Detecting Depression Using Single-Channel EEG and Graph Methods
Article
Article Title | Detecting Depression Using Single-Channel EEG and Graph Methods |
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ERA Journal ID | 213646 |
Article Category | Article |
Authors | Zhu, Guohun (Author), Qiu, Tong (Author), Ding, Yi (Author), Gao, Shang (Author), Zhao, Nan (Author), Liu, Feng (Author), Zhou, Xujuan (Author) and Gururajan, Raj (Author) |
Journal Title | Mathematics |
Journal Citation | 10 (22), pp. 1-10 |
Article Number | 4177 |
Number of Pages | 10 |
Year | 2022 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2227-7390 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/math10224177 |
Web Address (URL) | https://www.mdpi.com/2227-7390/10/22/4177 |
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. |
Keywords | mental health; classification; isolate nodes; graph entropy; mean degree |
ANZSRC Field of Research 2020 | 461199. Machine learning not elsewhere classified |
Institution of Origin | University of Southern Queensland |
Byline Affiliations | University of Queensland |
University of Queensland | |
School of Business | |
SRM Institute of Science and Technology, India |
https://research.usq.edu.au/item/q7wvq/detecting-depression-using-single-channel-eeg-and-graph-methods
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