Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals
Article
Article Title | Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals |
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ERA Journal ID | 5039 |
Article Category | Article |
Authors | Tor, Hui Tian, Ooi, Chui Ping, Lim-Ashworth, Nikki SJ, Koh, Joel Koh En, Jahmunah, V., Oh, Shu Lih, Acharya, U. Rajendra and Fung, Daniel Shuen Sheng |
Journal Title | Computer Methods and Programs in Biomedicine |
Journal Citation | 200 |
Article Number | 105941 |
Number of Pages | 13 |
Year | 2021 |
Publisher | Elsevier |
Place of Publication | Ireland |
ISSN | 0169-2607 |
1872-7565 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.cmpb.2021.105941 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0169260721000158 |
Abstract | Background and objectives Methods Results Potential application |
Keywords | Attention deficit hyperactive disorder; Sequential forward selection; Nonlinear features; K-fold validation; Conduct disorder; Machine learning; Classifiers |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Singapore University of Social Sciences (SUSS), Singapore |
Institute of Mental Health, Singapore | |
Ngee Ann Polytechnic, Singapore | |
School of Mathematics, Physics and Computing | |
Asia University, Taiwan | |
National University of Singapore |
https://research.usq.edu.au/item/z1w39/automated-detection-of-conduct-disorder-and-attention-deficit-hyperactivity-disorder-using-decomposition-and-nonlinear-techniques-with-eeg-signals
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