Developing an EEG-based model to predict awakening after cardiac arrest using partial processing with the BIS Engine

Samuel B Snider; Bradley J Molyneaux; Anarghya Murthy; Quinn Rademaker; Hafeez Rajwani; Benjamin M Scirica; Jong Woo Lee; Christopher Connor

doi:10.1097/ALN.0000000000005369

Developing an EEG-based model to predict awakening after cardiac arrest using partial processing with the BIS Engine

Anesthesiology. 2025 Jan 9. doi: 10.1097/ALN.0000000000005369. Online ahead of print.

Authors

Samuel B Snider¹, Bradley J Molyneaux¹, Anarghya Murthy², Quinn Rademaker¹, Hafeez Rajwani³, Benjamin M Scirica⁴, Jong Woo Lee⁵, Christopher Connor²

Affiliations

¹ Division of Neurocritical Care, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston MA, USA.
² Department of Anesthesiology, Brigham and Women's Hospital and Harvard Medical School, Boston MA, USA.
³ Department of Anesthesia, Hamilton General Hospital, McMaster University, Ontario, Canada.
⁴ Division of Cardiovascular Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
⁵ Division of Epilepsy, Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston MA, USA.

PMID: 39786948
DOI: 10.1097/ALN.0000000000005369

Abstract

Introduction: Accurate prognostication in comatose survivors of cardiac arrest is a challenging and high-stakes endeavor. We sought to determine whether internal EEG subparameters extracted by the Bispectral Index (BIS) monitor, a device commonly used to estimate depth-of-anesthesia intraoperatively, could be repurposed to predict recovery of consciousness after cardiac arrest.

Methods: In this retrospective cohort study, we trained a 3-layer neural network to predict recovery of consciousness to the point of command following versus not based on 48 hours of continuous EEG recordings in 315 comatose patients admitted to a single US academic medical center after cardiac arrest (Derivation cohort: N=181; Validation cohort: N=134). Continuous EEGs were partially processed into subparameters using virtualized emulation of the BIS Engine (i.e., the internal software of the BIS monitor) applied to signals from the frontotemporal leads of the standard 10-20 EEG montage. Our model was trained on hourly-averaged measurements of these internal subparameters. We compared this model's performance to the modified Westhall qualitative EEG scoring framework.

Results: Maximum prognostic accuracy in the Derivation Cohort was achieved using a network trained on only four BIS subparameters (inverse burst suppression ratio, mean spectral power density, gamma power, and theta/delta power). In a held-out sample of 134 patients, our model outperformed current state-of-the-art qualitative EEG assessment techniques at predicting recovery of consciousness (area under the receiver operating characteristic curve: 0.86, accuracy: 0.87, sensitivity: 0.83, specificity: 0.88, positive predictive value: 0.71, negative predictive value: 0.94). Gamma band power has not been previously reported as a correlate of recovery potential after cardiac arrest.

Conclusions: In patients comatose after cardiac arrest, four EEG features calculated internally by the BIS Engine were repurposed by a compact neural network to achieve a prognostic accuracy superior to the current clinical qualitative gold-standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.