Unravelling Schizophrenia: An Attention-Based Deep Learning Approach for Detection Using EEG Signals

Elgendy, Mohamed A.; Eletriby, Sherif; Keshk, Arabi; Sakr, Mohamed

doi:10.21608/ijci.2024.316854.1173

	Unravelling Schizophrenia: An Attention-Based Deep Learning Approach for Detection Using EEG Signals
IJCI. International Journal of Computers and Information
Article 5, Volume 12, Issue 1, January 2025, Page 67-84 PDF (1.25 MB)
Document Type: Original Article
DOI: 10.21608/ijci.2024.316854.1173
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Authors
Mohamed A. Elgendy ¹; Sherif Eletriby ²; Arabi Keshk³; Mohamed Sakr²
¹Computer Science Department, Computers and Information Faculty, Menoufia University, Menoufia 32511, Egypt
²Department of Computer Science, Faculty of Computers and Information, Menoufia University, Menoufia 32511, Egypt
³Faculty of Computer and Information Menoufia University
Abstract
Schizophrenia (SZ) affects over 20 million people globally, with many patients diagnosed too late to receive appropriate treatment. Current diagnostic methods are time-consuming, requiring skilled psychiatrists, underscoring the need for more efficient approaches. This work explores using attention-based deep learning models to classify EEG signals, a non-invasive and cost-effective method, into healthy individuals and SZ patients. The proposed attention-GRU model incorporates convolutional neural networks (CNNs) for spatial feature extraction, gated recurrent units (GRUs) for sequence interpretation, and attention layers to highlight the most relevant inputs. Unlike previous works that require time-consuming manual feature extraction, our end-to-end model learns directly from EEG data, reducing preprocessing steps and enhancing the potential for real-time clinical application. Experimental results show a significant improvement in SZ detection, reaching a competitive 98.52% accuracy on an open-source EEG dataset, overcoming the accuracy reported in previous studies. This work highlights the potential of advanced deep learning models in improving the accuracy and efficiency of SZ diagnosis, addressing standardization challenges, and paving the way for more reliable diagnostic tools in psychiatric care. Our results indicate that, with further validation, AI-driven assessments can support early intervention and broader access to treatment for mental disorders.
Keywords
schizophrenia (SZ) detection; electroencephalogram (EEG) signals; attention; long short-term memory (LSTM); gated recurrent unit (GRU)


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