ArticlePhysical and engineering sciences in medicine2020
Transfer learning with deep convolutional neural network for automated detection of schizophrenia from EEG signals.
Article in Physical and engineering sciences in medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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Who cites it
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 156 citations in OpenAlex.
- 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
- Automated detection and classification of maxillary sinus variations using slice-based and full-volume CBCT deep learning models.BMC oral health · 2026Article
- Analysis and prediction of schizophrenia patients based on high-order graph attention generative adversarial networks.Scientific reports · 2026Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.PloS one · 2026Article
- Obsessive-compulsive disorder detection using ensemble of scalp EEG-based convolutional neural network.Physical and engineering sciences in medicine · 2025Article
- Exploring brain lobe-specific insights in an explainable framework for EEG-based schizophrenia detection.bioRxiv : the preprint server for biology · 2025Article
- Automated depression detection via cloud based EEG analysis with transfer learning and synchrosqueezed wavelet transform.Scientific reports · 2025Article
- Enhanced classification of tinnitus patients using EEG microstates and deep learning techniques.Scientific reports · 2025Article
- Transfer learning and self-distillation for automated detection of schizophrenia using single-channel EEG and scalogram images.Physical and engineering sciences in medicine · 2025Article
- Schizophrenia recognition based on three-dimensional adaptive graph convolutional neural network.Scientific reports · 2025Article
- Automatic maxillary sinus segmentation and pathology classification on cone-beam computed tomographic images using deep learning.BMC oral health · 2024Article
- EEG-based schizophrenia detection using fusion of effective connectivity maps and convolutional neural networks with transfer learning.Cognitive neurodynamics · 2024Article
- Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals.Cognitive neurodynamics · 2024Article
- A deep learning approach for diagnosis of schizophrenia disorder via data augmentation based on convolutional neural network and long short-term memory.Biomedical engineering letters · 2024Article
- Automated diagnosis of schizophrenia based on spatial-temporal residual graph convolutional network.Biomedical engineering online · 2024Article
- Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.Journal of medical systems · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Data leakage in deep learning studies of translational EEG.Frontiers in neuroscience · 2024Article
- [Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Schizophrenia (SZ) is a severe disorder of the human brain which disturbs behavioral characteristics such as interruption in thinking, memory, perception, speech and other living activities. If the patient suffering from SZ is not diagnosed and treated in the early stages, damage to human behavioral abilities in its later stages could become more severe. Therefore, early discovery of SZ may help to cure or limit the effects. Electroencephalogram (EEG) is prominently used to study brain diseases such as SZ due to having high temporal resolution information, and being a noninvasive and inexpensive method. This paper introduces an automatic methodology based on transfer learning with deep convolutional neural networks (CNNs) for the diagnosis of SZ patients from healthy controls. First, EEG signals are converted into images by applying a time-frequency approach called continuous wavelet transform (CWT) method. Then, the images of EEG signals are applied to the four popular pre-trained CNNs: AlexNet, ResNet-18, VGG-19 and Inception-v3. The output of convolutional and pooling layers of these models are used as deep features and are fed into the support vector machine (SVM) classifier. We have tuned the parameters of SVM to classify SZ patients and healthy subjects. The efficiency of the proposed method is evaluated on EEG signals from 14 healthy subjects and 14 SZ patients. The experiments showed that the combination of frontal, central, parietal, and occipital regions applied to the ResNet-18-SVM achieved best results with accuracy, sensitivity and specificity of 98.60% ± 2.29, 99.65% ± 2.35 and 96.92% ± 2.25, respectively. Therefore, the proposed method as a diagnostic tool can help clinicians in detection of the SZ patients for early diagnosis and treatment.
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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.