OGTCN-E-MGO: an optimized deep learning framework for EEG-based schizophrenia detection

Schizophrenia (SCZ) is a complex neurological disorder characterized with deformed understanding. Traditionally, neurologists rely on interviews and visual analysis for SCZ detection and treatment. This work aims to present an automatic classification of the SCZ by electroencephalogram (EEG) signals, which can obtain variations in neural activity associated with cognitive changes in SCZ. This work presents an Optimized Gated Temporal Convolutional Network (OGTCN) for SCZ detection. The suggested OGTCN is the integration of the networks like Gated Recurrent Unit (GRU), Improved Temporal Convolutional Network (ITCN) and the Enhanced Mountain Gazelle Optimizer (E-MGO). The dataset utilized in this work comprises 19 channel EEG signals from 28 individuals, and the second dataset includes 64 channel EEG signals from 81 individuals. Here, the accuracy values achieved are 99.89% (Dataset1) and 99.99% (Dataset2). This research highlights the effectiveness of the OGTCN in enhancing EEG data for supporting proper detection of the SCZ. By integrating the DL model with E-MGO, this approach provided a promising solution to enhance diagnosis of the mental disorder via analysis of the EEG signal.