ArticleFrontiers in neuroinformatics2021
Automatic Diagnosis of Schizophrenia in EEG Signals Using CNN-LSTM Models.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers, 2 of them syntheses that pooled it.
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Who cites it
46 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning.Frontiers in human neuroscience · 2024Pooled it
- Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.JAMA network open · 2023Pooled it
- Adaptive drift-aware multi-stage deep learning framework for EEG-based schizophrenia diagnosis.BioData mining · 2026Article
- Stability-Driven Selection of EEG Connectivity Features for Psychosis Classification: A Network-Based Machine Learning Approach.CNS neuroscience & therapeutics · 2026Article
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- MDEFusion: A Multi-Domain EEG Feature Fusion Network With Bidirectional Attention LSTM and Half-Split Crossover SAE for Schizophrenia Recognition.Brain and behavior · 2026Article
- Subject-Independent Depression Recognition from EEG Using an Improved Bidirectional LSTM with Dynamic Vector Routing.Bioengineering (Basel, Switzerland) · 2026Article
- Automatic EEG artifact detection using a local-global feature fusion network in time and time-frequency domains.Acta epileptologica · 2026Article
- A Hybrid CNN-SVM Approach for ECG-Based Multi-Class Differential Diagnosis of PTSD, Depression, and Panic Attack.Biosensors · 2026Article
- MS-TSEFNet: Multi-Scale Spatiotemporal Efficient Feature Fusion Network.Sensors (Basel, Switzerland) · 2026Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.PloS one · 2026Article
- An optimized EEG-based hybrid deep learning framework for schizophrenia detection.Biomedical engineering letters · 2026Article
- Transformer architecture for diagnosing schizophrenia disabilities through EEG analysis.Frontiers in physiology · 2026Article
- A Comparative Study of Conventional and Tripolar EEG for High-Performance Reach-to-Grasp BCI Systems.IEEE transactions on bio-medical engineering · 2026Article
- Diagnosis of Schizophrenia Using Multimodal Data and Classification Using the EEGNet Framework.Diagnostics (Basel, Switzerland) · 2025Article
- AI-driven early diagnosis of specific mental disorders: a comprehensive study.Cognitive neurodynamics · 2025Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.bioRxiv : the preprint server for biology · 2025Article
- Schizophrenia detection from electroencephalogram signals using image encoding and wrapper-based deep feature selection approach.Scientific reports · 2025Article
- Enhanced schizophrenia detection using multichannel EEG and CAOA-RST-based feature selection.Scientific reports · 2025Article
- Recent Advancements in Neuroimaging-Based Alzheimer's Disease Prediction Using Deep Learning Approaches in e-Health: A Systematic Review.Health science reports · 2025Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 coordination between thoughts, actions, and emotions. This study provides various intelligent deep learning (DL)-based methods for automated SZ diagnosis
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.