Study on classification and identification of depressed patients and healthy people among adolescents based on optimization of brain characteristics of network.
10.7507/1001-5515.201908003
- Author:
Xiaotong SHEN
1
,
2
;
Yue WANG
1
,
2
;
Hui BI
1
,
2
;
Yin CAO
3
;
Suhong WANG
4
;
Ling ZOU
1
,
2
Author Information
1. School of Information Science and Engineering, Chang Zhou University, Changzhou, Jiangsu 213164, P.R.China
2. Changzhou Key Laboratory of Biomedical Information Technology, Changzhou, Jiangsu 213164, P.R.China.
3. Changzhou NO.2 People's Hospital, Chang Zhou, Jiangsu 213003, P.R.China.
4. The First People's Hospital of Changzhou, Chang Zhou, Jiangsu 213003, P.R.China.
- Publication Type:Journal Article
- Keywords:
computer-aided diagnosis;
depression;
electroencephalogram;
feature optimization;
network;
threshold
- MeSH:
Adolescent;
Brain/diagnostic imaging*;
Diagnosis, Computer-Assisted;
Electroencephalography;
Female;
Humans;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2020;37(6):1037-1044
- CountryChina
- Language:Chinese
-
Abstract:
To enhance the accuracy of computer-aided diagnosis of adolescent depression based on electroencephalogram signals, this study collected signals of 32 female adolescents (16 depressed and 16 healthy, age: 16.3 ± 1.3) with eyes colsed for 4 min in a resting state. First, based on the phase synchronization between the signals, the phase-locked value (PLV) method was used to calculate brain functional connectivity in the θ and α frequency bands, respectively. Then based on the graph theory method, the network parameters, such as strength of the weighted network, average characteristic path length, and average clustering coefficient, were calculated separately (
