Title: EMG scalogram-based classification of gait disorders using attention-based CNN: a comparative study of wavelet functions

Authors: Pranshu CBS Negi; Balendra; S.S. Pandey; Shiru Sharma; Neeraj Sharma

Addresses: School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India ' School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India ' Department of Orthopaedics, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India ' School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India ' School of Biomedical Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi, India

Abstract: This study aims to classify gait abnormalities caused by rheumatoid arthritis and prolapsed intervertebral disc using scalograms from the EMG signals. Classifying EMG signals is difficult because of their variability, high dimensionality, and sensor placement. We propose to bridge this gap by using the wavelet transform and attention-based neural networks. The study involved five participants: one with rheumatoid arthritis, two with prolapsed intervertebral disc, and two healthy subjects. The proposed methodology uses four different wavelet functions: complex Gaussian, frequency B Spline, Mexican Hat, and Shannon, to construct scalograms, and an attention-based CNN for classification. A comparison of performance of the proposed algorithm with nine machine learning classifiers: K nearest neighbour, Naïve Bayes, support vector machine, decision tree, logistic regression, random forest, AdaBoost, gradient boost, and XGBoost was conducted. Out of the nine machine learning classifiers that were tested, XGBoost achieved the highest accuracy of 90.38%, however, in comparison to this the performance of the proposed algorithm was much better, with an accuracy of 99% and precision of 99%. These results indicate that this approach is highly effective in accurately categorising EMG signals.

Keywords: electromyography; EMG; convolution neural networks; attention networks; scalogram; gait analysis.

DOI: 10.1504/IJBET.2024.140562

International Journal of Biomedical Engineering and Technology, 2024 Vol.45 No.4, pp.314 - 336

Received: 22 Jun 2023
Accepted: 24 Nov 2023
Published online: 23 Aug 2024 *

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