Identification of normal and depression EEG signals in variational mode decomposition domain

Health Information Science and Systems - Tập 10 - Trang 1-14 - 2022
Hesam Akbari1, Muhammad Tariq Sadiq2, Siuly Siuly3, Yan Li4, Paul Wen5
1Department of Biomedical Engineering, Islamic Azad University, Tehran, Iran
2School of Architecture, Technology, and Engineering, University of Brighton, Brighton, UK
3Institute for Sustainable Industries & Liveable Cities, Victoria University, Melbourne, Australia
4School of Mathematics Physics and Computing, University of Southern Queensland, Toowoomba Campus, Australia
5School of Engineering, Victoria University, Melbourne, University of Southern Queensland, Toowoomba Campus, Australia

Tóm tắt

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.

Tài liệu tham khảo

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