  Parkinson's disease (PD), a severe and progressive neurological illness,
affects millions of individuals worldwide. For effective treatment and
management of PD, an accurate and early diagnosis is crucial. This study
presents a deep learning-based model for the diagnosis of PD using resting
state electroencephalogram (EEG) signal. The objective of the study is to
develop an automated model that can extract complex hidden nonlinear features
from EEG and demonstrate its generalizability on unseen data. The model is
designed using a hybrid model, consists of convolutional neural network (CNN),
bidirectional gated recurrent unit (Bi-GRU), and attention mechanism. The
proposed method is evaluated on three public datasets (Uc San Diego Dataset,
PRED-CT, and University of Iowa (UI) dataset), with one dataset used for
training and the other two for evaluation. The results show that the proposed
model can accurately diagnose PD with high performance on both the training and
hold-out datasets. The model also performs well even when some part of the
input information is missing. The results of this work have significant
implications for patient treatment and for ongoing investigations into the
early detection of Parkinson's disease. The suggested model holds promise as a
non-invasive and reliable technique for PD early detection utilizing resting
state EEG.
