EMG Signal Classification for Neuromuscular Disorders with Attention-Enhanced CNN

TL;DR

This paper presents a deep learning approach using attention-enhanced CNNs to classify EMG signals for neuromuscular disorder diagnosis, achieving 92% accuracy in distinguishing Myopathy, Normal, and ALS signals.

Contribution

Introduces SpectroEMG-Net with attention mechanisms for multi-class EMG classification, improving diagnostic accuracy for neuromuscular disorders.

Findings

Achieved 92% overall accuracy in classifying three EMG signal classes.

Effectively distinguished ALS, Myopathy, and normal signals using spectral features.

Demonstrated robustness of the proposed deep learning model across datasets.

Abstract

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.