10.1109/ICMLA.2012.100
M3 - Conference contribution
AN - SCOPUS:84873589331
SN - 9780769549132
T3 - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
SP - 548
EP - 553
BT - Proceedings - 2012 11th International Conference on Machine Learning and Applications, ICMLA 2012
Y2 - 12 December 2012 through 15 December 2012
ER -