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

Visualize waveforms

Database Restricted Access

VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs

Hieu Huy Pham, Hieu Nguyen Trung, Ha Quy Nguyen

VinDr-SpineXR: A large annotated medical image dataset for spinal lesions detection and classification from radiographs

Published: Aug. 24, 2021. Version: 1.0.0

Database Open Access

A Pressure Map Dataset for In-bed Posture Classification

Pressure sensor data captured from 13 participants in various sleeping postures.

pressure sleep posture gait

Published: Sept. 28, 2017. Version: 1.0.0

Challenge Open Access

AF Classification from a Short Single Lead ECG Recording: The PhysioNet/Computing in Cardiology Challenge 2017

The 2017 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify, from a single short ECG lead recording (between 30 s and 60 s in length), whether the recording shows normal sinus rhythm, atrial fibrillation (AF), an al…

atrial fibrillation challenge ecg

Published: Feb. 1, 2017. Version: 1.0.0

Challenge Open Access

Classification of Heart Sound Recordings: The PhysioNet/Computing in Cardiology Challenge 2016

The 2016 PhysioNet/CinC Challenge aims to encourage the development of algorithms to classify heart sound recordings collected from a variety of clinical or nonclinical (such as in-home visits) environments. The aim is to identify, from a single sho…

sound heart challenge phonocardiogram

Published: March 4, 2016. Version: 1.0.0

Database Restricted Access

KURIAS-ECG: a 12-lead electrocardiogram database with standardized diagnosis ontology

Hakje Yoo, Yunjin Yum, Soowan Park, Jeong Moon Lee, Moonjoung Jang, Yoojoong Kim, Jong-Ho Kim, Hyun-Joon Park, Kap Su Han, Jae Hyoung Park, Hyung Joon Joo

The KURIAS-ECG database is a high-quality 12-lead ECG DB including standard vocabulary (SNOMED CT, OMOP-CDM), and ECG diagnoses of our DB are grouped into 10 diagnoses by applying the minnesota code.

snomed 12-lead minnesota ecg

Published: Nov. 8, 2021. Version: 1.0

Database Credentialed Access

Generalized Image Embeddings for the MIMIC Chest X-Ray dataset

Andrew Sellergren, Atilla Kiraly, Tom Pollard, Wei-Hung Weng, Yun Liu, Akib Uddin, Christina Chen

This database contains compact information-rich embeddings of the MIMIC-CXR Database v2.0.0 using the CXR Foundation API v1.0.

Published: Feb. 22, 2023. Version: 1.0

Database Restricted Access

Multitaper spectra recorded during GABAergic anesthetic unconsciousness

John Abel, Marcus Badgeley, Benyamin Meschede-Krasa, Gabe Schamberg, Indie Garwood, Kimaya Lecamwasam, Sourish Chakravarty, David Zhou, Matt Keating, Patrick Purdon, Emery Brown

EEG power spectra recorded during anesthesia

eeg electroencephalography unconsciousness anesthesia

Published: April 19, 2021. Version: 1.0.0

Database Open Access

Wide-field calcium imaging sleep state database

Eric Landsness, Xiaohui Zhang, Wei Chen, Hanyang Miao, Michelle Tang, Lindsey Brier, Mark Anastasio, Jin-Moo Lee, Joseph Culver

Wide-field calcium imaging database that consists of annotated sleep recording collected from transgenic mice at Washington University of St Louis School of Medicine.

sleep machine learning wide-field calcium imaging sleep state classification sleep staging

Published: March 17, 2022. Version: 1.0.1

Database Credentialed Access

MS-CXR-T: Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing

Shruthi Bannur, Stephanie Hyland, Qianchu Liu, Fernando Pérez-García, Max Ilse, Daniel Coelho de Castro, Benedikt Boecking, Harshita Sharma, Kenza Bouzid, Anton Schwaighofer, Maria Teodora Wetscherek, Hannah Richardson, Tristan Naumann, Javier Alvarez Valle, Ozan Oktay

The MS-CXR-T is a multimodal benchmark that enhances the MIMIC-CXR v2 dataset by including expert-verified annotations. Its goal is to evaluate biomedical visual-language processing models in terms of temporal semantics extracted from image and text.

multimodal radiology chest x-ray cxr disease progression vision-language processing

Published: March 17, 2023. Version: 1.0.0