Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023

Matthew A. Reyna*, Edilberto Amorim, Reza Sameni, James Weigle, Andoni Elola, Ali Bahrami Rad, Salman Seyedi, Hyeokhyen Kwon, Wei Long Zheng, Mohammad M. Ghassemi, Michel J.A.M. Van Putten, Jeannette Hofmeijer, Nicolas Gaspard, Adithya Sivaraju, Susan T. Herman, Jong Woo Lee, M. Brandon Westover, Gari D. Clifford

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

34 Citations (Scopus)


The George B. Moody PhysioNet Challenge 2023 invited teams to develop algorithmic approaches for predicting the recovery of comatose patients after cardiac arrest. A patient's prognosis after the return of spontaneous circulation informs treatment, including the continuation or withdrawal of life support. Brain monitoring with an electroencephalogram (EEG) can improve the objectivity of a prognosis, but EEG interpretation requires clinical expertise. The algorithmic analysis of EEGs can potentially improve the accuracy and accessibility of prognoses, but existing work is limited by small and homogeneous datasets. The PhysioNet Challenge 2023 contributed to addressing these problems. It introduced the International Cardiac Arrest REsearch consortium (I-CARE) dataset, which is a large, multi-center collection of EEGs, other physiological data, and clinical outcomes, with over 57,000 hours of data from 1,020 patients from seven hospitals. It required teams to submit their complete training and inference code to improve the reproducibility and generalizability of their research. A total of 111 teams participated in the Challenge, contributing diverse approaches from academic, clinical, and industry participants worldwide.

Original languageEnglish
Title of host publicationComputing in Cardiology, CinC 2023
ISBN (Electronic)9798350382525
Publication statusPublished - 2023
Event50th Computing in Cardiology, CinC 2023 - Atlanta, United States
Duration: 1 Oct 20234 Oct 2023
Conference number: 50

Publication series

NameComputing in Cardiology
ISSN (Print)2325-8861
ISSN (Electronic)2325-887X


Conference50th Computing in Cardiology, CinC 2023
Abbreviated titleCinC 2023
Country/TerritoryUnited States


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