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Schizophrenia (SZ) is a severe neuropsychiatric illness that interferes with cognitive and emotional functioning, making early diagnosis essential. Traditional clinical assessments are often subjective, while EEG signals offer valuable insights but are challenged by high dimensionality, noise, and non-linear patterns. To address these issues, a Novel Heterogeneous Dual-Channel Edge-Enhanced Temporal Graph Convolutional Networks with Quantum-Based Avian Navigation Optimizer (NHDETGCNets+QBANO) is proposed for accurate schizophrenia prediction. The proposed framework begins with the preprocessing of raw EEG signals obtained from the Moscow and Warsaw schizophrenia datasets, employing the Adaptive Self-Guided Loop Filter (AS-GLF) to efficiently suppress noise, artifacts, and other signal distortions while enhancing overall signal quality. Subsequently, informative features are extracted using the Quaternion Quadratic-Phase Fourier Transform Domain (QQ-PFTD), capturing both the temporal and spectral characteristics of the EEG signals. For predictive modeling, the NHDETGCNets architecture is utilized, which seamlessly integrates A Novel Dual-Channel Temporal Convolutional Network (AND-CTCN) with a Heterogeneous Edge-Enhanced Graph Attention Network (HE-EGAN), enabling robust relational modeling across EEG channels. To further optimize performance, the Quantum-Based Avian Navigation Optimizer (QBANO) is employed for dynamic hyperparameter tuning, ensuring faster convergence, minimized error rates, reduced computational overhead, and enhanced scalability. Extensive experimental evaluations demonstrate that the NHDETGCNets+QBANO framework consistently achieves an outstanding prediction accuracy of 99.9%, outperforming existing state-of-the-art approaches and establishing itself as a highly reliable and efficient tool for the early and precise prediction of schizophrenia. The outcomes of the suggested framework provide reliable and precise schizophrenia prediction, accurately distinguishing affected individuals, reducing errors, maintaining consistent performance, and demonstrating robustness and efficiency for early detection.
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Copyright © 2026, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: Ramya T, ramya.t2020@vitstudent.ac.in
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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