Database Restricted Access

I-CARE: International Cardiac Arrest REsearch consortium Electroencephalography Database

Edilberto Amorim Wei-Long Zheng Mohammad Ghassemi Mahsa Aghaeeaval Pravinkumar Kandhare Vishnu Karukonda Jong Woo Lee Susan Herman adithya sivaraju Nicolas Gaspard Jeannette Hofmeijer Michel JAM van Putten Reza Sameni Matthew Reyna Gari Clifford M Brandon Westover

Published: Oct. 11, 2023. Version: 1.0

When using this resource, please cite: (show more options)
Amorim, E., Zheng, W., Ghassemi, M., Aghaeeaval, M., Kandhare, P., Karukonda, V., Lee, J. W., Herman, S., sivaraju, a., Gaspard, N., Hofmeijer, J., van Putten, M. J., Sameni, R., Reyna, M., Clifford, G., & Westover, M. B. (2023). I-CARE: International Cardiac Arrest REsearch consortium Electroencephalography Database (version 1.0). Brain Data Science Platform.

Additionally, please cite the original publication:

Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval A, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Nicolas Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. Critical Care Medicine (in print); doi:


The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalography (EEG) recordings from 1,020 comatose patients with a diagnosis of cardiac arrest who were admitted to an intensive care unit from seven academic hospitals in the U.S. and Europe. Patients were monitored with 18 bipolar EEG channels over hours to days for the diagnosis of seizures and for neurological prognostication. Long-term neurological function was determined using the Cerebral Performance Category scale.


More than 6 million cardiac arrests happen every year worldwide, with survival rates ranging from 1% to 10% depending on geographic location [1]. Severe brain injury is the main determinant of poor outcome for patients surviving cardiac arrest resuscitation every year [1,2]. Most patients surviving to intensive care unit admission are comatose, and 50-80% have life-sustaining therapies withdrawn due to a perceived poor neurological prognosis [3]. 

Brain monitoring with electroencephalography aims to reduce the subjectivity in neurologic prognostication following cardiac arrest [4-9]. Clinical neurophysiologists have identified patterns of brain activity that help to predict prognosis following cardiac arrest, including the presences of reduced voltage, burst suppression (alternating periods of high and low voltage), seizures, and a variety of seizure-like patterns [8]. The evolution of electroencephalogram (EEG) patterns over time provides additional predictive information [6,7]. However, qualitative interpretation of continuous EEG is laborious, expensive, and requires review from neurologists with advanced training in neurophysiology who are unavailable in most medical centers.

Automated analysis of continuous EEG data has the potential to improve prognostic accuracy and to increase access to brain monitoring where experts are not readily available [6,7]. However, the datasets used in most studies have small numbers of patients (<100) from single hospitals, which are unsuitable for deployment of several types of machine learning methods for EEG data analysis. To overcome this limitation the International Cardiac Arrest REsearch consortium (I-CARE) assembled a large collection of EEG data and neurologic outcomes from comatose patients who underwent EEG monitoring following cardiac arrest in seven hospitals from the United States and Europe.[10-11]


The database originates from seven academic hospitals in the U.S. and Europe led by investigators part of the International Cardiac Arrest REsearch consortium (I-CARE).

  1. Rijnstate Hospital, Arnhem, The Netherlands (Jeannette Hofmeijer).
  2. Medisch Spectrum Twente, Enschede, The Netherlands (Barry J. Ruijter, Marleen C. Tjepkema-Cloostermans, Michel J. A. M. van Putten).
  3. Erasme Hospital, Brussels, Belgium (Nicolas Gaspard).
  4. Massachusetts General Hospital, Boston, Massachusetts, USA (Edilberto Amorim, Wei-Long Zheng, Mohammad Ghassemi, and M. Brandon Westover).
  5. Brigham and Women’s Hospital, Boston, Massachusetts, USA (Jong Woo Lee).
  6. Beth Israel Deaconess Medical Center, Boston, Massachusetts, USA (Susan T. Herman).
  7. Yale New Haven Hospital, New Haven, Connecticut, USA (Adithya Sivaraju).

This database consists of clinical and EEG data from adult patients with out-of-hospital or in-hospital cardiac arrest who had return of heart function (i.e., return of spontaneous circulation [ROSC]) but remained comatose - defined as inability to follow verbal commands and a Glasgow Coma Score inferior or equal to 8. The initial database release contains data from 607 patients - this is the public training set for the 2023 PhysioNet Challenge. This database release does not contain data from the remaining 413 patients that we are retaining as the hidden validation and test sets for the Challenge. Algorithms developed using the I-CARE training set can be submitted to the Brain Data Science Platform (BDSP) team for evaluation using the held-out dataset (

All patients were admitted to an ICU and had their brain activity monitored with continuous EEG. Monitoring was typically started within hours of cardiac arrest and continues for several hours to days depending on the patients’ condition, so recording start time and duration varies from patient to patient. This database include EEG clips (up to 5-minute long) obtained hourly from the time of initiation of EEG and up to 72 hours from ROSC for each individual patient. 

