Resting-state electroencephalogram classification of patients with schizophrenia or depression.
10.7507/1001-5515.201812041
- Author:
Hongyu LAI
1
;
Jingwen FENG
1
;
Yi WANG
1
;
Wei DENG
2
;
Jinkun ZENG
2
;
Tao LI
3
;
Junpeng ZHANG
4
;
Kai LIU
1
Author Information
1. School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.
2. Mental Health Center, West China hospital, Sichuan University, Chengdu 610041, P.R.China.
3. Mental Health Center, West China hospital, Sichuan University, Chengdu 610041, P.R.China.xuntao26@hotmail.com.
4. School of Electrical Engineering and Information, Sichuan University, Chengdu 610065, P.R.China.Junpeng.zhang@gmail.com.
- Publication Type:Journal Article
- Keywords:
depression;
electroencephalogram;
feature extraction;
naive Bayes;
schizophrenia;
support vector machine
- MeSH:
Bayes Theorem;
Depression;
Electroencephalography;
Humans;
Schizophrenia;
Signal Processing, Computer-Assisted;
Support Vector Machine
- From:
Journal of Biomedical Engineering
2019;36(6):916-923
- CountryChina
- Language:Chinese
-
Abstract:
The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, β). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the β rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.
