Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 31; pp. 464 - 473
• Main Authors: Grover, Nitin, Chharia, Aviral, Upadhyay, Rahul, Longo, Luca
• Format: Journal Article
• Language: English
• Published: United States IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Biomarkers; Brain; brain connectivity features; Brain modeling; classification; Deep learning; Diagnosis; EEG; Electroencephalography; Feature extraction; feature fusion; Hallucinations; Human error; Machine learning; Medical diagnosis; Mental disorders; Neural networks; Pattern recognition; Pollution measurement; Recording; Schizophrenia
• ISSN: 1534-4320; 1558-0210; 1558-0210
• DOI: 10.1109/TNSRE.2023.3237375

Schizophrenia (SCZ) is a serious mental condition that causes hallucinations, delusions, and disordered thinking. Traditionally, SCZ diagnosis involves the subject's interview by a skilled psychiatrist. The process needs time and is bound to human errors and bias. Recently, brain connectivity indices have been used in a few pattern recognition methods to discriminate neuro-psychiatric patients from healthy subjects. The study presents <inline-formula> <tex-math notation="LaTeX">\textit {Schizo-Net} </tex-math></inline-formula>, a novel, highly accurate, and reliable SCZ diagnosis model based on a late multimodal fusion of estimated brain connectivity indices from EEG activity. First, the raw EEG activity is pre-processed exhaustively to remove unwanted artifacts. Next, six brain connectivity indices are estimated from the windowed EEG activity, and six different deep learning architectures (with varying neurons and hidden layers) are trained. The present study is the <inline-formula> <tex-math notation="LaTeX">\textit {first} </tex-math></inline-formula> which considers a large number of brain connectivity indices, especially for SCZ. A detailed study was also performed that identifies SCZ-related changes occurring in brain connectivity, and the vital significance of BCI is drawn in this regard to identify the biomarkers of the disease. <inline-formula> <tex-math notation="LaTeX">\textit {Schizo-Net} </tex-math></inline-formula> surpasses current models and achieves 99.84% accuracy. An optimum deep learning architecture selection is also performed for improved classification. The study also establishes that Late fusion technique outperforms single architecture-based prediction in diagnosing SCZ.