Developing an Electroencephalogram-based Model to Predict Awakening after Cardiac Arrest Using Partial Processing with the BIS Engine
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 accepted standard, with high sensitivity for recovery. These features hold promise for assessing patients after cardiac arrest.
Authors
- Jong Woo Lee (ORCID: https://orcid.org/0000-0001-5283-7476)
- Anarghya Murthy
- Benjamin M. Scirica (ORCID: https://orcid.org/0000-0002-7093-7048)
- Quinn Rademaker
- Bradley J. Molyneaux (ORCID: https://orcid.org/0000-0002-3377-1229)
- Samuel B. Snider (ORCID: https://orcid.org/0000-0002-6965-7890)
- Christopher W. Connor (ORCID: https://orcid.org/0000-0003-1735-641X)
- Hafeez Rajwani
Institutions
- Brigham and Women's Hospital (US)
- Harvard University (US)
- Hamilton General Hospital (CA)
- McMaster University (CA)
Publication Details
- Journal
- Anesthesiology
- Published
- 2025-01-09
- DOI
- https://doi.org/10.1097/aln.0000000000005369
- Citations
- 3
- Primary Topic
- Cardiac Arrest and Resuscitation
- Type
- article
- Field-Weighted Citation Impact
- 6.76