ArticlePhysical and engineering sciences in medicine2021
Automatic classification of schizophrenia patients using resting-state EEG signals.
Article in Physical and engineering sciences in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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Who cites it
16 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
- 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
- Quantification of motor abnormalities with instrumental tasks among adolescents with first-episode schizophrenia and depressive disorders.Scientific reports · 2025Article
- Moving toward precision and personalized treatment strategies in psychiatry.The international journal of neuropsychopharmacology · 2025Review
- A Machine-Learning-Based Analysis of Resting State Electroencephalogram Signals to Identify Latent Schizotypal and Bipolar Development in Healthy University Students.Diagnostics (Basel, Switzerland) · 2025Article
- Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia.Frontiers in human neuroscience · 2025Article
- EEG-based classification of Alzheimer's disease and frontotemporal dementia: a comprehensive analysis of discriminative features.Cognitive neurodynamics · 2024Article
- Multiresolution feature fusion for smart diagnosis of schizophrenia in adolescents using EEG signals.Cognitive neurodynamics · 2024Article
- Detection of Schizophrenia from EEG Signals using Selected Statistical Moments of MFC Coefficients and Ensemble Learning.Neuroinformatics · 2024Article
- Diagnosis of mental disorders using machine learning: Literature review and bibliometric mapping from 2012 to 2023.Heliyon · 2024Review
- A comparative study of wavelet families for schizophrenia detection.Frontiers in human neuroscience · 2024Article
- Schizophrenia diagnosis based on diverse epoch size resting-state EEG using machine learning.PeerJ. Computer science · 2024Article
- Weighted ordinal connection based functional network classification for schizophrenia disease detection using EEG signal.Physical and engineering sciences in medicine · 2023Article
- Hybrid Deep Learning Approach for Stress Detection Using Decomposed EEG Signals.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
Schizophrenia is one of the serious mental disorders, which can suspend the patient from all aspects of life. In this paper we introduced a new method based on the adaptive neuro fuzzy inference system (ANFIS) to classify recorded electroencephalogram (EEG) signals from 14 schizophrenia patients and 14 age-matched control participants. Sixteen EEG channels from 19 main channels that had the most discriminatory information were selected. Possible artifacts of these channels were eliminated with the second-order Butterworth filter. Four features, Shannon entropy, spectral entropy, approximate entropy, and the absolute value of the highest slope of autoregressive coefficients (AVLSAC) were extracted from each selected EEG channel in 5 frequency sub-bands, Delta, Theta, Alpha, Beta, and Gamma. Forty-six features were introduced among the 640 possible ones, and the results included accuracies of near 100%, 98.89%, and 95.59% for classifiers of ANFIS, support vector machine (SVM), and artificial neural network (ANN), respectively. Also, our results show that channels of alpha of O1, theta and delta of Fz and F8, and gamma of Fp1 have the most discriminatory information between the two groups. The performance of our proposed model was also compared with the recently published approaches. This study led to presenting a new decision support system (DSS) that can receive a person's EEG signal and separates the schizophrenia patient and healthy subjects with high accuracy.
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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.