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 ISBNChapterpeer-review


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 languageEnglish
Title of host publicationHealth Information Science - 10th International Conference, HIS 2021, Proceedings
EditorsSiuly Siuly, Hua Wang, Lu Chen, Yanhui Guo, Chunxiao Xing
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783030908843
Publication statusPublished - 10 Nov 2021
Event10th International Conference on Health Information Science, HIS 2021 - Melbourne, Australia
Duration: 25 Oct 202128 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13079 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th International Conference on Health Information Science, HIS 2021

Bibliographical note

Funding Information:
Supported by organization x.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.


  • Classification
  • Depression
  • Depression diagnostic index
  • EEG
  • Fluctuation index
  • Variational mode decomposition


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