Data Augmentation of Electromyography Signal for Neuromuscular Disorders Diagnosis

Abstract

This paper presents a novel approach for diagnosing neuromuscular disorders, specifically Myopathy and amyotrophic lateral sclerosis (ALS), from electromyography (EMG) signals using a data augmentation-enhanced convolutional neural network (CNN) with an integrated attention mechanism. Given the limited availability and variability of labeled EMG data, we employed multiple data augmentation techniques including noise addition, time warping, scaling, magnitude warping, and jittering, to expand the dataset and create a more robust model training process. Each augmentation method was evaluated through distinct multiclass classification tasks using a CNN model, enhanced with attention blocks that focus on the most relevant temporal and spatial features within the EMG signals. Our results demonstrated that the CNN model, with attention mechanisms, achieved high classification accuracy of 98.49% using jittering technique, showcasing the effectiveness of our approach in improving the early diagnosis of neuromuscular disorders.