Resting-state electroencephalogram relevance state recognition of Parkinson's disease based on dynamic weighted symbolic mutual information and k-means clustering.
10.7507/1001-5515.202211002
- Author:
Hao DING
1
;
Jinhui WU
1
;
Xudong TANG
1
;
Jiangnan YU
1
;
Xuanheng CHEN
1
;
Zhanxiong WU
1
Author Information
1. School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, P. R. China.
- Publication Type:Journal Article
- Keywords:
Electroencephalogram;
Parkinson’s disease;
Weighted symbolic mutual information;
k-means clustering
- MeSH:
Humans;
Parkinson Disease/diagnosis*;
Quality of Life;
Cluster Analysis;
Electroencephalography;
Healthy Volunteers
- From:
Journal of Biomedical Engineering
2023;40(1):20-26
- CountryChina
- Language:Chinese
-
Abstract:
At present, the incidence of Parkinson's disease (PD) is gradually increasing. This seriously affects the quality of life of patients, and the burden of diagnosis and treatment is increasing. However, the disease is difficult to intervene in early stage as early monitoring means are limited. Aiming to find an effective biomarker of PD, this work extracted correlation between each pair of electroencephalogram (EEG) channels for each frequency band using weighted symbolic mutual information and k-means clustering. The results showed that State1 of Beta frequency band ( P = 0.034) and State5 of Gamma frequency band ( P = 0.010) could be used to differentiate health controls and off-medication Parkinson's disease patients. These findings indicated that there were significant differences in the resting channel-wise correlation states between PD patients and healthy subjects. However, no significant differences were found between PD-on and PD-off patients, and between PD-on patients and healthy controls. This may provide a clinical diagnosis reference for Parkinson's disease.
