Automatic schizophrenic discrimination on fNIRS by using complex brain network analysis and SVM.
BMC Med Inform Decis Mak
; 17(Suppl 3): 166, 2017 12 20.
Article
en En
| MEDLINE
| ID: mdl-29297320
ABSTRACT
BACKGROUND:
Schizophrenia is a kind of serious mental illness. Due to the lack of an objective physiological data supporting and a unified data analysis method, doctors can only rely on the subjective experience of the data to distinguish normal people and patients, which easily lead to misdiagnosis. In recent years, functional Near-Infrared Spectroscopy (fNIRS) has been widely used in clinical diagnosis, it can get the hemoglobin concentration through the variation of optical intensity.METHODS:
Firstly, the prefrontal brain networks were constructed based on oxy-Hb signals from 52-channel fNIRS data of schizophrenia and healthy controls. Then, Complex Brain Network Analysis (CBNA) was used to extract features from the prefrontal brain networks. Finally, a classier based on Support Vector Machine (SVM) is designed and trained to discriminate schizophrenia from healthy controls. We recruited a sample which contains 34 healthy controls and 42 schizophrenia patients to do the one-back memory task. The hemoglobin response was measured in the prefrontal cortex during the task using a 52-channel fNIRS system.RESULTS:
The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 85.5%, 92.8% for schizophrenia samples and 76.5% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.CONCLUSIONS:
Our results suggested that, using the appropriate classification method, fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Esquizofrenia
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Corteza Prefrontal
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Espectroscopía Infrarroja Corta
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Neuroimagen Funcional
/
Máquina de Vectores de Soporte
Tipo de estudio:
Prognostic_studies
Límite:
Adult
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Female
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Humans
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Male
Idioma:
En
Revista:
BMC Med Inform Decis Mak
Asunto de la revista:
INFORMATICA MEDICA
Año:
2017
Tipo del documento:
Article
País de afiliación:
China