ArticleIEEE transactions on bio-medical engineering2022
Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.
Article in IEEE transactions on bio-medical engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 2 of them syntheses that pooled it.
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Who cites it
18 citing papers in PubMed, 2 syntheses or guidelines pooled it, 38 citations in OpenAlex.
- Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.Resuscitation · 2024Pooled it
- cEEG and rEEG detection rates of prognostic indicators in cardiac arrest patients: a systematic review and diagnostic meta-analysis.Frontiers in neurologyPooled it
- Automated Cerebral Edema Detection using Electroencephalography in Post-cardiac Arrest Patients.Neurocritical care · 2026Article
- [A method for predicting neurological outcomes after cardiac arrest based on time-frequency domain feature fusion].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2026Article
- Predicting neurological recovery following cardiac arrest based on dynamic brain-heart coupling.Journal of neuroengineering and rehabilitation · 2026Article
- EEG-based machine-learning prognostication in comatose patients with indeterminate outcome after cardiac arrest.Critical care (London, England) · 2026Observational
- Iterative multiblock framework for high frequency EEG based neurological disorder detection.Scientific reports · 2026Article
- Artificial intelligence in neurocardiology: decoding brain-heart network interactions for clinical and translational insights.Frontiers in neuroscience · 2026Review
- Pediatric cardiac arrest outcome prediction using data-driven machine learning of early quantitative electroencephalogram (qEEG) features.Resuscitation · 2025Article
- How much information is needed to predict outcomes after cardiac arrest?Resuscitation · 2025Article
- Toward a Universal Map of EEG: A Semantic, Low-Dimensional Manifold for EEG Classification, Clustering, and Prognostication.Annals of neurology · 2025Article
- Eye movement detection using electrooculography and machine learning in cardiac arrest patients.Resuscitation · 2025Article
- Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.Scientific reports · 2025Article
- Can artificial intelligence improve the diagnosis and prognosis of disorders of consciousness? A scoping review.Frontiers in artificial intelligence · 2025Review
- Application of multi-feature-based machine learning models to predict neurological outcomes of cardiac arrest.Resuscitation plus · 2024Review
- AI and machine learning in resuscitation: Ongoing research, new concepts, and key challenges.Resuscitation plus · 2023Review
- Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.Neurology · 2023Article
- Auditory stimulation and deep learning predict awakening from coma after cardiac arrest.Brain : a journal of neurology · 2023Article
Corrections and comments
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Authors and funding
15 authors at 10 institutions in 3 countries.
Funding
Abstract
objectiveMost cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information.
methodsWe present a recurrent deep neural network with the goal of capturing temporal dynamics from longitudinal EEG data to predict long-term neurological outcomes. We utilized a large international dataset of continuous EEG recordings from 1,038 cardiac arrest patients from seven hospitals in Europe and the US. Poor outcome was defined as a Cerebral Performance Category (CPC) score of 3-5, and good outcome as CPC score 0-2 at 3 to 6-months after cardiac arrest. Model performance is evaluated using 5-fold cross validation.
resultsThe proposed approach provides predictions which improve over time, beginning from an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% CI: 0.72-0.81) at 12 hours, and reaching 0.88 (95% CI: 0.85-0.91) by 66 h after cardiac arrest. At 66 h, (sensitivity, specificity) points of interest on the ROC curve for predicting poor outcomes were (32,99)%, (55,95)%, and (62,90)%, (99,23)%, (95,47)%, and (90,62)%; whereas for predicting good outcome, the corresponding operating points were (17,99)%, (47,95)%, (62,90)%, (99,19)%, (95,48)%, (70,90)%. Moreover, the model provides predicted probabilities that closely match the observed frequencies of good and poor outcomes (calibration error 0.04). CONCLUSIONS AND SIGNIFICANCE: These findings suggest that accounting for EEG trend information can substantially improve prediction of neurologic outcomes for patients with coma following cardiac arrest.
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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.