A Hybrid Deep Spatio-Temporal Attention-Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals

TL;DR

This paper introduces a hybrid deep learning model combining CNN, Bi-GRU, and attention mechanisms to accurately diagnose Parkinson's disease from resting state EEG signals, demonstrating high performance and robustness across multiple datasets.

Contribution

The study develops a novel hybrid deep learning model that effectively extracts nonlinear features from EEG for early PD diagnosis, showing improved accuracy and generalizability.

Findings

High diagnostic accuracy on multiple datasets

Robust performance with incomplete input data

Potential for non-invasive early PD detection

Abstract

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.