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

V Milner Paul^1,2, Adarsh V Parekkattil³, Devika S Kumar⁴

¹Department of Electrical Engineering, National Institute of Technology Manipur (NITM), Imphal, India. 19404004.phd@nitmanipur.ac.in.

Physical and Engineering Sciences in Medicine

|January 5, 2026

Summary

This summary is machine-generated.

This study introduces an Optimized Gated Temporal Convolutional Network (OGTCN) for automatic Schizophrenia (SCZ) detection using electroencephalogram (EEG) signals. The OGTCN achieved high accuracy, offering a promising tool for diagnosing this complex neurological disorder.

Keywords:

Deep learning in healthcare EEG signal analysis Electroencephalogram Optimized gated temporal convolutional network Schizophrenia diagnosis

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Area of Science:

Neuroscience
Artificial Intelligence
Medical Diagnostics

Background:

Schizophrenia (SCZ) is a complex neurological disorder impacting cognition.
Current SCZ diagnosis relies on subjective interviews and visual analysis.
Electroencephalogram (EEG) signals capture neural activity variations linked to cognitive changes.

Purpose of the Study:

To develop an automated system for Schizophrenia (SCZ) classification using EEG signals.
To enhance the accuracy and efficiency of SCZ detection through advanced deep learning.
To investigate the efficacy of an integrated deep learning model for neurological disorder diagnosis.

Main Methods:

An Optimized Gated Temporal Convolutional Network (OGTCN) was developed.
The OGTCN integrates Gated Recurrent Unit (GRU), Improved Temporal Convolutional Network (ITCN), and Enhanced Mountain Gazelle Optimizer (E-MGO).
The model was trained and validated on two distinct EEG datasets (19-channel and 64-channel).

Main Results:

The OGTCN achieved high classification accuracies: 99.89% on Dataset 1 and 99.99% on Dataset 2.
The proposed method demonstrated significant effectiveness in enhancing EEG data analysis for SCZ.
Integration of deep learning with E-MGO provided a robust diagnostic solution.

Conclusions:

The OGTCN model offers a highly accurate and automated approach for Schizophrenia detection.
This deep learning framework shows promise for improving the diagnosis of mental disorders via EEG analysis.
The study highlights the potential of advanced AI techniques in clinical neuroscience.