| ISSN |
2220-3206 (online) |
| Open Access |
This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/ |
| Copyright |
© The Author(s) 2025. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Computer Science, Artificial Intelligence |
| Manuscript Type |
Retrospective Study |
| Article Title |
Explainable electroencephalography-based attention-deficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform
|
| Manuscript Source |
Invited Manuscript |
| All Author List |
Yavuz Atas, Serkan Kırık, Kübra Yıldırım, Burak Tasci, Prabal Datta Barua, Ferhat Balgetir, Sengul Dogan, Turker Tuncer, Ru-San Tan, Elizabeth Palmer, Aruna Devi and U Rajendra Acharya |
| ORCID |
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| Funding Agency and Grant Number |
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| Corresponding Author |
Sengul Dogan, Full Professor, Department of Digital Forensics Engineering, College of Technology, Firat University, Elazığ 23119, Türkiye. sdogan@firat.edu.tr |
| Key Words |
Attention-deficit/hyperactivity disorder detection; Combination ternary pattern; Electroencephalography signal classification; Explainable feature engineering; Twin wavelet transform |
| Core Tip |
This study introduces a novel combination ternary pattern-based framework integrated with a newly developed twin wavelet transform for automated attention-deficit/hyperactivity disorder detection using electroencephalography signals. Leveraging a newly acquired multi-channel electroencephalography dataset of over 7000 recordings, the proposed approach performs channel-wise feature extraction, statistical fusion, and optimal feature selection via neighborhood component analysis. The model achieves remarkable classification performance, with up to 99.97% accuracy through majority voting, demonstrating its potential as a reliable, explainable, and non-invasive diagnostic support tool for attention-deficit/hyperactivity disorder detection assessment. |
| Publish Date |
2026-02-28 08:32 |
| Citation |
Atas Y, Kırık S, Yıldırım K, Tasci B, Barua PD, Balgetir F, Dogan S, Tuncer T, Tan RS, Palmer E, Devi A, Acharya UR. Explainable electroencephalography-based attention-deficit/hyperactivity disorder detection model with a combination of ternary pattern and twin wavelet transform. World J Psychiatry 2026; 16(3): 112962 |
| URL |
https://www.wjgnet.com/2220-3206/full/v16/i3/112962.htm |
| DOI |
https://dx.doi.org/10.5498/wjp.v16.i3.112962 |