Clinical Data

Patient information recorded at the time of admission (age, sex), location of arrest (out or in-hospital), type of cardiac rhythm recorded at the time of resuscitation (shockable rhythms include ventricular fibrillation or ventricular tachycardia and non-shockable rhythms include asystole and pulseless electrical activity), and the time between cardiac arrest and ROSC. Patient temperature after cardiac arrest is controlled using a closed-loop feedback device (TTM) in most patients unless there are contraindications such as severe and difficult to control hypotension or delay in hospital admission. For patients undergoing TTM, the temperature level can be controlled at either 36 or 33 degrees Celsius.

Neurological Prognostication and Outcome Assessment

All participating hospitals have protocols for multimodal neurological prognostication that follow international guideline recomendations. Formal neurological prognostication is deferred until the normothermia phase and confounding from sedatives can be minimized.

Patient Outcomes

Clinical outcome was determined prospectively in two centers by phone interview (at 6 months from ROSC), and at the remaining five hospitals retrospectively through chart review (at 3-6 months from ROSC). Neurological function was determined using the best Cerebral Performance Category (CPC) scale [12]. CPC is an ordinal scale ranging from 1 to 5, ranging from good neurological function to death.


Clinical and EEG data were de-identified. Patients with age above 89 years old are listed with age "90". EEG timestamps are organized based on the time elapsed since ROSC.

Data Description

EEG Data

All EEG signal data is provided in WFDB format, with the signal data stored in MATLAB (MAT v4 format). MAT file. The files are timed based on the time elapsed since ROSC. For example, the binary signal file 0284_001_004_EEG.mat contains the first file with EEG signal data, which starts at 4 hours, 7 minutes, and 23 seconds after ROSC and ends at 4 hours, 59 minutes, and 59 seconds after ROSC, for patient 0284 of the I-CARE patient cohort. The plain text header file 0284_001_004_EEG.hea describes the contents of the signal file as well as the start time, stop time, and utility frequency (i.e., powerline frequency or mains frequency) for the data.

The sampling rate for the EEG files is unchanged from the original data received from participating sites. Each file contains an array with EEG signals from 18 bipolar channel pairs. Patients may have EEG started several hours after the arrest or need to have brain monitoring interrupted transiently while in the ICU, so gaps in data may be present. The EEG recordings continue for several hours to days, so the EEG signals are prone to quality deterioration from non-physiological artifacts. The EEG data might have one or many channels contaminated by artifact, and some patients might only have artifactual, non-physiological data available.

Clinical Data and Patient Outcome

The following clinical data is contained in each .txt file: 

Age (in years): number

Sex: Male, Female

ROSC (return of spontaneous circulation, in minutes): time from cardiac arrest to return of spontaneous circulation

OHCA (out-of-hospital cardiac arrest): True = out of hospital cardiac arrest: False = in-hospital cardiac arrest

VFib (ventricular fibrillation): True = shockable rhythm, False = non-shockable rhythm

TTM (targeted temperature management; in Celsius): 33, 36, or NaN for no TTM

Outcome: Good (CPC score of 1-2), Poor (CPC score of 3-5)

CPC: Cerebral Performance Category (CPC) score (ordinal scale 1-5)

CPC = 1: good neurological function and independent for activities of daily living 

CPC = 2: moderate neurological disability but independent for activities of daily living

CPC = 3: severe neurological disability

CPC = 4: unresponsive wakefulness syndrome [previously known as vegetative state] 

CPC = 5: dead. 

We have grouped CPC scores in two categories: 

  • “Good outcome”: CPC = 1 or 2
  • “Poor outcome”: CPC = 3, 4, or 5

Usage Notes

These data are in a WFDB-compatible format, and WFDB packages can be used to read them. 

We have implemented example prediction algorithms in MATLAB and Python that read the data:

MATLAB example at [14].

Python example at [15].

Release Notes

By downloading the data, you agree not to repost the data or to publish or otherwise share any work that uses the data, in full or in part, before the end of the PhysioNet Challenge 2023 except to the Computing in Cardiology conference. After that, you may publish work that uses the data, bu tyou may not repost the data. 


