Optimizing Parkinson's Disease Diagnosis through Hierarchical NeuroSignal Processing Techniques

Parkinson's Disease (PD), a progressive neurodegenerative disorder, poses significant diagnostic challenges, particularly in its early stages. This study proposes a novel hierarchical neurosignal processing framework to enhance the accuracy and timeliness of PD diagnosis. Leveraging multilevel signal decomposition, feature extraction, and machine learning classification, the proposed methodology systematically analyzes neurophysiological signals-such as EEG and EMG-capturing both spatial and temporal characteristics relevant to PD biomarkers. The hierarchical structure allows for refined signal denoising and targeted feature mapping across different neural frequency bands, significantly improving signal fidelity and diagnostic specificity. Experimental validation on benchmark datasets demonstrates that the proposed approach outperforms conventional flat processing models in terms of classification accuracy, sensitivity, and robustness. These findings suggest that hierarchical neurosignal processing can serve as a powerful diagnostic tool, paving the way for early intervention strategies and improved patient outcomes in Parkinson's Disease management.