Research on electroencephalogram specifics in patients with schizophrenia under cognitive load.
10.7507/1001-5515.201810007
- Author:
Xin DU
1
,
2
;
Jiahui LI
1
,
2
;
Dongsheng XIONG
1
,
2
;
Zhilin PAN
1
,
2
;
Fengchun WU
3
,
4
;
Yuping NING
3
,
4
;
Jun CHEN
5
,
6
;
Kai WU
1
,
3
,
5
,
6
,
7
Author Information
1. Department of Biomedical Engineering, School of Material Science and Engineering, South China University of Technology, Guangzhou 510006, P.R.China
2. Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, P.R.China.
3. Guangdong Engineering Technology Research Center for Translational Medicine of Mental Disorders, Guangzhou 510370, P.R.China
4. Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, P.R.China.
5. Guangdong Engineering Technology Research Center for Diagnosis and Rehabilitation of Dementia, Guangzhou 510500, P.R.China
6. National Engineering Research Center for Healthcare Devices, Guangzhou 510500, P.R.China.
7. Guangzhou Huiai Hospital, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou 510370, P.R.China
- Publication Type:Journal Article
- Keywords:
cognitive impairment;
electroencephalogram;
functional brain network;
machine learning;
schizophrenia
- From:
Journal of Biomedical Engineering
2020;37(1):45-53
- CountryChina
- Language:Chinese
-
Abstract:
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.
