Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine

Autor: Li Yi, Guojun Xie, Zhihao Li, Xiaoling Li, Yizheng Zhang, Kai Wu, Guangjian Shao, Biliang Lv, Huan Jing, Chunguo Zhang, Wenting Liang, Jinyan Sun, Zhifeng Hao, Jiaquan Liang
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Frontiers in Neuroscience, Vol 17 (2023)
Druh dokumentu: article
ISSN: 1662-453X
DOI: 10.3389/fnins.2023.1205931
Popis: Depression is a common mental disorder that seriously affects patients’ social function and daily life. Its accurate diagnosis remains a big challenge in depression treatment. In this study, we used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) and measured the whole brain EEG signals and forehead hemodynamic signals from 25 depression patients and 30 healthy subjects during the resting state. On one hand, we explored the EEG brain functional network properties, and found that the clustering coefficient and local efficiency of the delta and theta bands in patients were significantly higher than those in normal subjects. On the other hand, we extracted brain network properties, asymmetry, and brain oxygen entropy as alternative features, used a data-driven automated method to select features, and established a support vector machine model for automatic depression classification. The results showed the classification accuracy was 81.8% when using EEG features alone and increased to 92.7% when using hybrid EEG and fNIRS features. The brain network local efficiency in the delta band, hemispheric asymmetry in the theta band and brain oxygen sample entropy features differed significantly between the two groups (p
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