A Review of Artificial Intelligence Methods for Diagnosing and Classifying Schizophrenia Using EEG Signals - Saleh Alian, Abeer; Sabar Miften, Firas (Journal of Al-Qadisiyah for Computer Science and Mathematics, 2025)
Schizophrenia (SZ) is a chronic and severe mental disorder characterized by impairments in cognitive skills, perceptions, emotions, and social interactions. A timely and accurate diagnosis is crucial for improving prognosis and developing effective treatment strategies. Recently, researchers have utilized computational models to enhance the effectiveness and speed of schizophrenia diagnosis using electroencephalogram (EEG), consequently reducing clinical workload. This research investigates the integration of traditional signal processing techniques, feature extraction methods, and artificial intelligence (AI), including machine learning (ML) and deep learning (DL), for the categorization of schizophrenia (SZ) utilizing EEG data. The electroencephalogram, a crucial tool for assessing cerebral activity, has demonstrated importance in mental health research. Upon acquiring brain data, various signal-processing techniques are employed to extract pertinent information from the temporal, frequency, and spatial domains. The gathered properties, encompassing mean, variance, and band power, are the basis for recognizing EEG signals. Traditional machine learning techniques, such as Decision Trees and Support Vector Machines (SVMs), provide interpretability and effectiveness with constrained datasets. In contrast, deep learning techniques, such as convolutional neural networks (CNNs) and extended short-term memory networks (LSTMs), excel in analyzing complex EEG patterns; however, they require extensive data and significant computational resources. The study examines the challenges associated with implementing AI in the diagnosis of schizophrenia, including ethical concerns and issues with data quality. These difficulties require collaborative and ethically sound approaches to ensure reliable advancement in the area. The research highlights the importance of employing many approaches to improve diagnosis accuracy, showcasing the potential of AI-driven solutions in the classification of schizophrenia. This review offers a comprehensive examination of contemporary literature, encompassing themes, approaches, and conclusions. The aim is to identify significant advancements and provide insights that help researchers and clinicians understand and tackle schizophrenia through innovative AI-driven approaches.