|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|
|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|
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.
|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
|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.|