Parkinson’s Disease Detection From Electrical Stimulations WiFi Signals Using Information Fusion of Proposed Neural Networks

Parkinson’s disease (PD) is a degenerative, chronic neurological condition that impairs a person’s ability to move normally. People may experience difficulties with speaking, writing, walking, or performing basic tasks if dopamine-generating neurons in the brain are injured or die. Using traditional techniques for PD analysis is time-consuming and challenging, as the evaluation process is prone to high misclassification rates. Therefore, we proposed a deep learning-based architecture for classifying PD using WiFi signals in this work. The data is generated at the initial stage using WiFi signals. After that, we proposed two deep learning architectures from scratch. The first architecture, named E3-ST transformer, is based on a three-stage encoding scheme, and the second two residual-attention-block-based networks are named PD-RAN2. Both models are trained on generated WiFi signal data, and the hyperparameters are optimized using Bayesian Optimization (BO). In the next phase, trained models are used, and deep features are incorporated, employing a new method termed serial-based attention-weighted. The fused features are finally classified using neural network classifiers. The output is in label classes such as slow walking, fast walking, sitting on a chair, standing still, and FOG episodes. The Medium Neural Network (MN2) classifier achieved the best accuracy of 97.78%, whereas the individual models achieved 97.0% and 97.70%, respectively. A comparison with recent techniques shows that the proposed architectures achieve improved performance.