QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals
Article
Tasci, Gulay, Gun, Mehmet Veysel, Keles, Tugce, Tasci, Burak Tasci, Barua, Prabal Datta, Tasci, Irem, Dogan, Sengul, Baygin, Mehmet, Palmer, Elizabeth Emma, Tuncer, Turker, Ooi, Chui Ping and Acharya, U. Rajendra. 2023. "QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals." Chaos, Solitons and Fractals. 172. https://doi.org/10.1016/j.chaos.2023.113472
| Article Title | QLBP: Dynamic patterns-based feature extraction functions for automatic detection of mental health and cognitive conditions using EEG signals |
|---|---|
| ERA Journal ID | 88 |
| Article Category | Article |
| Authors | Tasci, Gulay, Gun, Mehmet Veysel, Keles, Tugce, Tasci, Burak Tasci, Barua, Prabal Datta, Tasci, Irem, Dogan, Sengul, Baygin, Mehmet, Palmer, Elizabeth Emma, Tuncer, Turker, Ooi, Chui Ping and Acharya, U. Rajendra |
| Journal Title | Chaos, Solitons and Fractals |
| Journal Citation | 172 |
| Article Number | 113472 |
| Number of Pages | 16 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | United Kingdom |
| ISSN | 0960-0779 |
| 1873-2887 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.chaos.2023.113472 |
| Web Address (URL) | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85159085269&doi=10.1016%2fj.chaos.2023.113472&partnerID=40&md5=519d114452ab484523f533e4b39c9ce0 |
| Abstract | Background: Severe psychiatric disorders, including depressive disorders, schizophrenia spectrum disorders, and intellectual disability, have devastating impacts on vital life domains such as mental, psychosocial, and cognitive functioning and are correlated with an increased risk of mortality. Accurate symptom monitoring and early diagnosis are essential to optimize treatment and enhance patient outcomes. Electroencephalography (EEG) is a potential diagnostic and monitoring tool for mental health and cognitive disorders, as EEG signals are ideal inputs for machine learning models. In this paper, we propose a novel machine learning model for mental disorder detection based on EEG signals. Aim: electroencephalography (EEG) signals for the detection of three major mental health conditions, namely intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD); and (ii) to introduce two novel conditional local binary pattern-based feature extractors for precise classification of these three classes. Material and method: We collected a novel electroencephalography (EEG) signal dataset from 69 individuals, including a control group and participants diagnosed with bipolar disorder, schizophrenia, and intellectual disability. To extract informative features from the dataset, we developed two novel conditional feature extraction functions that improve upon traditional local binary pattern (LBP) functions by utilizing maximum and minimum distance vectors to generate patterns. We refer to these functions as quantum LBP (QLBP). Additionally, we employed wavelet packet decomposition to construct a multileveled feature extraction model. We evaluated several feature selection techniques, including neighborhood component analysis (NCA), Chi2, maximum relevance minimum redundancy (MRMR), and ReliefF, to select the most informative features. Finally, we employed iterative hard majority voting (IHMV) to obtain the final predicted results. Results: Using our multichannel electroencephalography (EEG) signal dataset, we calculated channel-by-channel results and voted results for the classification of intellectual disability (ID), schizophrenia (SZ), and bipolar disorder (BD) classes. Our proposed model, employing the k-nearest neighbors (kNN) classifier with the leave-one subject out cross-validation (LOSO CV) strategy, achieved high accuracy rates of 97.47 %, 94.36 %, and 93.49 % for the ID, SZ, and BD classes, respectively. Conclusions: Employing the leave-one subject out cross-validation (LOSO CV) technique, our proposed model achieved classification accuracy rates of over 90 % for all cases, thereby providing strong evidence for the effectiveness of the proposed quantum local binary pattern (QLBP) feature extraction method. © 2023 Elsevier Ltd |
| Keywords | Mental disorder detection |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Elazig Fethi Sekin City Hospital, Turkey |
| Firat University, Turkey | |
| School of Business | |
| Cogninet Australia, Australia | |
| University of Technology Sydney | |
| Australian International Institute of Higher Education, Australia | |
| University of New England | |
| Taylor's University, Malaysia | |
| SRM Institute of Science and Technology, India | |
| Kumamoto University, Japan | |
| University of Sydney | |
| Erzurum Technical University, Turkey | |
| University of New South Wales | |
| Sydney Children's Hospital, Australia | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| School of Mathematics, Physics and Computing |
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