Evidence map›Paper›PMID 42806266›Full record

ArticleNeurocritical care2026

Automated Cerebral Edema Detection using Electroencephalography in Post-cardiac Arrest Patients.

Rebecca A Stafford, Vedika Srivastava, Aiman Z Altaf, Lin Tang, Abheet Singh Sachdeva, Dan Elman, Leigh Ann Mallinger, Allyson L Reinert, Sashank Sai Krishna Madipally, Lindsay R Salvati and 6 more

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Article in Neurocritical care, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

16 authors.

Rebecca A StaffordBoston Medical Center, Boston, USA.
Vedika SrivastavaSchool of Public Health, Boston University, Boston, USA.
Aiman Z AltafDepartment of Neurology, Boston University Chobanian and Avedisian School of Medicine, 85 E Concord St., Suite 1116, Boston, MA, 02118, USA.
Lin TangSchool of Public Health, Boston University, Boston, USA.
Abheet Singh SachdevaSchool of Public Health, Boston University, Boston, USA.
Dan ElmanBoston Medical Center, Boston, USA.
Leigh Ann MallingerBoston Medical Center, Boston, USA.
Allyson L ReinertBoston Medical Center, Boston, USA.
Sashank Sai Krishna MadipallySchool of Public Health, Boston University, Boston, USA.
Lindsay R SalvatiSchool of Public Health, Boston University, Boston, USA.
Sarah WahlsterUniversity of Washington, Seattle, USA.
Denise ChenUniversity of Washington, Seattle, USA.
Emily J GilmoreYale University, New Haven, USA.
David M GreerBoston Medical Center, Boston, USA.
Huimin ChengSchool of Public Health, Boston University, Boston, USA.
Charlene J OngBoston Medical Center, Boston, USA. cjong@bu.edu.ORCID http://orcid.org/0000-0003-2377-3025

Funding

American Heart Association 23CDA1041762NINDS NIH HHS K23NS11603
6 · The paper itself

Abstract

objectivesTo develop an electroencephalography (EEG)-based machine learning model to identify diffuse cerebral edema in patients post-cardiac arrest and evaluate its ability to predict edema before radiographic detection.

methodsWe performed a retrospective, single-center cohort study of adult patients resuscitated from cardiac arrest (2016-2024) who underwent neuroimaging and EEG monitoring as part of routine clinical care. Machine learning models using transformer and long short-term memory (LSTM) architectures were trained to detect diffuse cerebral edema from 4- to 8-h EEG segments obtained > 24 h after arrest. The best-performing detection model was then evaluated for its ability to predict diffuse cerebral edema using EEG segments preceding radiographic recognition in patients who ultimately developed edema, compared with matched referents without edema (matched by age, sex, witnessed arrest, and EEG timing). Model performance was assessed using area under the curve (AUC), accuracy, sensitivity, and specificity.

resultsAmong 124 patients in the detection model, the median age was 53 years, and 74 (59.7%) were male patients. Sixty-five patients (52.4%) developed diffuse cerebral edema. The best-performing detection model, a transformer using 4-h EEG segments, achieved strong performance (median AUC 83.5%, accuracy 75.0%, sensitivity 80.0%, specificity 70.0%). In a secondary analysis of 19 patients with diffuse cerebral edema and 19 matched referents, the top-performing prediction model used 8-h EEG segments (median AUC 80.0%, accuracy 70.0%, sensitivity 80.0%, specificity 60.0%).

conclusionsMachine learning models applied to routine EEG data can classify EEG patterns associated with radiographic diffuse cerebral edema in cardiac arrest survivors. In this study, a transformer-based approach outperformed LSTM both for classifying established edema and for identifying similar EEG patterns before radiographic recognition. With external validation, this approach may help flag evolving cerebral edema for clinician review during standard EEG monitoring, potentially informing earlier intervention and family discussions regarding both reversible and irreversible brain injury.

Indexed as

Cerebral edemaElectroencephalographyTransformer

Identifiers

PMID42806266

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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.