An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals

Hesam Akbari; Muhammad Tariq Sadiq; Siuly Siuly; Yan Li; Paul Wen

doi:10.1007/978-3-030-90885-0_6

An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals

Hesam Akbari, Muhammad Tariq Sadiq, Siuly Siuly, Yan Li, Paul Wen

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Chapter › peer-review

Abstract

Detection of depression utilizing electroencephalography (EEG) signals is one of the major challenges in neural engineering applications. This study introduces a novel automated computerized depression detection method using EEG signals. In proposed design, firstly, EEG signals are decomposed into 10 empirically chosen intrinsic mode functions (IMFs) with the aid of variational mode decomposition (VMD). Secondly, the fluctuation index (FI) of IMFs is computed as the discrimination features. Finally, these features are fed into cascade forward neural network and feed-forward neural network classifiers which achieved better classification accuracy, sensitivity, and specificity from the right brain hemisphere in a 10-fold cross-validation strategy in comparison with available literature. In this study, we also propose a new depression diagnostic index (DDI) using the FI of IMFs in the VMD domain. This integrated index would assist in a quicker and more objective identification of normal and depression EEG signals. Both the proposed computerized framework and the DDI can help health workers, large enterprises and product developers to build a real-time system.

Original language	English
Title of host publication	Health Information Science - 10th International Conference, HIS 2021, Proceedings
Editors	Siuly Siuly, Hua Wang, Lu Chen, Yanhui Guo, Chunxiao Xing
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	59–70
Number of pages	12
ISBN (Print)	9783030908843
DOIs	https://doi.org/10.1007/978-3-030-90885-0_6
Publication status	Published - 10 Nov 2021
Event	10th International Conference on Health Information Science, HIS 2021 - Melbourne, Australia Duration: 25 Oct 2021 → 28 Oct 2021

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13079 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	10th International Conference on Health Information Science, HIS 2021
Country/Territory	Australia
City	Melbourne
Period	25/10/21 → 28/10/21

Bibliographical note

Funding Information:
Supported by organization x.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Classification
Depression
Depression diagnostic index
EEG
Fluctuation index
Variational mode decomposition

Access to Document

10.1007/978-3-030-90885-0_6

Cite this

Akbari, H., Sadiq, M. T., Siuly, S., Li, Y., & Wen, P. (2021). An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals. In S. Siuly, H. Wang, L. Chen, Y. Guo, & C. Xing (Eds.), Health Information Science - 10th International Conference, HIS 2021, Proceedings (pp. 59–70). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13079 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-90885-0_6

Akbari, Hesam ; Sadiq, Muhammad Tariq ; Siuly, Siuly et al. / An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals. Health Information Science - 10th International Conference, HIS 2021, Proceedings. editor / Siuly Siuly ; Hua Wang ; Lu Chen ; Yanhui Guo ; Chunxiao Xing. Springer Science and Business Media Deutschland GmbH, 2021. pp. 59–70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Detection of depression utilizing electroencephalography (EEG) signals is one of the major challenges in neural engineering applications. This study introduces a novel automated computerized depression detection method using EEG signals. In proposed design, firstly, EEG signals are decomposed into 10 empirically chosen intrinsic mode functions (IMFs) with the aid of variational mode decomposition (VMD). Secondly, the fluctuation index (FI) of IMFs is computed as the discrimination features. Finally, these features are fed into cascade forward neural network and feed-forward neural network classifiers which achieved better classification accuracy, sensitivity, and specificity from the right brain hemisphere in a 10-fold cross-validation strategy in comparison with available literature. In this study, we also propose a new depression diagnostic index (DDI) using the FI of IMFs in the VMD domain. This integrated index would assist in a quicker and more objective identification of normal and depression EEG signals. Both the proposed computerized framework and the DDI can help health workers, large enterprises and product developers to build a real-time system.",

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author = "Hesam Akbari and Sadiq, {Muhammad Tariq} and Siuly Siuly and Yan Li and Paul Wen",

