Neurophysiological Characterization of a Non-Human Primate Model of Traumatic Spinal Cord Injury Utilizing Fine-Wire EMG Electrodes

A Novel Application of Deep Learning (Convolutional Neural Network) for Traumatic Spinal Cord Injury Classification Using Automatically Learned Features of EMG Signal

In this study, a traumatic spinal cord injury (TSCI) classification system is proposed using a convolutional neural network (CNN) technique with automatically learned features from electromyography (EMG) signals for a non-human primate (NHP) model. A comparison between the proposed classification system and a classical classification method (k-nearest neighbors, kNN) is also presented. Developing such an NHP model with a suitable assessment tool (i.e., classifier) is a crucial step in detecting the effect of TSCI using EMG, which is expected to be essential in the evaluation of the efficacy of new TSCI treatments. Intramuscular EMG data were collected from an agonist/antagonist tail muscle pair for the pre- and post-spinal cord lesion from five Macaca fasicularis monkeys. The proposed classifier is based on a CNN using filtered segmented EMG signals from the pre- and post-lesion periods as inputs, while the kNN is designed using four hand-crafted EMG features. The results suggest that the CNN provides a promising classification technique for TSCI, compared to conventional machine learning classification. The kNN with hand-crafted EMG features classified the pre- and post-lesion EMG data with an F-measure of 89.7% and 92.7% for the left- and right-side muscles, respectively, while the CNN with the EMG segments classified the data with an F-measure of 89.8% and 96.9% for the left- and right-side muscles, respectively. Finally, the proposed deep learning classification model (CNN), with its learning ability of high-level features using EMG segments as inputs, shows high potential and promising results for use as a TSCI classification system. Future studies can confirm this finding by considering more subjects.

Building capacity through open approaches: Lessons from developing undergraduate electrophysiology practicals

Background: Electrophysiology has a wide range of biomedical research and clinical applications. As such, education in the theoretical basis and hands-on practice of electrophysiological techniques is essential for biomedical students, including at the undergraduate level. However, offering hands-on learning experiences is particularly difficult in environments with limited resources and infrastructure. Methods: In 2017, we began a project to design and incorporate electrophysiology laboratory practicals into our Biomedical Physics undergraduate curriculum at the Universidad Nacional Autónoma de México. We describe some of the challenges we faced, how we maximized resources to overcome some of these challenges, and in particular, how we used open scholarship approaches to build both educational and research capacity. Results: We succeeded in developing a number of experimental and data analysis practicals in electrophysiology, including electrocardiogram, electromyogram, and electrooculogram techniques. The use of open tools, open platforms, and open licenses was key to the success and broader impact of our project. We share examples of our practicals and explain how we use these activities to strengthen interdisciplinary learning, namely the application of concepts in physics to understanding functions of the human body. Conclusions: Open scholarship provides multiple opportunities for universities to build capacity. Our goal is to provide ideas, materials, and strategies for educators working in similar resource-limited environments.

Comparison study of classification methods of intramuscular electromyography data for non-human primate model of traumatic spinal cord injury

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.