IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i22p4177-d966784.html
   My bibliography  Save this article

Detecting Depression Using Single-Channel EEG and Graph Methods

Author

Listed:
  • Guohun Zhu

    (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Tong Qiu

    (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Yi Ding

    (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Shang Gao

    (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Nan Zhao

    (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Feng Liu

    (School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane 4072, Australia)

  • Xujuan Zhou

    (School of Business, The University of Southern Queensland, Toowoomba 4350, Australia)

  • Raj Gururajan

    (School of Business, The University of Southern Queensland, Toowoomba 4350, Australia
    School of Computing, SRM Institute of Science and Technology, Chennai 603202, India)

Abstract

Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H ( p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe.

Suggested Citation

  • Guohun Zhu & Tong Qiu & Yi Ding & Shang Gao & Nan Zhao & Feng Liu & Xujuan Zhou & Raj Gururajan, 2022. "Detecting Depression Using Single-Channel EEG and Graph Methods," Mathematics, MDPI, vol. 10(22), pages 1-9, November.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4177-:d:966784
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/22/4177/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/22/4177/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:22:p:4177-:d:966784. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.