Author ORCID Identifier
https://orcid.org/0000-0002-2718-5426
Document Type
Article
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences
Abstract
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 Schizo-Net , 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 first 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. Schizo-Net 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.
DOI
https://doi.org/10.1109/TNSRE.2023.3237375
Recommended Citation
Grover, Nitin; Chharia, Aviral; Upadhyay, Rahul; and Longo, Luca, "Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices" (2023). Articles. 195.
https://arrow.tudublin.ie/scschcomart/195
Funder
Artificial Intelligence and Cognitive Load Research Laboratory, Technological University Dublin
Creative Commons License
This work is licensed under a Creative Commons Attribution-Share Alike 4.0 International License.
Publication Details
https://ieeexplore.ieee.org/document/10023506/keywords#keywords
DOI: 10.1109/TNSRE.2023.3237375
N. Grover, A. Chharia, R. Upadhyay and L. Longo, "Schizo-Net: A novel Schizophrenia Diagnosis Framework Using Late Fusion Multimodal Deep Learning on Electroencephalogram-Based Brain Connectivity Indices," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 31, pp. 464-473, 2023.