Identification of Normal and Depression EEG Signals in Variational Mode Decomposition Domain
Article
Article Title | Identification of Normal and Depression EEG Signals in Variational Mode Decomposition Domain |
---|---|
ERA Journal ID | 212669 |
Article Category | Article |
Authors | Akbari, Hesam (Author), Sadiq, Muhammad Tariq (Author), Siuly, Siuly (Author), Li, Yan (Author) and Wen, Paul (Author) |
Journal Title | Health Information Science and Systems |
Journal Citation | 10 (1), pp. 1-14 |
Article Number | 24 |
Number of Pages | 14 |
Year | 2022 |
Publisher | Springer |
Place of Publication | Germany |
ISSN | 2047-2501 |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13755-022-00187-7 |
Web Address (URL) | https://link.springer.com/article/10.1007/s13755-022-00187-7 |
Abstract | Early detection of depression is critical in assisting patients in receiving the best therapy possible to avoid negative repercussions. Depression detection using electroencephalogram (EEG) signals is a simple, low-cost, convenient, and accurate approach. This paper proposes a six-stage novel method for detecting depression using EEG signals. First, EEG signals are recorded from 44 subjects, with 22 subjects being normal and 22 subjects being depressed. Second, a simple notch filter with EEG signals differencing approach is employed for effective preprocessing. Third, the variational mode decomposition (VMD) approach is implemented for nonlinear and non-stationary EEG signals analysis, resulting in many modes. Fourth, mutual information-based novel modes selection criterion is proposed to select the most informative modes. In the fifth step, a combination of linear and nonlinear features are extracted from selected modes and at last, classification is performed with neural networks. In this study, a novel single feature is also proposed, which is made using Log energy, norm entropies and fluctuation index, which delivers 100% classification accuracy, sensitivity and specificity. By using these features, a novel depression diagnostic index is also proposed. This integrated index would assist in quicker and more objective identification of normal and depression EEG signals. The proposed computerized framework and the DDI can help health workers, large enterprises, and product developers build a real-time system. |
Keywords | EEG; Depression detection; Variational Mode Decomposition Domain; Feature extraction |
ANZSRC Field of Research 2020 | 460102. Applications in health |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Islamic Azad University, Iran |
University of Brighton, United Kingdom | |
Victoria University | |
School of Mathematics, Physics and Computing | |
School of Engineering | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q7qw9/identification-of-normal-and-depression-eeg-signals-in-variational-mode-decomposition-domain
90
total views2
total downloads2
views this month0
downloads this month