[Research on electroencephalogram specifics in patients with schizophrenia under cognitive load].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
; 37(1): 45-53, 2020 Feb 25.
Article
em Zh
| MEDLINE
| ID: mdl-32096376
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, ß, 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.
Palavras-chave
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Esquizofrenia
/
Processamento de Sinais Assistido por Computador
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Cognição
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Eletroencefalografia
Tipo de estudo:
Prognostic_studies
/
Screening_studies
Limite:
Humans
Idioma:
Zh
Revista:
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
Assunto da revista:
ENGENHARIA BIOMEDICA
Ano de publicação:
2020
Tipo de documento:
Article