Faculty Collaboration Database

Machine Learning for Supporting Diagnosis of Amyotrophic Lateral Sclerosis Using Surface Electromyogram. IEEE Trans Neural Syst Rehabil Eng 2014 Jan;22(1):96-103

Date

09/03/2013

Pubmed ID

23996581

DOI

10.1109/TNSRE.2013.2274658

Scopus ID

2-s2.0-84892404505 (requires institutional sign-in at Scopus site)   39 Citations

Abstract

Needle electromyogram (EMG) is routinely used in clinical neurophysiology for examination of neuromuscular diseases. This study presents a noninvasive surface EMG method for supporting diagnosis of amyotrophic lateral sclerosis (ALS). Three diagnostic markers including the clustering index, the kurtosis of EMG amplitude histogram, and the kurtosis of EMG crossing-rate expansion, were used respectively to characterize surface EMG patterns recorded during different levels of voluntary muscle contraction. We then applied a linear discriminant analysis classifier to discriminate the ALS subjects from the neurologically intact subjects, using the statistics derived from all the three markers as input feature sets to the classifier. The method was tested in 10 ALS subjects and 11 neurologically intact subjects. Combination of the three surface EMG markers achieved 90% diagnostic sensitivity and 100% diagnostic specificity, which were higher than solely using a single surface EMG marker. Given the high diagnostic yield, the proposed surface EMG analysis can be used as a supplement to needle EMG examination in supporting the diagnosis of ALS.

Author List

Zhang X, Barkhaus PE, Rymer WZ, Zhou P

Author

Paul E. Barkhaus MD Professor in the Neurology department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Aged
Algorithms
Amyotrophic Lateral Sclerosis
Diagnosis, Computer-Assisted
Electromyography
Female
Humans
Machine Learning
Male
Middle Aged
Muscle Contraction
Muscle, Skeletal
Pattern Recognition, Automated
Reproducibility of Results
Sensitivity and Specificity