Evaluation of Parkinson's disease early diagnosis using single-channel EEG features and auditory cognitive assessment.

ABSTRACT:

Background

Parkinson's disease (PD) often presents with subtle early signs, making diagnosis difficult. F-DOPA PET imaging provides a reliable measure of dopaminergic function and is a primary tool for early PD diagnosis. This study aims to evaluate the ability of machine-learning (ML) extracted EEG features to predict F-DOPA results and distinguish between PD and non-PD patients. These features, extracted using a single-channel EEG during an auditory cognitive assessment, include EEG feature A0 associated with cognitive load in healthy subjects, and EEG feature L1 associated with cognitive task differentiation.

Methods

Participants in this study are comprised of cognitively healthy patients who had undergone an F-DOPA PET scan as a part of their standard care (n = 32

SUBMITTER: Molcho L 

PROVIDER: S-EPMC10762798 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature