ArticleClinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology2022
Automated EEG-based prediction of delayed cerebral ischemia after subarachnoid hemorrhage.
Article in Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.
- Machine Learning for the Early Prediction of Delayed Cerebral Ischemia in Patients With Subarachnoid Hemorrhage: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Cerebral Vasospasm Detection and Delayed Cerebral Ischemia Prediction after Aneurysmal Subarachnoid Hemorrhage: A Scoping Review.Neurocritical care · 2026Article
- Between Patterns and Predictions: Interpretable Latent EEG Representations for Clinical Insights.medRxiv : the preprint server for health sciences · 2026Article
- Noninvasive neurophysiological diagnostics of delayed cerebral ischemia in aneurysmal subarachnoid hemorrhage: a scoping review.Acta neurochirurgica · 2026Article
- EEG for bedside monitoring: the intensivist's point of view.Critical care (London, England) · 2026Review
- Phenotype-Aligned Metabolomics Identifies Plasma Arginine as a Candidate Predictor of Delayed Cerebral Ischemia after Aneurysmal Subarachnoid Hemorrhage.Translational stroke research · 2026Article
- Alpha-Delta Ratio Change on Electroencephalography Associated with Delayed Cerebral Ischemia in Subarachnoid Hemorrhage: A Case Report.Annals of Indian Academy of Neurology · 2025Article
- Beyond the bleed: complications after aneurysmal subarachnoid hemorrhage. Pathophysiology, clinical implications, and management strategies: a review.Critical care (London, England) · 2025Review
- Delayed cerebral ischemia after aneurysmal subarachnoid hemorrhage: a narrative review.Arquivos de neuro-psiquiatria · 2025Review
- Using artificial intelligence to optimize anti-seizure treatment and EEG-guided decisions in severe brain injury.Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics · 2025Review
- Utility of Quantitative EEG in Neurological Emergencies and ICU Clinical Practice.Brain sciences · 2024Review
- Utility and rationale for continuous EEG monitoring: a primer for the general intensivist.Critical care (London, England) · 2024Review
- Diffuse microglial responses and persistent EEG changes correlate with poor neurological outcome in a model of subarachnoid hemorrhage.Scientific reports · 2024Article
- The neurophysiological effect of mild hypothermia in gyrencephalic brains submitted to ischemic stroke and spreading depolarizations.Frontiers in neuroscience · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
objectiveDelayed cerebral ischemia (DCI) is a leading complication of aneurysmal subarachnoid hemorrhage (SAH) and electroencephalography (EEG) is increasingly used to evaluate DCI risk. Our goal is to develop an automated DCI prediction algorithm integrating multiple EEG features over time.
methodsWe assess 113 moderate to severe grade SAH patients to develop a machine learning model that predicts DCI risk using multiple EEG features.
resultsMultiple EEG features discriminate between DCI and non-DCI patients when aligned either to SAH time or to DCI onset. DCI and non-DCI patients have significant differences in alpha-delta ratio (0.08 vs 0.05, p < 0.05) and percent alpha variability (0.06 vs 0.04, p < 0.05), Shannon entropy (p < 0.05) and epileptiform discharge burden (205 vs 91 discharges per hour, p < 0.05) based on whole brain and vascular territory averaging. Our model improves predictions by emphasizing the most informative features at a given time with an area under the receiver-operator curve of 0.73, by day 5 after SAH and good calibration between 48-72 hours (calibration error 0.13).
conclusionsOur proposed model obtains good performance in DCI prediction. SIGNIFICANCE: We leverage machine learning to enable rapid, automated, multi-featured EEG assessment and has the potential to increase the utility of EEG for DCI prediction.
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Registered trials
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.