Resources


Challenge Open Access

Heart Murmur Detection from Phonocardiogram Recordings: The George B. Moody PhysioNet Challenge 2022

Matthew Reyna, Yashar Kiarashi, Andoni Elola, Jorge Oliveira, Francesco Renna, Annie Gu, Erick Andres Perez Alday, Nadi Sadr, Sandra Mattos, Miguel Coimbra, Reza Sameni, Ali Bahrami Rad, Zuzana Koscova, Gari Clifford

2022 Physionet Challenge is devoted to detecting the presence or absence of murmurs from multiple heart sound recordings from multiple auscultation locations, as well as detecting the clinical outcomes.

challenge competition cardiac auscultation congenital heart diseases

Published: Sept. 28, 2023. Version: 1.0.0

Database Credentialed Access

Neurocritical care waveform recordings in pediatric patients

Thomas Heldt, Andrea Fanelli, Robert Tasker, Frederick Vonberg, Kerri LaRovere

The database contains waveform recordings, including arterial blood pressure, intracranial pressure, and cerebral blood flow velocity, from pediatric patients in neurocritical care.

intracranial pressure arterial blood pressure noninvasive icp neurocritical care neurotrauma cerebral blood flow velocity pediatric patients

Published: Jan. 8, 2024. Version: 1.0.0

Database Credentialed Access

Neurocritical care waveform recordings in pediatric patients

Thomas Heldt, Andrea Fanelli, Robert Tasker, Frederick Vonberg, Kerri LaRovere

The database contains waveform recordings, including arterial blood pressure, intracranial pressure, and cerebral blood flow velocity, from pediatric patients in neurocritical care.

intracranial pressure arterial blood pressure noninvasive icp neurocritical care neurotrauma cerebral blood flow velocity pediatric patients

Published: Jan. 8, 2024. Version: 1.0.0

Database Open Access

MIMIC Database

The MIMIC Database includes data recorded from over 90 ICU patients. The data in each case include signals and periodic measurements obtained from a bedside monitor as well as clinical data obtained from the patient's medical record. The recordi…

health record icu ehr critical care mimic

Published: March 15, 2000. Version: 1.0.0

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Database Open Access

Influence of the MHD effect on 12-lead and 3-lead ECGs recorded in 1T to 7T MRI scanners

Johannes W Krug Passand

ECG signals were acquired in various MRI scanners to enable the study of the magnetohydrodynamic (MHD) effect. The MHD effect, which is caused by an interaction of the blood flow and the MRI’s high static magnetic field, superimposes the ECG signal.

cardiac mri patient monitoring magnetohydrodynamic mri mhd ecg

Published: May 18, 2021. Version: 1.0.0

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Database Credentialed Access

Critical care database comprising patients with infection at Zigong Fourth People's Hospital

Ping Xu, Lin Chen, Zhongheng Zhang

Routinely collected data from critical care units at Zigong Fourth People’s Hospital, Sichuan, China for patients admitted between January 2019 and December 2020 Missing information on temperature are updated in the new version.

Published: June 30, 2022. Version: 1.1

Database Open Access

MIMIC-IV Waveform Database

Benjamin Moody, Sicheng Hao, Brian Gow, Tom Pollard, Wei Zong, Roger Mark

The MIMIC-IV Waveform Database collects physiological signals and measurements from ICU bedside monitors. Coupled with the clinical information available in MIMIC-IV, it provides a detailed view into the physiology of critically ill patients.

Published: July 10, 2022. Version: 0.1.0

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Database Open Access

I-CARE: International Cardiac Arrest REsearch consortium Database

Edilberto Amorim, Wei-Long Zheng, Jong Woo Lee, Susan Herman, Mohammad Ghassemi, Adithya Sivaraju, Nicolas Gaspard, Jeannette Hofmeijer, Michel J A M van Putten, Matthew Reyna, Gari Clifford, Brandon Westover

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

Published: Dec. 14, 2023. Version: 2.1

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Database Open Access

Hillel Yaffe Glaucoma Dataset (HYGD): A Gold-Standard Annotated Fundus Dataset for Glaucoma Detection

Or Abramovich, Hadas Pizem, Jonathan Fhima, Eran Berkowitz, Ben Gofrit, Jan Van Eijgen, Eytan Blumenthal, Joachim Behar

HYGD is a rigorously annotated fundus image dataset with gold-standard clinical labels designed to improve and benchmark deep learning models for accurate glaucoma detection.

ophthalmology retina dfi gold-standard gon fundus glaucoma

Published: June 3, 2025. Version: 1.0.0

Database Open Access

ECG Fragment Database for the Exploration of Dangerous Arrhythmia

Anatoly Nemirko, Liudmila Manilo, Anna Tatarinova, Boris Alekseev, Ekaterina Evdakova

Dataset derived from the MIT-BIH Malignant Ventricular Ectopy Database.

short ecg fragments dangerous arrhythmias ecg

Published: March 17, 2022. Version: 1.0.0

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