EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals
Article
Article Title | EPSPatNet86: eight-pointed star pattern learning network for detection ADHD disorder using EEG signals |
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ERA Journal ID | 14630 |
Article Category | Article |
Authors | Tanko, Dahiru, Barua, Prabal Datta, Dogan, Sengul, Tuncer, Turker, Palmer, Elizabeth, Ciaccio, Edward J. and Acharya, U. Rajendra |
Journal Title | Physiological Measurement |
Journal Citation | 43 (3), pp. 1-13 |
Article Number | 035002 |
Number of Pages | 13 |
Year | 2022 |
Publisher | IOP Publishing |
Place of Publication | United Kingdom |
ISSN | 0967-3334 |
1361-6579 | |
Digital Object Identifier (DOI) | https://doi.org/10.1088/1361-6579/ac59dc |
Web Address (URL) | https://iopscience.iop.org/article/10.1088/1361-6579/ac59dc |
Abstract | Objective. The main objective of this work is to present a hand-modelled one-dimensional signal classification system to detect Attention-Deficit Hyperactivity Disorder (ADHD) disorder using electroencephalography (EEG) signals. Approach. A novel handcrafted feature extraction method is presented in this research. Our proposed method uses a directed graph and an eight-pointed star pattern (EPSPat). Also, tunable q wavelet transforms (TQWT), wavelet packet decomposition (WPD), statistical extractor, iterative Chi2 (IChi2) selector, and the k-nearest neighbors (kNN) classifier have been utilized to develop the EPSPat based learning model. This network uses two wavelet decomposition methods (TQWT and WPD), and 85 wavelet coefficient bands are extracted. The proposed EPSPat and statistical feature creator generate features from the 85 wavelet coefficient bands and the original EEG signal. The learning network is termed EPSPatNet86. The main purpose of the presented EPSPatNet86 is to detect abnormalities of the EEG signals. Therefore, 85 wavelet sub-bands have been generated to extract features. The created 86 feature vectors have been evaluated using the Chi2 selector and the kNN classifier in the loss value calculation phase. The final features vector is created by employing a minimum loss-valued eight feature vectors. The IChi2 selector selects the best feature vector, which is fed to the kNN classifier. An EEG signal dataset has been used to demonstrate the presented model's EEG signal classification ability. We have used an ADHD EEG dataset since ADHD is a commonly seen brain-related ailment. Main results. Our developed EPSPatNet86 model can detect the ADHD EEG signals with 97.19% and 87.60% accuracy using 10-fold cross and subject-wise validations, respectively. Significance. The calculated results demonstrate that the presented EPSPatNet86 attained satisfactory EEG classification ability. Results show that we can apply our developed EPSPatNet86 model to other EEG signal datasets to detect abnormalities. |
Keywords | ADHD; EEG signal classification; EPSPatNet86; feature engineering; hand-modeled learning network |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Ngee Ann Polytechnic, Singapore |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan | |
Firat University, Turkey | |
School of Business | |
University of Technology Sydney | |
Cogninet Australia, Australia | |
Department of Health, New South Wales | |
University of New South Wales | |
Columbia University Irving Medical Center, United States |
https://research.usq.edu.au/item/yyw64/epspatnet86-eight-pointed-star-pattern-learning-network-for-detection-adhd-disorder-using-eeg-signals
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