GaborPDNet: Gabor Transformation and Deep Neural Network for Parkinson’s Disease Detection Using EEG Signals

TLDR

Parkinson’s disease, the most common neurodegenerative movement disorder, is marked by loss of dopaminergic neurons and is often misdiagnosed using clinical features alone, prompting interest in noninvasive EEG biomarkers. This study proposes a deep‑learning model for automated PD diagnosis. EEG recordings from 16 healthy controls and 15 PD patients were transformed into spectrograms via Gabor transform and fed into a two‑dimensional convolutional neural network for training. The model achieved 99.46 % (±0.73) accuracy in ten‑fold cross‑validation for a three‑class task distinguishing healthy controls, medicated PD, and unmedicated PD, demonstrating strong potential for automated diagnosis and medication status detection.

Abstract

Parkinson’s disease (PD) is globally the most common neurodegenerative movement disorder. It is characterized by a loss of dopaminergic neurons in the substantia nigra of the brain. However, current methods to diagnose PD on the basis of clinical features of Parkinsonism may lead to misdiagnoses. Hence, noninvasive methods such as electroencephalographic (EEG) recordings of PD patients can be an alternative biomarker. In this study, a deep-learning model is proposed for automated PD diagnosis. EEG recordings of 16 healthy controls and 15 PD patients were used for analysis. Using Gabor transform, EEG recordings were converted into spectrograms, which were used to train the proposed two-dimensional convolutional neural network (2D-CNN) model. As a result, the proposed model achieved high classification accuracy of 99.46% (±0.73) for 3-class classification (healthy controls, and PD patients with and without medication) using tenfold cross-validation. This indicates the potential of proposed model to simultaneously automatically detect PD patients and their medication status. The proposed model is ready to be validated with a larger database before implementation as a computer-aided diagnostic (CAD) tool for clinical-decision support.