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. 2022 Nov 3;22(21):8455.
doi: 10.3390/s22218455.

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

Farah Masood  1   2 , Milan Sharma  1 , Davleen Mand  1 , Shanker Nesathurai  3   4   5 , Heather A Simmons  3 , Kevin Brunner  3 , Dane R Schalk  3 , John B Sledge  6 , Hussein A Abdullah  1
Affiliations

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

Farah Masood et al. Sensors (Basel). .

Abstract

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.

Keywords: convolutional neural network; deep learning; electromyography; k-nearest neighbors; machine learning; non-human primate; spinal cord injury.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Convolutional neural network structure utilized to build the EMG-based TSCI classification system.
Figure 2
Figure 2
The accuracy curve for the EMG data classification of the left side using the proposed CNN architecture.
Figure 3
Figure 3
The loss curve for the EMG data classification of the left side using the proposed CNN architecture.
Figure 4
Figure 4
The accuracy curve for the EMG data classification of the right side using the proposed CNN architecture.
Figure 5
Figure 5
The loss curve for the EMG data classification of the right side using the proposed CNN architecture.
Figure 6
Figure 6
Evaluation metrics of the CNN and kNN classifiers (left side).
Figure 7
Figure 7
Evaluation metrics of the CNN and kNN classifiers (right side).
See this image and copyright information in PMC

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