Sensor technology with gait as a diagnostic tool for assessment of Parkinson’s disease: a survey

Kour, Navleen; Gupta, Sunanda; Arora, Sakshi

doi:10.1007/s11042-022-13398-7

Cited by 6 publications

(1 citation statement)

References 180 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various studies have been conducted to automatically diagnose PD. Among these studies, some of them have focused on voice data [5][6][7] and some others such as [8][9][10] investigated gait signals to develop an automated PD diagnosis system. But lately, electroencephalography (EEG) has gained a lot of attention in the research community [11], and in recent years there have been several works done on automatic diagnosis of PD from an EEG record [12][13][14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Deep Spatiotemporal Attention‐Based Model for Parkinson's Disease Diagnosis Using Resting State EEG Signals

Delfan,

Shahsavari,

Hussain

et al. 2024

Int J Imaging Syst Tech

View full text Add to dashboard Cite

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 a 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, consisting of a 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, PRED‐CT, and University of Iowa [UI] dataset), with one dataset used for training and the other two for evaluation. The proposed model demonstrated remarkable performance, attaining high accuracy scores of 99.4%, 84%, and 73.2% using UC San Diego, PRED‐CT, and UI datasets, respectively. These results justify the effectiveness and robustness of the proposed model across diverse datasets, highlighting its potential for versatile applications in data analysis and prediction tasks. Our proposed hybrid spatiotemporal attention‐based model has been developed with 10‐fold cross‐validation (CV) for UC San Diego dataset and 10‐fold CV and leave‐one‐out cross‐validation (LOOCV) strategies for PRED‐CT and UI datasets. Our results indicate that the proposed PD detection system is accurate and robust. The developed prototype can be used for other neurodegenerative diseases such as Alzheimer's disease, Huntington's disease, and so forth.

show abstract