Resources


Challenge Open Access

Classification of 12-lead ECGs: The PhysioNet/Computing in Cardiology Challenge 2020

Erick Andres Perez Alday, Annie Gu, Amit Shah, Chengyu Liu, Ashish Sharma, Salman Seyedi, Ali Bahrami Rad, Matthew Reyna, Gari Clifford

The goal of the 2020 PhysioNet - Computing in Cardiology Challenge is to design and implement a working, open-source algorithm that can automatically identify cardiac abnormalities in 12-lead ECG recordings.

Published: July 29, 2022. Version: 1.0.2

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Challenge Open Access

Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Matthew Reyna, Nadi Sadr, Annie Gu, Erick Andres Perez Alday, Chengyu Liu, Salman Seyedi, Amit Shah, Gari Clifford

Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021

challenge cardiac abnormalities multilead ecgs classification competition

Published: July 29, 2022. Version: 1.0.3

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Challenge Open Access

Will Two Do? Varying Dimensions in Electrocardiography: The PhysioNet/Computing in Cardiology Challenge 2021

Matthew Reyna, Nadi Sadr, Annie Gu, Erick Andres Perez Alday, Chengyu Liu, Salman Seyedi, Amit Shah, Gari Clifford

Will Two Do? Varying Dimensions in Electrocardiography: the PhysioNet/Computing in Cardiology Challenge 2021

challenge cardiac abnormalities multilead ecgs classification competition

Published: July 29, 2022. Version: 1.0.3

Visualize waveforms

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

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 Restricted Access

CheXchoNet: A Chest Radiograph Dataset with Gold Standard Echocardiography Labels

Pierre Elias, Shreyas Bhave

Early detection of heart failure is vital for improving outcomes. The dataset contains 71,589 CXRs paired with gold standard labels from echocardiograms to enable the training of models to detect pathologies indicative of early stage heart failure.

heart failure chest x-rays deep learning early detection cardiac structural abnormalties

Published: March 20, 2024. Version: 1.0.0

Database Credentialed Access

EchoNotes Structured Database derived from MIMIC-III (ECHO-NOTE2NUM)

Gloria Hyunjung Kwak, Dana Moukheiber, Mira Moukheiber, Lama Moukheiber, Sulaiman Moukheiber, Neel Butala, Leo Anthony Celi, Christina Chen

A structured echocardiogram database derived from 43,472 observational notes obtained during echocardiogram studies conducted in the intensive care unit at the Beth Israel Deaconess Medical Center between 2001 and 2012.

Published: Feb. 23, 2024. Version: 1.0.0