Independent Institutional Review Board approvals at participating hospitals were pursued - Beth Israel Deaconess Medical Center Committee on Clinical Investigations (2015P000004): Brain Data Science Contributing Collaborators Consortium (July 1st, 2015); Massachusetts General Brigham Human Research Protection Program (2013P001024): A Database to Support Large-Scale Acute Care Research (October 24th, 2013); Yale Human Investigation Committee (111009342): Urgent Inpatient EEG and Multimodality Monitoring Databank; The Medical Research Ethics Committee Twente (K12-01): EEG measurements in the ICU (January 2nd, 2012); Erasme Hospital Ethics Committee (P2014/119): Critical Care EEG Monitoring Research Database (May 30, 2014). Need for informed consent was waived by the institutional review board at all participating centers for analysis of data obtained as part of routine medical care. Informed consent was obtained on clinical follow up from surviving patients in the University of Twente cohort.


We gratefully acknowledge support from the The AWS Open Data Sponsorship Program, which allows us to share this data with the research community.  

Conflicts of Interest

M.V.P is founder of Clinical Science Systems. Clinical Science Systems did not contribute funding nor played any role in the study. M.B.W. is a co-founder of Beacon Biosignals. Beacon Biosignals did not contribute funding nor played any role in the study.


  1. Yan, S., Gan, Y., Jiang, N. et al. The global survival rate among adult out-of-hospital cardiac arrest patients who received cardiopulmonary resuscitation: a systematic review and meta-analysis. Crit Care 24, 61 (2020).
  2. Dankiewicz J, Cronberg T, Lilja G, et al. Hypothermia versus Normothermia after Out-of-Hospital Cardiac Arrest. N Engl J Med. 2021;384:2283–2294.
  3. Elmer J, Torres C, Aufderheide TP, Austin MA, Callaway CW, Golan E, Herren H, Jasti J, Kudenchuk PJ, Scales DC, Stub D, Richardson DK, Zive DM; Resuscitation Outcomes Consortium. Association of early withdrawal of life-sustaining therapy for perceived neurological prognosis with mortality after cardiac arrest. Resuscitation. 2016 May;102:127-35. doi: 10.1016/j.resuscitation.2016.01.016. Epub 2016 Feb 3. PMID: 26836944; PMCID: PMC4834233.
  4. Amorim E, Rittenberger JC, Zheng JJ, et al. Continuous EEG monitoring enhances multimodal outcome prediction in hypoxic-ischemic brain injury. Resuscitation. 2016;109:121–126.
  5. Hofmeijer J, Beernink TMJ, Bosch FH, Beishuizen A, Tjepkema-Cloostermans MC, van Putten MJAM. Early EEG contributes to multimodal outcome prediction of postanoxic coma. Neurology. 2015;85:137–143.
  6. Zheng W-L, Amorim E, Jing J, et al. Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks. Resuscitation. 2021;169:86–94.
  7. Zheng W-L, Amorim E, Jing J, et al. Predicting Neurological Outcome from Electroencephalogram Dynamics in Comatose Patients after Cardiac Arrest with Deep Learning. IEEE Trans Biomed Eng. Epub 2021.:1–1.
  8. Khazanova D, Douglas VC, Amorim E. A matter of timing: EEG monitoring for neurological prognostication after cardiac arrest in the era of targeted temperature management. Minerva Anestesiol. 2021;87:704–713.
  9. Ruijter BJ, van Putten MJAM, van den Bergh WM, Tromp SC, Hofmeijer J. Propofol does not affect the reliability of early EEG for outcome prediction of comatose patients after cardiac arrest. Clin Neurophysiol Off J Int Fed Clin Neurophysiol. 2019;130:1263–1270.
  10. Amorim E, Zheng WL, Ghassemi MM, Aghaeeaval A, Kandhare P, Karukonda V, Lee JW, Herman ST, Sivaraju A, Nicolas Gaspard N, Hofmeijer J, van Putten MJAM, Sameni R, Reyna MA, Clifford GD, Westover MB. The International Cardiac Arrest Research (I-CARE) Consortium Electroencephalography Database. Critical Care Medicine (in print); doi:
  11. Reyna MA, Amorim E, Sameni S, Weigle J, Elola A, Bahrami Rad A, Seyedi S, Kwon H, Zheng, WL and Ghassemi M, van Putten MJAM, Hofmeijer J, Gaspard N, Sivaraju A, Herman S, Lee JW, Westover MB, Clifford GD.: Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023. Atlanta, USA: 2023. p. 1:4.
  12. Taccone FS, Horn J, Storm C, et al. Death after awakening from post-anoxic coma: the “Best CPC” project. Crit Care Lond Engl. 2019;23:107.
  13. International Cardiac Arrest EEG Consortium (ICARE) Dataset with Deep Learning.
  14. PhysioNet 2023 Challenge MATLAB Example.
  15. PhysioNet 2023 Challenge Python Example.


Access Policy:
Only registered users who sign the specified data use agreement can access the files.

License (for files):
BDSP Restricted Health Data License 1.0.0

Data Use Agreement:
BDSP Restricted Health Data Use Agreement

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