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.

创伤性脊髓损伤是一种严重的神经系统疾病。患者会出现多种症状，这些症状可归因于受损的神经纤维束。这包括肢体无力、感觉障碍和躯干不稳定，以及各种自主神经异常。本文将讨论如何使用机器学习分类来表征非人类灵长类创伤性脊髓损伤模型中肌电图信号的初始损伤和随后的恢复。最终目标是确定创伤性脊髓损伤的潜在治疗方法。这项工作特别侧重于寻找合适的分类器，该分类器使用肌电信号区分两个不同的实验阶段（损伤前和损伤后）。从收集到的肌电图数据中提取了八个时域特征。为了克服不平衡的数据集问题，应用了合成少数过采样技术。应用了不同的ML分类技术，包括多层感知器、支持向量机、K-最近邻和径向基函数网络；然后比较了他们的表现。使用混淆矩阵和其他五个统计指标（灵敏度、特异性、精确度、准确度和 F 度量）来评估生成的分类器的性能。结果表明，左侧和右侧数据的最佳分类器是多层感知器，左侧和右侧的总 F-measure 分别为 79.5% 和 86.0%。