Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector

Hanshu Cai; Xiaocong Sha; Xue Han; Shixin Wei; Bin Hu

doi:10.1109/BIBM.2016.7822696

Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector

Hanshu Cai, Xiaocong Sha, Xue Han, Shixin Wei, Bin Hu

Lanzhou University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

70 Citations (Scopus)

Abstract

According to the World Health Organization, it is predicted that in 2020, depression will become the second largest illness threatening the health of mankind. In order to alleviate the worldwide damage caused by depression, a portable and accurate diagnosing technique is the most essential. This research uses three-electrode pervasive EEG collector to collect EEG data from Fp1, Fp2, and Fpz as locations of scalp electrodes, since these locations are closely related to emotions, and uncovered by hair. Special designed experiment has been conducted and totally 178 subjects' EEG data have been collected. Then the research uses KNN (k-Nearest Neighbor), SVM (Support Vector Machine), ANN (Artificial Neuro Network) and DBN (Deep Belief Network) to analyze the data. The results show DBN performed better than traditional methods using shallow algorithms. Moreover, the results suggested the absolute power of beta wave is a valid characteristic, which could be used for detection of depression. The accuracy reached 78.24% using the combination of DBN and the absolute power of beta wave. This research proves the feasibility of smaller-size pervasive system for depression diagnosis.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1239-1246
Number of pages	8
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822696
Publication status	Published - 17 Jan 2017
Externally published	Yes
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

Deep belief network
Depression diagnosis
EEG
Pervasive system

Access to Document

10.1109/BIBM.2016.7822696

Cite this

Cai, H., Sha, X., Han, X., Wei, S., & Hu, B. (2017). Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 1239-1246). Article 7822696 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822696

Cai, Hanshu ; Sha, Xiaocong ; Han, Xue et al. / Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 1239-1246 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{0d8aabd35d0f4601b441a21c3d23fb20,

title = "Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector",

abstract = "According to the World Health Organization, it is predicted that in 2020, depression will become the second largest illness threatening the health of mankind. In order to alleviate the worldwide damage caused by depression, a portable and accurate diagnosing technique is the most essential. This research uses three-electrode pervasive EEG collector to collect EEG data from Fp1, Fp2, and Fpz as locations of scalp electrodes, since these locations are closely related to emotions, and uncovered by hair. Special designed experiment has been conducted and totally 178 subjects' EEG data have been collected. Then the research uses KNN (k-Nearest Neighbor), SVM (Support Vector Machine), ANN (Artificial Neuro Network) and DBN (Deep Belief Network) to analyze the data. The results show DBN performed better than traditional methods using shallow algorithms. Moreover, the results suggested the absolute power of beta wave is a valid characteristic, which could be used for detection of depression. The accuracy reached 78.24% using the combination of DBN and the absolute power of beta wave. This research proves the feasibility of smaller-size pervasive system for depression diagnosis.",

keywords = "Deep belief network, Depression diagnosis, EEG, Pervasive system",

author = "Hanshu Cai and Xiaocong Sha and Xue Han and Shixin Wei and Bin Hu",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822696",

language = "English",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1239--1246",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, {Xiaohua Tony} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

address = "United States",

}

Cai, H, Sha, X, Han, X, Wei, S & Hu, B 2017, Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822696, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 1239-1246, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822696

Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector. / Cai, Hanshu; Sha, Xiaocong; Han, Xue et al.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1239-1246 7822696 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector

AU - Cai, Hanshu

AU - Sha, Xiaocong

AU - Han, Xue

AU - Wei, Shixin

AU - Hu, Bin

PY - 2017/1/17

Y1 - 2017/1/17

N2 - According to the World Health Organization, it is predicted that in 2020, depression will become the second largest illness threatening the health of mankind. In order to alleviate the worldwide damage caused by depression, a portable and accurate diagnosing technique is the most essential. This research uses three-electrode pervasive EEG collector to collect EEG data from Fp1, Fp2, and Fpz as locations of scalp electrodes, since these locations are closely related to emotions, and uncovered by hair. Special designed experiment has been conducted and totally 178 subjects' EEG data have been collected. Then the research uses KNN (k-Nearest Neighbor), SVM (Support Vector Machine), ANN (Artificial Neuro Network) and DBN (Deep Belief Network) to analyze the data. The results show DBN performed better than traditional methods using shallow algorithms. Moreover, the results suggested the absolute power of beta wave is a valid characteristic, which could be used for detection of depression. The accuracy reached 78.24% using the combination of DBN and the absolute power of beta wave. This research proves the feasibility of smaller-size pervasive system for depression diagnosis.

AB - According to the World Health Organization, it is predicted that in 2020, depression will become the second largest illness threatening the health of mankind. In order to alleviate the worldwide damage caused by depression, a portable and accurate diagnosing technique is the most essential. This research uses three-electrode pervasive EEG collector to collect EEG data from Fp1, Fp2, and Fpz as locations of scalp electrodes, since these locations are closely related to emotions, and uncovered by hair. Special designed experiment has been conducted and totally 178 subjects' EEG data have been collected. Then the research uses KNN (k-Nearest Neighbor), SVM (Support Vector Machine), ANN (Artificial Neuro Network) and DBN (Deep Belief Network) to analyze the data. The results show DBN performed better than traditional methods using shallow algorithms. Moreover, the results suggested the absolute power of beta wave is a valid characteristic, which could be used for detection of depression. The accuracy reached 78.24% using the combination of DBN and the absolute power of beta wave. This research proves the feasibility of smaller-size pervasive system for depression diagnosis.

KW - Deep belief network

KW - Depression diagnosis

KW - EEG

KW - Pervasive system

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U2 - 10.1109/BIBM.2016.7822696

DO - 10.1109/BIBM.2016.7822696

M3 - Conference contribution

AN - SCOPUS:85013350277

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 1239

EP - 1246

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 -

Cai H, Sha X, Han X, Wei S, Hu B. Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 1239-1246. 7822696. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822696

Pervasive EEG diagnosis of depression using Deep Belief Network with three-electrodes EEG collector

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Keywords

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