SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals
Article
Khare, Smith K., Bajaj, Varun and Acharya, U. Rajendra. 2021. "SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals." IEEE Transactions on Instrumentation and Measurement. 70. https://doi.org/10.1109/TIM.2021.3070608
| Article Title | SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals |
|---|---|
| ERA Journal ID | 979 |
| Article Category | Article |
| Authors | Khare, Smith K., Bajaj, Varun and Acharya, U. Rajendra |
| Journal Title | IEEE Transactions on Instrumentation and Measurement |
| Journal Citation | 70 |
| Article Number | 2507409 |
| Number of Pages | 9 |
| Year | 2021 |
| Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
| Place of Publication | United States |
| ISSN | 0018-9456 |
| 1557-9662 | |
| Digital Object Identifier (DOI) | https://doi.org/10.1109/TIM.2021.3070608 |
| Web Address (URL) | https://ieeexplore.ieee.org/document/9393968 |
| Abstract | Schizophrenia (SZ) is a psychiatric disorder characterized by cognitive dysfunctions, hallucinations, and delusions, which may lead to lifetime disability. Detection and diagnosis of SZ by visual inspection is subjective, difficult, and time-consuming. Electroencephalogram (EEG) signals are widely used to detect the SZ as they reflect the conditions of the brain. Conventional machine learning methods involve many lengthy manual steps, such as decomposition, feature extraction, feature selection, and classification. In this article, automated identification of SZ is proposed using a combination of time–frequency analysis and convolutional neural network (CNN) to overcome the limitations of feature extraction-based methods. Three press button tasks are analyzed to segregate normal subjects and SZ patients. The EEG signals are subjected to continuous wavelet transform, short-time Fourier transform, and smoothed pseudo-Wigner–Ville distribution (SPWVD) techniques to obtain scalogram, spectrogram, and SPWVD-based time–frequency representation (TFR) plots, respectively. These 2-D plots are fed to pretrained AlexNet, VGG16, ResNet50, and CNN. We have obtained an accuracy of 93.36% using the SPWVD-based TFR and CNN model. In comparison to the benchmark AlexNet, ResNet50, and VGG16 networks, the developed CNN model with four convolutional layers not only requires fewer learnable parameters but also is computationally efficient and fast. This clearly indicates that our proposed method combining the SPWVD-CNN model has performed better than the state-of-the-art transfer learning techniques. Our developed model is ready to be tested with more EEG data and can aid psychiatrists in their diagnosis. |
| Keywords | Convolutional neural networks (CNNs) |
| ANZSRC Field of Research 2020 | 400306. Computational physiology |
| Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
| Byline Affiliations | Indian Institute of Information Technology Design and Manufacturing, India |
| Ngee Ann Polytechnic, Singapore | |
| Singapore University of Social Sciences (SUSS), Singapore | |
| Asia University, Taiwan |
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