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Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy
Li, Zhaohua1; Wang, Yuduo1; Quan, Wenxiang2,3,4; Wu, Tongning5; Lv, Bin5
关键词Schizophrenia Near-infrared spectroscopy (NIRS) Verbal fluency task (VFT) Principal component analysis (PCA) Support vector machine (SVM) Classification algorithm evaluation
刊名JOURNAL OF NEUROSCIENCE METHODS
2015-02-15
DOI10.1016/j.jneumeth.2014.12.020
241页:101-110
收录类别SCI
文章类型Article
WOS标题词Science & Technology
类目[WOS]Biochemical Research Methods ; Neurosciences
研究领域[WOS]Biochemistry & Molecular Biology ; Neurosciences & Neurology
关键词[WOS]VERBAL FLUENCY TASK ; BRAIN ACTIVATION ; DISCRIMINATIVE ANALYSIS ; PREFRONTAL ACTIVATION ; CORTICAL THICKNESS ; HEALTHY CONTROLS ; STATE ; CONNECTIVITY ; PATTERNS ; FMRI
英文摘要

Background: Based on near-infrared spectroscopy (NIRS), recent converging evidence has been observed that patients with schizophrenia exhibit abnormal functional activities in the prefrontal cortex during a verbal fluency task (VFT). Therefore, some studies have attempted to employ NIRS measurements to differentiate schizophrenia patients from healthy controls with different classification methods. However, no systematic evaluation was conducted to compare their respective classification performances on the same study population.

New method: In this study, we evaluated the classification performance of four classification methods (including linear discriminant analysis, k-nearest neighbors, Gaussian process classifier, and support vector machines) on an NIRS-aided schizophrenia diagnosis. We recruited a large sample of 120 schizophrenia patients and 120 healthy controls and measured the hemoglobin response in the prefrontal cortex during the VFT using a multichannel NIRS system. Features for classification were extracted from three types of NIRS data in each channel. We subsequently performed a principal component analysis (PCA) for feature selection prior to comparison of the different classification methods.

Results: We achieved a maximum accuracy of 85.83% and an overall mean accuracy of 83.37% using a PCA-based feature selection on oxygenated hemoglobin signals and support vector machine classifier. Comparison with existing methods: This is the first comprehensive evaluation of different classification methods for the diagnosis of schizophrenia based on different types of NIRS signals.

Conclusions: Our results suggested that, using the appropriate classification method, NIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia. (C) 2014 Elsevier B.V. All rights reserved.

语种英语
WOS记录号WOS:000350188100012
引用统计
被引频次:7[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.bjmu.edu.cn/handle/400002259/64655
专题北京大学精神卫生研究所
北京大学精神卫生研究所_精神科
作者单位1.Peking Univ, Sixth Hosp, Beijing 100871, Peoples R China
2.Peking Univ, Inst Mental Hlth, Beijing 100871, Peoples R China
3.Beijing Informat Sci & Technol Univ, Sch Informat & Commun Engn, Beijing, Peoples R China
4.Peking Univ, Minist Hlth, Key Lab Mental Hlth, Beijing 100871, Peoples R China
5.China Acad Telecommun Res, Minist Ind & Informat Technol, Beijing 100191, Peoples R China
推荐引用方式
GB/T 7714
Li, Zhaohua,Wang, Yuduo,Quan, Wenxiang,et al. Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy[J]. JOURNAL OF NEUROSCIENCE METHODS,2015,241:101-110.
APA Li, Zhaohua,Wang, Yuduo,Quan, Wenxiang,Wu, Tongning,&Lv, Bin.(2015).Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy.JOURNAL OF NEUROSCIENCE METHODS,241,101-110.
MLA Li, Zhaohua,et al."Evaluation of different classification methods for the diagnosis of schizophrenia based on functional near-infrared spectroscopy".JOURNAL OF NEUROSCIENCE METHODS 241(2015):101-110.
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