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Articles Published Processes
2/28/2026 8:32:53 AM | Browse: 56 | Download: 215
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Received |
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2025-08-11 08:21 |
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Peer-Review Started |
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2025-08-11 08:22 |
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First Decision by Editorial Office Director |
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2025-09-04 06:44 |
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Return for Revision |
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2025-09-04 06:44 |
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Revised |
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2025-09-09 11:20 |
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Publication Fee Transferred |
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Second Decision by Editor |
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2025-11-11 02:46 |
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Second Decision by Editor-in-Chief |
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Final Decision by Editorial Office Director |
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2025-11-11 09:43 |
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Articles in Press |
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2025-11-11 09:43 |
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Edit the Manuscript by Language Editor |
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2025-11-16 04:45 |
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Typeset the Manuscript |
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2026-02-13 00:27 |
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Publish the Manuscript Online |
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2026-02-28 08:32 |
| 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
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| 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
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| URL |
https://www.wjgnet.com/2220-3206/full/v16/i3/112962.htm |
| DOI |
https://dx.doi.org/10.5498/wjp.v16.i3.112962 |
Copyright © 1993-2026 Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA. All rights reserved, including rights relating to text and data mining, AI training, and similar technologies. For open-access content, the applicable copyright and licensing terms govern.
