Automated accurate detection of depression using twin Pascal's triangles lattice pattern with EEG Signals
Article
Tasci, Gulay, Loh, Hui Wen, Barua, Prabal Datta, Baygin, Mehmet, Tasci, Burak, Dogan, Sengul, Tuncer, Turker, Palmer, Elizabeth Emma, Tan, Ru-San and Acharya, U. Rajendra. 2023. "Automated accurate detection of depression using twin Pascal's triangles lattice pattern with EEG Signals." Knowledge-Based Systems. 260. https://doi.org/10.1016/j.knosys.2022.110190
| Article Title | Automated accurate detection of depression using twin Pascal's triangles lattice pattern with EEG Signals |
|---|---|
| ERA Journal ID | 18062 |
| Article Category | Article |
| Authors | Tasci, Gulay, Loh, Hui Wen, Barua, Prabal Datta, Baygin, Mehmet, Tasci, Burak, Dogan, Sengul, Tuncer, Turker, Palmer, Elizabeth Emma, Tan, Ru-San and Acharya, U. Rajendra |
| Journal Title | Knowledge-Based Systems |
| Journal Citation | 260 |
| Article Number | 110190 |
| Number of Pages | 15 |
| Year | 2023 |
| Publisher | Elsevier |
| Place of Publication | Netherlands |
| ISSN | 0950-7051 |
| 1872-7409 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1016/j.knosys.2022.110190 |
| Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0950705122012862 |
| Abstract | Electroencephalogram (EEG)-based major depressive disorder (MDD) machine learning detection models can objectively differentiate MDD from healthy controls but are limited by high complexities or low accuracies. This work presents a self-organized computationally lightweight handcrafted classification model for accurate MDD detection using a reference subject-based validation strategy. We used the public Multimodal Open Dataset for Mental Disorder Analysis (MODMA) comprising 128-channel EEG signals from 24 MDD and 29 healthy control (HC) subjects. The input EEG was decomposed using multilevel discrete wavelet transform with Daubechies 4 mother wavelet function into eight low- and high-level wavelet bands. We used a novel Twin Pascal’s Triangles Lattice Pattern(TPTLP) comprising an array of 25 values to extract local textural features from the raw EEG signal and subbands. For each overlapping signal block of length 25, two walking paths that traced the maximum and minimum L1-norm distances from v1 to v25 of the TPTLP were dynamically generated to extract features. Forty statistical features were also extracted in parallel per run. We employed neighborhood component analysis for feature selection, a k-nearest neighbor classifier to obtain 128 channel-wise prediction vectors, iterative hard majority voting to generate 126 voted vectors, and a greedy algorithm to determine the best overall model result. Our generated model attained the best channel-wise and overall model accuracies. The generated system attained an accuracy of 76.08% (for Channel 1) and 83.96% (voted from the top 13 channels) using leave-one-subject-out(LOSO) cross-validation (CV) and 100% using 10-fold CV strategies, which outperformed other published models developed using same (MODMA) dataset. |
| Keywords | Dynamic feature extraction; Twin Pascal’s Triangles Lattice Pattern; Major depressive disorder; Electroencephalography; Signal decomposition |
| 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 |
| Singapore University of Social Sciences (SUSS), Singapore | |
| School of Business | |
| University of Technology Sydney | |
| Ardahan University, Turkiye | |
| Firat University, Turkey | |
| Sydney Children's Hospital, Australia | |
| University of New South Wales | |
| National Heart Centre, Singapore | |
| Duke-NUS Medical School, Singapore | |
| Ngee Ann Polytechnic, Singapore | |
| Asia University, Taiwan |
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