Amyotrophic Lateral Sclerosis (ALS) and Myopathy present considerable challenges in the realm of neuromuscular disorder diagnostics. In this study, we employ advanced deep-learning techniques to address the detection of ALS and Myopathy, two debilitating conditions. Our methodology begins with the extraction of informative features from raw electromyography (EMG) signals, leveraging the Log-spectrum, and Delta Log spectrum, which capture the frequency contents, and spectral and temporal characteristics of the signals. Subsequently, we applied a deep-learning model, SpectroEMG-Net, combined with Convolutional Neural Networks (CNNs) and Attention for the classification of three classes. The robustness of our approach is rigorously evaluated, demonstrating its remarkable performance in distinguishing among the classes: Myopathy, Normal, and ALS, with an outstanding overall accuracy of 92\%. This study marks a contribution to addressing the diagnostic challenges posed by neuromuscular disorders through a data-driven, multi-class classification approach, providing valuable insights into the potential for early and accurate detection.
@article{arxiv.2309.10483,
title = {EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN},
author = {Md. Toufiqur Rahman and Minhajur Rahman and Celia Shahnaz},
journal= {arXiv preprint arXiv:2309.10483},
year = {2024}
}
Comments
We have identified an error in the methodology and calculations presented in our paper, which affects the validity of our results. We plan to correct these issues and resubmit once we have verified the findings