Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG
  Signals: A Comprehensive Review from 2002-2023
Mahboobeh Jafari Delaram Sadeghi Afshin Shoeibi Hamid Alinejad-Rokny Amin Beheshti David López García Zhaolin Chen U. Rajendra Acharya Juan M. Gorriz
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive,
emotional, and behavioral changes. Symptoms of SZ include hallucinations,
illusions, delusions, lack of motivation, and difficulties in concentration.
Diagnosing SZ involves employing various tools, including clinical interviews,
physical examinations, psychological evaluations, the Diagnostic and
Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques.
Electroencephalography (EEG) recording is a significant functional neuroimaging
modality that provides valuable insights into brain function during SZ.
However, EEG signal analysis poses challenges for neurologists and scientists
due to the presence of artifacts, long-term recordings, and the utilization of
multiple channels. To address these challenges, researchers have introduced
artificial intelligence (AI) techniques, encompassing conventional machine
learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This
study reviews papers focused on SZ diagnosis utilizing EEG signals and AI
methods. The introduction section provides a comprehensive explanation of SZ
diagnosis methods and intervention techniques. Subsequently, review papers in
this field are discussed, followed by an introduction to the AI methods
employed for SZ diagnosis and a summary of relevant papers presented in tabular
form. Additionally, this study reports on the most significant challenges
encountered in SZ diagnosis, as identified through a review of papers in this
field. Future directions to overcome these challenges are also addressed. The
discussion section examines the specific details of each paper, culminating in
the presentation of conclusions and findings.