Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury. (September 2020)

• Record Type: Journal Article
• Authors: Masood, Farah; Farzana, Maisha; Nesathurai, Shanker; Abdullah, Hussein A.
• Is Part Of: Proceedings of the Institution of Mechanical Engineers. Volume 234:Number 9(2020)
• Page Start: 955
• Page End: 965
• Publication Date: 2020-09
• Subjects: Electromyography -- non-human primate model -- traumatic spinal cord injury -- classification -- MLP -- SVM -- KNN -- RBFN -- SMOTE; Biomedical engineering -- Periodicals; Medical instruments and apparatus -- Periodicals; 610.28
• DOI: 10.1177/0954411920935741
• Languages: English
• ISSNs: 0954-4119

Abstract

Traumatic spinal cord injury is a serious neurological disorder. Patients experience a plethora of symptoms that can be attributed to the nerve fiber tracts that are compromised. This includes limb weakness, sensory impairment, and truncal instability, as well as a variety of autonomic abnormalities. This article will discuss how machine learning classification can be used to characterize the initial impairment and subsequent recovery of electromyography signals in an non-human primate model of traumatic spinal cord injury. The ultimate objective is to identify potential treatments for traumatic spinal cord injury. This work focuses specifically on finding a suitable classifier that differentiates between two distinct experimental stages (pre-and post-lesion) using electromyography signals. Eight time-domain features were extracted from the collected electromyography data. To overcome the imbalanced dataset issue, synthetic minority oversampling technique was applied. Different ML classification techniques were applied including multilayer perceptron, support vector machine, K-nearest neighbors, and radial basis function network; then their performances were compared. A confusion matrix and five other statistical metrics (sensitivity, specificity, precision, accuracy, and F-measure) were used to evaluate the performance of the generated classifiers. The results showed that the best classifier for the left- and right-side data is the multilayer perceptron with a total F-measure of 79.5% and 86.0% for the left and right sides, respectively. This work will help to build a reliable classifier that can differentiate between these two phases by utilizing some extracted time-domain electromyography features.