Pioneering Prognosis and Management in Neuromuscular Healthcare Using EMG Signal Processing with Advanced Deep Learning Techniques

A breakthrough new approach may be used to analyse Electromyography (EMG) data and diagnose neuromuscular illnesses in addition to the usual Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) models. This method is presented in this research article. This one-of-a-kind infrastructure, known as "NeuroFusionNet," is based on a groundbreaking hybrid deep learning architecture and employs cutting-edge signal processing techniques. State-of-the-art technologies such as advanced artefact removal and adaptive filtering are used in the preprocessing step to ensure excellent EMG signal quality. This helps to ensure that the signal is as high-quality as possible. To improve the feature extraction process, a proprietary algorithm capable of recognising complicated patterns in the time and frequency domains has been implemented. This is a completely different method than what is generally used. NeuroFusionNet, a unique neural network design, was recently developed. Their own design advantages are blended with those of deep convolutional structures. This architecture integrates both attention-based operational techniques and Graph Neural Network (GNN) concepts. Because it was created specifically to grasp the complex and non-linear connections present in EMG data, this architecture provides superior pattern recognition abilities. Furthermore, the method strives to be both durable and generalizable, which it does by employing a unique regularisation strategy to decrease the possibility of overfitting. The proposed technique provides a major improvement over the industry's primary competitors, which are deep learning models that are currently used for the categorization of neuromuscular disorders. It has the potential to totally alter EMG-based diagnostics by delivering a tool that is more accurate, efficient over time, and adaptable.