Identification of patients with de novo Parkinson's Disease from chemosensory EEG signals using ICEEMDAN domain Entropy Features

Parkinson's disease (PD) is a progressive neurodegenerative disorder that impairs motor and sensory functions, with early symptoms often involving olfactory dysfunction. Given the importance of detecting these early biomarkers for timely intervention, this letter proposes the novel use of chemosensory EEG for the early detection of PD, as it captures the brain's responses to olfactory stimuli, one of the primary sensory modalities affected by the disease. The proposed method employs an improved complete ensemble empirical mode decomposition with adaptive noise to decompose EEG signals into intrinsic mode functions (IMFs). Entropic features, including approximate entropy, sample entropy, and Rényi permutation entropy (RpEn), are extracted from these IMFs to identify distinguishing characteristics. These features are then evaluated using several machine learning classifiers. A comprehensive evaluation reveals that combining RpEn features with least squares support vector machine classifier achieves optimal performance, with an accuracy of 96.47%, a precision of 96.14%, and a kappa score of 0.95.