Publication

Abstract : Parkinson Disease (PD) is a complex neurological disorder attributed by loss of neurons generating dopamine in the SN per compacta. Electroencephalogram (EEG) plays an important role in diagnosing PD as it offers a non-invasive continuous assessment of the disease progression and reflects these complex patterns. This study focuses on the non-linear analysis of resting state EEG signals in PD, with a gender-specific, brain region-specific, and EEG band-specific approach, utilizing recurrence plots (RPs) and machine learning (ML) algorithms for classification. For this an open EEG dataset consisting of 14 PD and 14 healthy (HC) subjects is utilized. Recurrence plots and cross-recurrence plots (CRPs) were constructed for each frequency band and brain region, extracting complexity measures such as determinism (DET) and entropy (ENT). The interpretability of the ML model decisions is investigated using explainability technique. The scattered distribution of points in RPs of male PD individuals reflects the complex and dynamic nature of abnormal brain function. Also, CRPs confirms the enhanced effect of Beta Gamma synchronization during PD in the Parietal region. Low DET and high ENT corresponds to the complex non-linear characteristics of EEG signals and brain neuronal circuits during PD condition in male subjects. The extracted recurrence features served as inputs to the ML models, which achieved high classification performance, across all the scenarios. This study demonstrates the potential of recurrence plot-based complexity analysis combined with machine learning for the gender-specific, region-specific, and band-specific assessment of EEG signals during resting state in Parkinson's disease.