SchizoNET: A robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals
Article
Khare, Smith K, Bajaj, Varun and Acharya, U Rajendra. 2023. "SchizoNET: A robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals." Physiological Measurement. 44 (3). https://doi.org/10.1088/1361-6579/acbc06
| Article Title | SchizoNET: A robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals |
|---|---|
| ERA Journal ID | 14630 |
| Article Category | Article |
| Authors | Khare, Smith K, Bajaj, Varun and Acharya, U Rajendra |
| Journal Title | Physiological Measurement |
| Journal Citation | 44 (3) |
| Article Number | 035005 |
| Number of Pages | 21 |
| Year | 2023 |
| Publisher | IOP Publishing |
| Place of Publication | Netherlands |
| ISSN | 0967-3334 |
| 1361-6579 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1088/1361-6579/acbc06 |
| Web Address (URL) | https://iopscience.iop.org/article/10.1088/1361-6579/acbc06 |
| Abstract | Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging. Approach. The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau–Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model. Results. The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model. Significance. The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios. |
| Keywords | convolutional neural networks; electroencephalogram classification; schizophrenia detection; s, decision support system |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Byline Affiliations | Aarhus University, Denmark |
| Indian Institute of Information Technology Nagpur (IIITN), India | |
| School of Mathematics, Physics and Computing | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan | |
| Kumamoto University, Japan | |
| University of Malaya, Malaysia |
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https://research.usq.edu.au/item/z1vw0/schizonet-a-robust-and-accurate-margenau-hill-time-frequency-distribution-based-deep-neural-network-model-for-schizophrenia-detection-using-eeg-signals
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