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Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine
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...
Autores principales: | , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483285/ https://www.ncbi.nlm.nih.gov/pubmed/37694121 http://dx.doi.org/10.3389/fnins.2023.1205931 |
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author | Yi, Li Xie, Guojun Li, Zhihao Li, Xiaoling Zhang, Yizheng Wu, Kai Shao, Guangjian Lv, Biliang Jing, Huan Zhang, Chunguo Liang, Wenting Sun, Jinyan Hao, Zhifeng Liang, Jiaquan |
author_facet | Yi, Li Xie, Guojun Li, Zhihao Li, Xiaoling Zhang, Yizheng Wu, Kai Shao, Guangjian Lv, Biliang Jing, Huan Zhang, Chunguo Liang, Wenting Sun, Jinyan Hao, Zhifeng Liang, Jiaquan |
author_sort | Yi, Li |
collection | PubMed |
description | 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 < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level. |
format | Online Article Text |
id | pubmed-10483285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-104832852023-09-08 Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine Yi, Li Xie, Guojun Li, Zhihao Li, Xiaoling Zhang, Yizheng Wu, Kai Shao, Guangjian Lv, Biliang Jing, Huan Zhang, Chunguo Liang, Wenting Sun, Jinyan Hao, Zhifeng Liang, Jiaquan Front Neurosci Neuroscience 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 < 0.05) and showed high depression distinguishing ability indicating that they may be effective biological markers for identifying depression. EEG, fNIRS and machine learning constitute an effective method for classifying depression at the individual level. Frontiers Media S.A. 2023-08-24 /pmc/articles/PMC10483285/ /pubmed/37694121 http://dx.doi.org/10.3389/fnins.2023.1205931 Text en Copyright © 2023 Yi, Xie, Li, Li, Zhang, Wu, Shao, Lv, Jing, Zhang, Liang, Sun, Hao and Liang. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Yi, Li Xie, Guojun Li, Zhihao Li, Xiaoling Zhang, Yizheng Wu, Kai Shao, Guangjian Lv, Biliang Jing, Huan Zhang, Chunguo Liang, Wenting Sun, Jinyan Hao, Zhifeng Liang, Jiaquan Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine |
title | Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine |
title_full | Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine |
title_fullStr | Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine |
title_full_unstemmed | Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine |
title_short | Automatic depression diagnosis through hybrid EEG and near-infrared spectroscopy features using support vector machine |
title_sort | automatic depression diagnosis through hybrid eeg and near-infrared spectroscopy features using support vector machine |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10483285/ https://www.ncbi.nlm.nih.gov/pubmed/37694121 http://dx.doi.org/10.3389/fnins.2023.1205931 |
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