ArticleBiomedicines2022
Accuracy of EEG Biomarkers in the Detection of Clinical Outcome in Disorders of Consciousness after Severe Acquired Brain Injury: Preliminary Results of a Pilot Study Using a Machine Learning Approach.
Article in Biomedicines, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 3 of them syntheses that pooled it.
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Who cites it
44 citing papers in PubMed, 3 syntheses or guidelines pooled it, 84 citations in OpenAlex.
- Prediction models for functional outcomes in prolonged disorders of consciousness: a systematic review and meta-analysis.Journal of neuroengineering and rehabilitation · 2025Pooled it
- A Meta-analysis of Predicting Disorders of Consciousness After Traumatic Brain Injury by Machine Learning Models.Alpha psychiatry · 2024Pooled it
- Research hotspots and trends in the application of electroencephalography for assessment of disorders of consciousness: a bibliometric analysis.Frontiers in neurology · 2024Pooled it
- Artificial intelligence-based biomarkers for the diagnosis and treatment of neurological conditions: a narrative review.Molecular brain · 2026Review
- Prognostic factors in prolonged disorders of consciousness: a narrative review.Reviews in the neurosciences · 2026Review
- Verticalization with Erigo® in early rehabilitation in patients with severe acquired brain injury: evidence from a pilot study.Frontiers in rehabilitation sciences · 2026Article
- AI and Neurology.Neurological research and practice · 2025Review
- Can artificial intelligence improve the diagnosis and prognosis of disorders of consciousness? A scoping review.Frontiers in artificial intelligence · 2025Review
- Functional connectivity in EEG: a multiclass classification approach for disorders of consciousness.Frontiers in neuroscience · 2025Article
- Utility of Electroencephalograms for Enhancing Clinical Care and Rehabilitation of Children with Acquired Brain Injury.International journal of environmental research and public health · 2024Review
- Assessment of residual awareness in patients with disorders of consciousness using functional near-infrared spectroscopy-based connectivity: a pilot study.Neurophotonics · 2024Article
- A Decade of Dedication: Pioneering Perspectives on Neurological Diseases and Mental Illnesses.Biomedicines · 2024Article
- Innovation at the Intersection: Emerging Translational Research in Neurology and Psychiatry.Cells · 2024Article
- The Impact of C-3 Side Chain Modifications on Kynurenic Acid: A Behavioral Analysis of Its Analogs in the Motor Domain.International journal of molecular sciences · 2024Article
- Targeting Human Glucocorticoid Receptors in Fear Learning: A Multiscale Integrated Approach to Study Functional Connectivity.International journal of molecular sciences · 2024Review
- Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks.Frontiers in medicine · 2024Article
- Variational mode decomposition-based EEG analysis for the classification of disorders of consciousness.Frontiers in neuroscience · 2024Article
- Neurophysiological and Psychometric Outcomes in Minimal Consciousness State after Advanced Audio-Video Emotional Stimulation: A Retrospective Study.Brain sciences · 2023Article
- Emerging Translational Research in Neurological and Psychiatric Diseases: From In Vitro to In Vivo Models.International journal of molecular sciences · 2023Article
- The Role of Serotonin in Fear Learning and Memory: A Systematic Review of Human Studies.Brain sciences · 2023Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accurate outcome detection in neuro-rehabilitative settings is crucial for appropriate long-term rehabilitative decisions in patients with disorders of consciousness (DoC). EEG measures derived from high-density EEG can provide helpful information regarding diagnosis and recovery in DoC patients. However, the accuracy rate of EEG biomarkers to predict the clinical outcome in DoC patients is largely unknown. This study investigated the accuracy of psychophysiological biomarkers based on clinical EEG in predicting clinical outcomes in DoC patients. To this aim, we extracted a set of EEG biomarkers in 33 DoC patients with traumatic and nontraumatic etiologies and estimated their accuracy to discriminate patients' etiologies and predict clinical outcomes 6 months after the injury. Machine learning reached an accuracy of 83.3% (sensitivity = 92.3%, specificity = 60%) with EEG-based functional connectivity predicting clinical outcome in nontraumatic patients. Furthermore, the combination of functional connectivity and dominant frequency in EEG activity best predicted clinical outcomes in traumatic patients with an accuracy of 80% (sensitivity = 85.7%, specificity = 71.4%). These results highlight the importance of functional connectivity in predicting recovery in DoC patients. Moreover, this study shows the high translational value of EEG biomarkers both in terms of feasibility and accuracy for the assessment of DoC.
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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.