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

by   Niloufar Delfan, et al.

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.


page 12

page 13


MP-SeizNet: A Multi-Path CNN Bi-LSTM Network for Seizure-Type Classification Using EEG

Seizure type identification is essential for the treatment and managemen...

Adversarial Representation Learning for Robust Patient-Independent Epileptic Seizure Detection

Objective: Epilepsy is a chronic neurological disorder characterized by ...

Email Spam Detection Using Hierarchical Attention Hybrid Deep Learning Method

Email is one of the most widely used ways to communicate, with millions ...

Gated Convolutional Bidirectional Attention-based Model for Off-topic Spoken Response Detection

Off-topic spoken response detection, the task aiming at assessing whethe...

An EEG-based approach for Parkinson's disease diagnosis using Capsule network

As the second most common neurodegenerative disease, Parkinson's disease...

Depression Diagnosis and Drug Response Prediction via Recurrent Neural Networks and Transformers Utilizing EEG Signals

The Early diagnosis and treatment of depression is essential for effecti...

BiCurNet: Pre-Movement EEG based Neural Decoder for Biceps Curl Trajectory Estimation

Kinematic parameter (KP) estimation from early electroencephalogram (EEG...

Please sign up or login with your details

Forgot password? Click here to reset