TY - GEN
T1 - Automatic schizophrenia discrimination on fNIRS by using PCA and SVM
AU - Song, Hong
AU - Bogdan, Iordachescu Ilie Mihaita
AU - Wang, Shuliang
AU - Dong, Wentian
AU - Quan, Wenxiang
AU - Dang, Weimin
AU - Yu, Xin
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2017/1/17
Y1 - 2017/1/17
N2 - A method is proposed to distinguish patients with schizophrenia from healthy controls based on data measured by functional near-infrared spectroscopy (fNIRS) during a cognitive task, which combines principal component analysis (PCA) and support vector machine (SVM). Firstly, a data reduction technique is applied prior to PCA, and then PCA is used to extract features on oxygenated hemoglobin (oxy-Hb) signals from 52-channel fNIRS data of schizophrenia and healthy subjects. Secondly, a classifier based on SVM is designed to discriminate schizophrenia from healthy controls. We recruited a large sample of 52 schizophrenia patients and 38 healthy controls. The hemoglobin response was measured in the prefrontal cortex during the one-back memory task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 93.33%, 100% for schizophrenia samples and 84.62% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.
AB - A method is proposed to distinguish patients with schizophrenia from healthy controls based on data measured by functional near-infrared spectroscopy (fNIRS) during a cognitive task, which combines principal component analysis (PCA) and support vector machine (SVM). Firstly, a data reduction technique is applied prior to PCA, and then PCA is used to extract features on oxygenated hemoglobin (oxy-Hb) signals from 52-channel fNIRS data of schizophrenia and healthy subjects. Secondly, a classifier based on SVM is designed to discriminate schizophrenia from healthy controls. We recruited a large sample of 52 schizophrenia patients and 38 healthy controls. The hemoglobin response was measured in the prefrontal cortex during the one-back memory task using a 52-channel fNIRS system. The experimental results indicate that the proposed method can achieve a satisfactory classification with the accuracy of 93.33%, 100% for schizophrenia samples and 84.62% for healthy controls. Also, our results suggested that fNIRS has the potential capacity to be an effective objective biomarker for the diagnosis of schizophrenia.
KW - Functional Near-infrared Spectroscopy
KW - Principal Component Analysis
KW - Schizophrenia Discrimination
KW - Support Vector Machine (SVM)
UR - https://www.scopus.com/pages/publications/85013328741
U2 - 10.1109/BIBM.2016.7822550
DO - 10.1109/BIBM.2016.7822550
M3 - Conference contribution
AN - SCOPUS:85013328741
T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
SP - 389
EP - 394
BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
A2 - Burrage, Kevin
A2 - Zhu, Qian
A2 - Liu, Yunlong
A2 - Tian, Tianhai
A2 - Wang, Yadong
A2 - Hu, Xiaohua Tony
A2 - Jiang, Qinghua
A2 - Song, Jiangning
A2 - Morishita, Shinichi
A2 - Burrage, Kevin
A2 - Wang, Guohua
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Y2 - 15 December 2016 through 18 December 2016
ER -