ArticleEuropean archives of psychiatry and clinical neuroscience2013
Single-subject classification of schizophrenia using event-related potentials obtained during auditory and visual oddball paradigms.
Article in European archives of psychiatry and clinical neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
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Who cites it
15 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
- Configurable Modular EEG Classification Framework with Multiscale Features and Ensemble Learning: A Reproducible Evaluation for Schizophrenia Detection.Bioengineering (Basel, Switzerland) · 2026Article
- Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.BMC medicine · 2023Article
- Machine Learning Techniques for the Diagnosis of Schizophrenia Based on Event-Related Potentials.Frontiers in neuroinformatics · 2022Article
- Classification of Schizophrenia by Combination of Brain Effective and Functional Connectivity.Frontiers in neuroscience · 2021Article
- A Multimodal Neuroprosthetic Interface to Record, Modulate and Classify Electrophysiological Biomarkers Relevant to Neuropsychiatric Disorders.Frontiers in bioengineering and biotechnology · 2021Article
- Applying deep learning to single-trial EEG data provides evidence for complementary theories on action control.Communications biology · 2020Article
- Cognitive Memory Comparison Between Tinnitus and Normal Cases Using Event-Related Potentials.Frontiers in integrative neuroscience · 2018Article
- Single-subject prediction of response inhibition behavior by event-related potentials.Journal of neurophysiology · 2016Article
- Progressive reduction of visual P300 amplitude in patients with first-episode schizophrenia: an ERP study.Schizophrenia bulletin · 2015Article
- Critical evaluation of auditory event-related potential deficits in schizophrenia: evidence from large-scale single-subject pattern classification.Schizophrenia bulletin · 2014Article
- Comparison of auditory and visual oddball fMRI in schizophrenia.Schizophrenia research · 2014Article
- Neurocognitive pattern analysis reveals classificatory hierarchy of attention deficits in schizophrenia.Schizophrenia bulletin · 2014Article
- Risk genes, metabolic syndrome and eye tracking deficits in psychiatric diseases.European archives of psychiatry and clinical neuroscience · 2013Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
In the search for the biomarkers of schizophrenia, event-related potential (ERP) deficits obtained by applying the classic oddball paradigm are among the most consistent findings. However, the single-subject classification rate based on these parameters remains to be determined. Here, we present a data-driven approach by applying machine learning classifiers to relevant oddball ERPs. Twenty-four schizophrenic patients and 24 matched healthy controls finished auditory and visual oddball tasks while high-density electrophysiological recordings were applied. The N1 component in response to standards and target as well as the P3 component following targets were submitted to different machine learning algorithms and the resulting ERP features were submitted to further correlation analyses. We obtained a classification accuracy of 72.4 % using only two ERP components. Latencies of parietal N1 components to visual standard stimuli at electrode positions Pz and P1 were sufficient for classification. Further analysis revealed a high correlation of these features in controls and an intermediate correlation in schizophrenia patients. These data exemplarily show how automated inference may be applied to classify a pathological state in single subjects without prior knowledge of their diagnoses and illustrate the potential of machine learning algorithms for the identification of potential biomarkers. Moreover, this approach assesses the discriminative accuracy of one of the most consistent findings in schizophrenia research by means of single-subject classification.
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