ArticleJournal of clinical medicine2020
EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach.
Article in Journal of clinical medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 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
- EEG hemispheric asymmetry and behavioral responsiveness following rTMS in unresponsive wakefulness syndrome: an exploratory study.Journal of neuroengineering and rehabilitation · 2026Article
- Ethical gaps in closed-loop neurotechnology: a scoping review.NPJ digital medicine · 2025Article
- Enhancing Electroencephalogram-Based Prediction of Posttraumatic Stress Disorder Treatment Response Using Data Augmentation.Psychiatry investigation · 2025Article
- Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning.Neuroinformatics · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Electrophysiological Markers Predicting Antipsychotic Treatment Response in Patients with Schizophrenia: A Retrospective Study.Neuropsychiatric disease and treatment · 2024Article
- Neurophysiological patterns reflecting vulnerability to delirium subtypes: a resting-state EEG and event-related potential study.Brain communications · 2024Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Landscape and future directions of machine learning applications in closed-loop brain stimulation.NPJ digital medicine · 2023Review
- Discriminative analysis of schizophrenia patients using graph convolutional networks: A combined multimodal MRI and connectomics analysis.Frontiers in neuroscience · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Predictions of tDCS treatment response in PTSD patients using EEG based classification.Frontiers in psychiatry · 2022Article
- Abnormality of Functional Connections in the Resting State Brains of Schizophrenics.Frontiers in human neuroscience · 2022Article
- Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).Sovremennye tekhnologii v meditsine · 2022Review
- Identification of Major Psychiatric Disorders From Resting-State Electroencephalography Using a Machine Learning Approach.Frontiers in psychiatry · 2021Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
A precise diagnosis and a comprehensive assessment of symptom severity are important clinical issues in patients with schizophrenia (SZ). We investigated whether electroencephalography (EEG) features obtained from EEG source network analyses could be effectively applied to classify the SZ subtypes based on symptom severity. Sixty-four electrode EEG signals were recorded from 119 patients with SZ (53 males and 66 females) and 119 normal controls (NC, 51 males and 68 females) during resting-state with closed eyes. Brain network features (global and local clustering coefficient and global path length) were calculated from EEG source activities. According to positive, negative, and cognitive/disorganization symptoms, the SZ patients were divided into two groups (high and low) by positive and negative syndrome scale (PANSS). To select features for classification, we used the sequential forward selection (SFS) method. The classification accuracy was evaluated using 10 by 10-fold cross-validation with the linear discriminant analysis (LDA) classifier. The best classification accuracy was 80.66% for estimating SZ patients from the NC group. The best classification accuracy between low and high groups in positive, negative, and cognitive/disorganization symptoms were 88.10%, 75.25%, and 77.78%, respectively. The selected features well-represented the pathological brain regions of SZ. Our study suggested that resting-state EEG network features could successfully classify between SZ patients and the NC, and between low and high SZ groups in positive, negative, and cognitive/disorganization symptoms.
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