SPWVD-CNN for Automated Detection of Schizophrenia Patients Using EEG Signals

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.