Work

AI-Based Approaches for Automatic Prescreening of Parkinson’s Disease Using EEG Signal during Sleep

Year: 2025

Type: article

Abstract: Parkinson's disease (PD) is the second most common neurodegenerative disorder, and its prevalence is steadily increasing worldwide.However, diagnosing PD through clinical examination remains challenging and may be complicated by clinician subjectivity.In this study, we proposed an artificial intelligence-based method for the automatic prescreening of PD using nocturnal electroencephalography (EEG) signals recorded during various sleep stages.Patients with PD often exhibit rapid eye movement (REM) sleep behavior disorders that can manifest as abnormal EEG patterns during the REM stage.Therefore, we hypothesized that there would be detectable differences in sleep-state EEG patterns between patients with PD and healthy controls.Nocturnal EEG data were recorded during polysomnography in 65 participants.This study utilized pre-trained models followed by either random forest (TL-RF) or fully connected (TL-FC) layers.The results showed superior performance in the REM stage, with the TL-RF and TL-FC achieving F1-scores of 0.79 and TL-FC model attaining an F1-score of 0.83, respectively.In conclusion, we demonstrated the effectiveness of transfer learning-based models and the potential of REM-stage EEG features in assisting the diagnosis of PD.

Source: International Journal of Fuzzy Logic and Intelligent Systems

DOI: 10.5391/ijfis.2025.25.2.146

Authors: Somin Oh, Jaehoon Ko, Erdenebayar Urtnasan, Jaehong Key

Institution: Yonsei University Mirae Campus

Language: English

Topic: EEG and Brain-Computer Interfaces

Subfield: Cognitive Neuroscience

Field: Neuroscience

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: National Research Foundation, National Research Foundation of Korea