ArticleIndian journal of psychiatry
Artificial intelligence-based classification of schizophrenia: A high density electroencephalographic and support vector machine study.
Article in Indian journal of psychiatry. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed, 47 citations in OpenAlex.
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- An innovative framework based on GMV-ALFF multi-modal MRI and Gaussian data augmentation for psychiatric disorder classification.Frontiers in computational neuroscience · 2026Article
- Translational significance of neurodevelopment underpinned 'neurophysiological-cognitive' biomarkers in schizophrenia.Indian journal of psychiatry · 2025Article
- Multi-modal EEG NEO-FFI with Trained Attention Layer (MENTAL) for mental disorder prediction.Brain informatics · 2024Article
- Quantum Machine-Based Decision Support System for the Detection of Schizophrenia from EEG Records.Journal of medical systems · 2024Article
- Assisting schizophrenia diagnosis using clinical electroencephalography and interpretable graph neural networks: a real-world and cross-site study.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2023Article
- Optimized adaptive neuro-fuzzy inference system based on hybrid grey wolf-bat algorithm for schizophrenia recognition from EEG signals.Cognitive neurodynamics · 2023Article
- Machine Learning and Non-Affective Psychosis: Identification, Differential Diagnosis, and Treatment.Current psychiatry reports · 2022Review
- Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis.Brain sciences · 2022Article
- Schizophrenia: A Narrative Review of Etiopathogenetic, Diagnostic and Treatment Aspects.Journal of clinical medicine · 2022Article
- Artificial intelligence and Psychiatry: An overview.Asian journal of psychiatry · 2022Review
- Diagnosis of Schizophrenia Based on the Data of Various Modalities: Biomarkers and Machine Learning Techniques (Review).Sovremennye tekhnologii v meditsine · 2022Review
- An integrated machine learning framework for a discriminative analysis of schizophrenia using multi-biological data.Scientific reports · 2021Article
- EEG microstate features for schizophrenia classification.PloS one · 2021Article
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7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundInterview-based schizophrenia (SCZ) diagnostic methods are not completely valid. Moreover, SCZ-the disease entity is very heterogeneous. Supervised-Machine-Learning (sML) application of Artificial-Intelligence holds a tremendous promise in solving these issues.
aimsTo sML-based discriminating validity of resting-state electroencephalographic (EEG) quantitative features in classifying SCZ from healthy and, positive (PS) and negative symptom (NS) subgroups, using a high-density recording. SETTINGS AND
designData collected at a tertiary care mental-health institute using a cross-sectional study design and analyzed at a premier Engineering Institute. MATERIALS AND
methodsData of 38-SCZ patients and 20-healthy controls were retrieved. The positive-negative subgroup classification was done using Positive and Negative Syndrome Scale operational-criteria. EEG was recorded using 256-channel high-density equipment. Eight STATISTICAL ANALYSIS: Mann-Whitney test was used for comparison of machine learning-features. Accuracy, sensitivity, specificity, and area under receiver operating characteristics-curve were measured as discriminatory indices of classifications.
resultsAccuracy of classifying SCZ from healthy and PS from NS SCZ, were 78.95% and 89.29%, respectively. While beta and gamma frequency related features most accurately classified SCZ from healthy controls, delta and theta frequency related features most accurately classified positive from negative SCZ. Inferior frontal gyrus features most accurately contributed to both the classificatory instances.
conclusionsSVM-based classification and sub-classification of SCZ using EEG data is optimal and might help in improving the "validity" and reducing the "heterogeneity" in the diagnosis of SCZ. These results might only be generalized to acute and moderately ill male SCZ patients.
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