School of Engineering, University of Guelph, Guelph, ON N1G 2W1, Canada.
The Department of Biomedical Engineering, Al-Khwarizmi College of Engineering, Baghdad University, Baghdad 10071, Iraq.
Sensors (Basel). 2022 Nov 3;22(21):8455. doi: 10.3390/s22218455.
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 (-nearest neighbors, NN) 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 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 NN 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 NN 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.
在这项研究中，提出了一种使用卷积神经网络（CNN）技术的外伤性脊髓损伤（TSCI）分类系统，该技术从肌电图（EMG）信号中自动学习特征，用于非人类灵长类动物（NHP）模型。还提出了一种将所提出的分类系统与经典分类方法（-最近邻，NN）进行比较。使用适当的评估工具（即分类器）开发这种 NHP 模型是使用 EMG 检测 TSCI 效果的关键步骤，这对于评估新的 TSCI 治疗效果至关重要。从 5 只猴子的术前和术后脊髓损伤采集了一对拮抗肌/主动肌尾部肌肉的肌内 EMG 数据。所提出的分类器基于使用术前和术后 EMG 信号滤波分段作为输入的 CNN，而 NN 则使用四个手工制作的 EMG 特征设计。结果表明，与传统机器学习分类相比，CNN 为 TSCI 提供了一种有前途的分类技术。使用手工制作的 EMG 特征的 NN 对术前和术后 EMG 数据的分类准确率分别为 89.7%和 92.7%，用于左侧和右侧肌肉，而使用 EMG 段的 CNN 对左侧和右侧肌肉的分类准确率分别为 89.8%和 96.9%。最后，所提出的深度学习分类模型（CNN），使用 EMG 段作为输入来学习高级特征，显示出作为 TSCI 分类系统的高潜力和有前途的结果。未来的研究可以通过考虑更多的对象来证实这一发现。