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Akbari, H, Sadiq, MT, Siuly, S, Li, Y & Wen, P 2021, An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals. in S Siuly, H Wang, L Chen, Y Guo & C Xing (eds), Health Information Science - 10th International Conference, HIS 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13079 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 59–70, 10th International Conference on Health Information Science, HIS 2021, Melbourne, Australia, 25/10/21. https://doi.org/10.1007/978-3-030-90885-0_6

An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals. / Akbari, Hesam; Sadiq, Muhammad Tariq; Siuly, Siuly et al.
Health Information Science - 10th International Conference, HIS 2021, Proceedings. ed. / Siuly Siuly; Hua Wang; Lu Chen; Yanhui Guo; Chunxiao Xing. Springer Science and Business Media Deutschland GmbH, 2021. p. 59–70 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13079 LNCS).

Research output: Chapter in Book/Conference proceeding with ISSN or ISBN › Chapter › peer-review

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T1 - An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals

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AU - Sadiq, Muhammad Tariq

AU - Siuly, Siuly

AU - Li, Yan

AU - Wen, Paul

PY - 2021/11/10

Y1 - 2021/11/10

N2 - Detection of depression utilizing electroencephalography (EEG) signals is one of the major challenges in neural engineering applications. This study introduces a novel automated computerized depression detection method using EEG signals. In proposed design, firstly, EEG signals are decomposed into 10 empirically chosen intrinsic mode functions (IMFs) with the aid of variational mode decomposition (VMD). Secondly, the fluctuation index (FI) of IMFs is computed as the discrimination features. Finally, these features are fed into cascade forward neural network and feed-forward neural network classifiers which achieved better classification accuracy, sensitivity, and specificity from the right brain hemisphere in a 10-fold cross-validation strategy in comparison with available literature. In this study, we also propose a new depression diagnostic index (DDI) using the FI of IMFs in the VMD domain. This integrated index would assist in a quicker and more objective identification of normal and depression EEG signals. Both the proposed computerized framework and the DDI can help health workers, large enterprises and product developers to build a real-time system.

AB - Detection of depression utilizing electroencephalography (EEG) signals is one of the major challenges in neural engineering applications. This study introduces a novel automated computerized depression detection method using EEG signals. In proposed design, firstly, EEG signals are decomposed into 10 empirically chosen intrinsic mode functions (IMFs) with the aid of variational mode decomposition (VMD). Secondly, the fluctuation index (FI) of IMFs is computed as the discrimination features. Finally, these features are fed into cascade forward neural network and feed-forward neural network classifiers which achieved better classification accuracy, sensitivity, and specificity from the right brain hemisphere in a 10-fold cross-validation strategy in comparison with available literature. In this study, we also propose a new depression diagnostic index (DDI) using the FI of IMFs in the VMD domain. This integrated index would assist in a quicker and more objective identification of normal and depression EEG signals. Both the proposed computerized framework and the DDI can help health workers, large enterprises and product developers to build a real-time system.

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KW - Depression diagnostic index

KW - EEG

KW - Fluctuation index

KW - Variational mode decomposition

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Health Information Science - 10th International Conference, HIS 2021, Proceedings

A2 - Siuly, Siuly

A2 - Wang, Hua

A2 - Chen, Lu

A2 - Guo, Yanhui

A2 - Xing, Chunxiao

PB - Springer Science and Business Media Deutschland GmbH

T2 - 10th International Conference on Health Information Science, HIS 2021

Y2 - 25 October 2021 through 28 October 2021

ER -

Akbari H, Sadiq MT, Siuly S, Li Y, Wen P. An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals. In Siuly S, Wang H, Chen L, Guo Y, Xing C, editors, Health Information Science - 10th International Conference, HIS 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 59–70. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-90885-0_6

An Automatic Scheme with Diagnostic Index for Identification of Normal and Depression EEG Signals

Abstract

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Bibliographical note

Keywords

Access to Document

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