Fuzzy relevance vector machine-based segmentation and adaptive network-based fuzzy inference systems for Parkinson’s disease detection and classification

Parkinson's disease (PD) is the second most prevalent neurodegenerative condition, yet the use of EEG signals for its differentiation from healthy controls remains underexplored. This paper presents a novel approach that leverages complex, non-stationary, and non-linear EEG signals for PD detection. We employ Butterworth filtering for preprocessing, followed by fuzzy relevance vector machine segmentation to effectively manage the extensive data points. Key features are extracted using a fast scale invariant feature transform, and classification is performed using an adaptive network-based fuzzy inference system (ANFIS). Our proposed method achieves an exceptional classification accuracy of 99.2%, significantly outperforming existing techniques. This work not only enhances the diagnostic accuracy for early-stage PD but also addresses the challenges of data management in clinical settings, thereby contributing to the development of more efficient diagnostic tools in neurology.