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

Masood, Farah; Abdullah, Hussein A.; Seth, Nitin; Simmons, Heather A.; Brunner, Kevin; Sejdić, Ervin; Schalk, Dane; Graham, William A.; Hoggatt, Amber; Rosene, Douglas L.; Sledge, John B.; Nesathurai, Shanker

doi:10.3390/s19153303

Cited by 3 publications

(6 citation statements)

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“…The experimental methods are described in detail elsewhere. 2,3,14 To provide context for this article, the following section has been restated from a previous publication. The subjects were six adult Macaca fascicularis monkeys.…”

Section: Methodsmentioning

confidence: 99%

“…This article will discuss how machine learning (ML) classification can be used to characterize the initial impairment and subsequent recovery of EMG signals in an NHP model of TSCI. The details of the model have been published elsewhere, 2,3,14 but the key details of the model are described in the methods section. The EMG signals are obtained from implanted fine wire electrodes, with the subject moving freely.…”

Section: Motivation: Emg As a Biomarker In Humane Nonhuman Model Of Tmentioning

confidence: 99%

“…In previous articles, the EMG data were analyzed with a focus on the number of peaks in the envelope 3 as well as utilizing wavelet transformation. 14 The ultimate motivation is to create a robust biomarker of impairment and recovery in this humane NHP model of TSCI. The objective of this model is to ultimately identify potential treatments for TSCI.…”

Section: Motivation: Emg As a Biomarker In Humane Nonhuman Model Of Tmentioning

confidence: 99%

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

Masood

Farzana

Nesathurai

et al. 2020

Proc Inst Mech Eng H

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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.

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Section: Methodsmentioning

confidence: 99%

Section: Motivation: Emg As a Biomarker In Humane Nonhuman Model Of Tmentioning

confidence: 99%

Section: Motivation: Emg As a Biomarker In Humane Nonhuman Model Of Tmentioning

confidence: 99%

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

Masood

Farzana

Nesathurai

et al. 2020

Proc Inst Mech Eng H

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“…EMG signals were initially analyzed based on a number of spikes in the aggregate signal [ 4 ], followed by more sophisticated approaches, such as those based on wavelet analysis [ 10 ] and machine learning [ 11 ]. Consequently, in the present work, we propose a new TSCI classification system using raw EMG segments and deep learning (DL) classification through the use of a convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

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

Masood

Sharma

Mand

et al. 2022

Sensors

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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.

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“…Electrophysiological techniques, like electromyogram (EMG), electrocardiogram (ECG), and electroencephalogram (EEG) recording, are commonly used in both clinical settings and biomedical research. For example, EMG recordings are used to study neuromuscular disorders 1 and spinal cord injury 2 , 3 ; ECG recordings are used to detect cardiac conduction disorders 4 and heart attack 5 ; and EEG recordings are used to study epileptic seizures 6 , 7 and sleep disorders 8 , 9 . Considering the importance of these techniques, it is vital that biomedical students receive training in their physiological basis, how to perform recordings, and how to analyze electrophysiological data, starting preferably at the undergraduate level.…”

Section: Introductionmentioning

confidence: 99%

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

McKiernan

Gómez

2021

F1000Res

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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.

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

Cited by 3 publications

References 49 publications

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

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

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

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

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