Optimized Time-domain Feature Extraction for Early Onset Diagnosis of Parkinson Disease From EEG Signals - Delshad Ghavami; Moein Radman; Ali Chaibakhsh (Caspian Journal of Neurological Sciences)
Background: Early and accurate diagnosis of Parkinson disease (PD) is essential for enhancing patients’ quality of life and enabling more effective symptom management. Brain signal analysis, a non-invasive and reliable technique, provides an alternative or complementary method to traditional diagnostic approaches. Objectives: This study aims to develop a diagnostic method for PD by combining signal processing techniques with machine learning (ML) algorithms. Materials & Methods: Electroencephalography (EEG) signals were initially segmented into smaller windows using a windowing technique. The intrinsic mode functions (IMFs) were subsequently derived using the empirical mode decomposition (EMD) technique. The second-order difference plot (SODP) method was applied to each IMF, and components with higher informational content were selected for feature extraction. These features were subsequently used to train a decision tree classifier. Various window lengths were evaluated to determine the optimal time window for feature extraction, with 4 seconds identified as the optimal duration. Results: The proposed method was evaluated using the San Diego EEG dataset, which demonstrated state-of-the-art performance compared to existing studies. The classification accuracies achieved for various scenarios were as follows: 99.7% for open-eyes off–PD vs healthy controls (HCs), 96.7% for open-eyes on–PD vs HC, and 98.54% for open-eyes off–PD vs on–PD. Conclusion: The results underscore the strong potential of the proposed method in effectively addressing key classification challenges associated with Parkinson’s disease.