Exploring deep residual network based features for automatic schizophrenia detection from EEG
Article
Siuly, Siuly, Guo, Yanhui, Alcin, Omer Faruk, Li, Yan, Wen, Peng and Wang, Hua. 2023. "Exploring deep residual network based features for automatic schizophrenia detection from EEG." Physical and Engineering Sciences in Medicine. 46 (2), pp. 561-574. https://doi.org/10.1007/s13246-023-01225-8
Article Title | Exploring deep residual network based features for automatic schizophrenia detection from EEG |
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ERA Journal ID | 5034 |
Article Category | Article |
Authors | Siuly, Siuly, Guo, Yanhui, Alcin, Omer Faruk, Li, Yan, Wen, Peng and Wang, Hua |
Journal Title | Physical and Engineering Sciences in Medicine |
Journal Citation | 46 (2), pp. 561-574 |
Number of Pages | 14 |
Year | 2023 |
Publisher | Springer |
Place of Publication | Netherlands |
ISSN | 0158-9938 |
1879-5447 | |
Digital Object Identifier (DOI) | https://doi.org/10.1007/s13246-023-01225-8 |
Web Address (URL) | https://link.springer.com/article/10.1007/s13246-023-01225-8 |
Abstract | Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists. |
Keywords | Classification; Electroencephalogram (EEG) signal; Schizophrenia detection; Deep residual network; Feature extraction |
ANZSRC Field of Research 2020 | 460501. Data engineering and data science |
Byline Affiliations | Centre for Health Research |
Victoria University | |
University of Illinois, United States | |
Malatya Turgut Ozal University, Turkiye | |
School of Mathematics, Physics and Computing | |
School of Engineering |
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https://research.usq.edu.au/item/z26x5/exploring-deep-residual-network-based-features-for-automatic-schizophrenia-detection-from-eeg
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