Evaluation of risk of bias in neuroimaging-based artificial intelligence models for psychiatric diagnosis: a systematic review

Z Chen, X Liu, Q Yang, YJ Wang, K Miao… - JAMA network …, 2023 - jamanetwork.com
Importance Neuroimaging-based artificial intelligence (AI) diagnostic models have
proliferated in psychiatry. However, their clinical applicability and reporting quality (ie …

Automatic diagnosis of schizophrenia in EEG signals using CNN-LSTM models

A Shoeibi, D Sadeghi, P Moridian… - Frontiers in …, 2021 - frontiersin.org
Schizophrenia (SZ) is a mental disorder whereby due to the secretion of specific chemicals
in the brain, the function of some brain regions is out of balance, leading to the lack of …

Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002–2023

M Jafari, D Sadeghi, A Shoeibi, H Alinejad-Rokny… - Applied …, 2024 - Springer
Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional,
and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of …

Schizophrenia detection technique using multivariate iterative filtering and multichannel EEG signals

K Das, RB Pachori - Biomedical Signal Processing and Control, 2021 - Elsevier
A new approach for extension of univariate iterative filtering (IF) for decomposing a signal
into intrinsic mode functions (IMFs) or oscillatory modes is proposed for multivariate multi …

Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features

K Singh, J Malhotra - Complex & Intelligent Systems, 2022 - Springer
Epilepsy is a chronic nervous disorder, which disturbs the normal daily routine of an
epileptic patient due to sudden seizure onset. In this era of smart healthcare, automated …

Detection of schizophrenia using hybrid of deep learning and brain effective connectivity image from electroencephalogram signal

S Bagherzadeh, MS Shahabi, A Shalbaf - Computers in Biology and …, 2022 - Elsevier
Detection of mental disorders such as schizophrenia (SZ) through investigating brain
activities recorded via Electroencephalogram (EEG) signals is a promising field in …

SchizoNET: a robust and accurate Margenau–Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals

SK Khare, V Bajaj, UR Acharya - Physiological Measurement, 2023 - iopscience.iop.org
Objective. Schizophrenia (SZ) is a severe chronic illness characterized by delusions,
cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking …

The gut microbiome is associated with brain structure and function in schizophrenia

S Li, J Song, P Ke, L Kong, B Lei, J Zhou, Y Huang… - Scientific reports, 2021 - nature.com
The effect of the gut microbiome on the central nervous system and its possible role in
mental disorders have received increasing attention. However, knowledge about the …

D2PAM: Epileptic seizures prediction using adversarial deep dual patch attention mechanism

AA Khan, RK Madendran… - CAAI Transactions …, 2023 - Wiley Online Library
Epilepsy is considered as a serious brain disorder in which patients frequently experience
seizures. The seizures are defined as the unexpected electrical changes in brain neural …

Smart neurocare approach for detection of epileptic seizures using deep learning based temporal analysis of EEG patterns

K Singh, J Malhotra - Multimedia Tools and Applications, 2022 - Springer
Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public
health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge …