ADHD Children Identification Based on EEG Using Effective Connectivity Techniques
Paper
Paper/Presentation Title | ADHD Children Identification Based on EEG Using Effective Connectivity Techniques |
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Presentation Type | Paper |
Authors | Shen, M., Wen, Peng (Author), Song, Bo (Author) and Li, Yan (Author) |
Editors | Siuly, Siuly, Wang, Hua, Chen, Lu, Guo, Yanhui and Xing, Chunxiao |
Journal or Proceedings Title | Proceedings of the 10th International Conference on Health Information Science (HIS 2021) |
Journal Citation | 13079, pp. 71-81 |
Number of Pages | 11 |
Year | 2021 |
Place of Publication | Switzerland |
ISBN | 9783030908843 |
9783030908850 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/978-3-030-90885-0_7 |
Web Address (URL) of Paper | https://link.springer.com/chapter/10.1007%2F978-3-030-90885-0_7 |
Web Address (URL) of Conference Proceedings | https://link.springer.com/book/10.1007/978-3-030-90885-0 |
Conference/Event | 10th International Conference on Health Information Science (HIS 2021) |
Event Details | 10th International Conference on Health Information Science (HIS 2021) Parent International Conference on Health Information Science (HIS) Delivery In person Event Date 25 to end of 28 Oct 2021 Event Location Melbourne, Australia |
Abstract | This paper presents a novel method to identify the Attention deficit hyperactivity disorder (ADHD) children using electroencephalography (EEG) signals and effective connectivity techniques. In this study, the original EEG data is pre-filtered and divided into Delta, Theta, Alpha and Beta bands. And then, the effective connectivity graphs are constructed by applying independent component analysis, multivariate regression model and phase slope index. The measures of clustering coefficient, nodal efficiency and degree centrality in graph theory are used to extract features from these graphs. Statistical analysis based on the standard error of the mean is employed to evaluate the performance in each frequency band. The results show a decreased average clustering coefficient in Delta band for ADHD subjects. Also, in Delta band, the ADHD subjects have increased nodal efficiency and degree centrality in left forehead and decreased in forehead middle. |
Keywords | ADHD; EEG; effective connectivity; multivariate regression model; phase slope index; graph theory |
ANZSRC Field of Research 2020 | 460299. Artificial intelligence not elsewhere classified |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Series | Lecture Notes in Computer Science |
Byline Affiliations | School of Mechanical and Electrical Engineering |
School of Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q6xy2/adhd-children-identification-based-on-eeg-using-effective-connectivity-techniques
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