Neurocritical care · 2026
Automated Cerebral Edema Detection using Electroencephalography in Post-cardiac Arrest Patients.
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Stafford Rebecca A, Srivastava Vedika, Altaf Aiman Z, Tang Lin, Sachdeva Abheet Singh, Elman Dan, Mallinger Leigh Ann, Reinert Allyson L, Madipally Sashank Sai Krishna, Salvati Lindsay R, Wahlster Sarah, Chen Denise, Gilmore Emily J, Greer David M, Cheng Huimin, Ong Charlene J
Аннотация
OBJECTIVES: To 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.
METHODS: We 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.
RESULTS: Among 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%).
CONCLUSIONS: Machine 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.
METHODS: We 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.
RESULTS: Among 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%).
CONCLUSIONS: Machine 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.