PhysioNet/CinC Challenge 2017: Training Sets

Version 3 - October 2017

When referencing this material, please cite:

Gari Clifford, Chengyu Liu, Benjamin Moody, Li-wei H. Lehman, Ikaro Silva, Qiao Li, Alistair Johnson, Roger G. Mark. AF Classification from a Short Single Lead ECG Recording: the PhysioNet Computing in Cardiology Challenge 2017. Computing in Cardiology (Rennes: IEEE), Vol 44, 2017 (In Press).

Please also include the standard citation for PhysioNet:

Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh, Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals. Circulation 101(23):e215-e220 [Circulation Electronic Pages; http://circ.ahajournals.org/content/101/23/e215]; 2000 (June 13). [bib]
@article{PhysioNet,
  author    = {Goldberger, Ary L. and Amaral, Luis A. N.
               and Glass, Leon and Hausdorff, Jeffrey M.
               and Ivanov, Plamen Ch. and Mark, Roger G.
               and Mietus, Joseph E. and Moody, George B.
               and Peng, Chung-Kang and Stanley, H. Eugene},
  title     = {{PhysioBank}, {PhysioToolkit}, and {PhysioNet}:
               Components of a New Research Resource for Complex
               Physiologic Signals},
  journal   = {Circulation},
  publisher = {American Heart Association, Inc.},
  volume    = {101},
  number    = {23},
  year      = {2000},
  month     = {June},
  pages     = {e215--e220},
  doi       = {10.1161/01.CIR.101.23.e215},
  issn      = {0009-7322},
  url       = {http://circ.ahajournals.org/content/101/23/e215}
}

This database contains 8,528 ECG recordings that were provided as a public training set for use in the 2017 PhysioNet/Computing in Cardiology Challenge. These recordings were collected using an AliveCor hand-held device, which uploads the recording automatically through an application on the user's mobile phone. These recordings, along with 3,658 recordings that have been retained as a hidden test set, were provided by AliveCor for use in the Challenge.

ECG recordings were sampled at 300 Hz and band-pass filtered by the AliveCor device. All data are provided in MATLAB V4 WFDB-compliant format (each including a .mat file containing the ECG and a .hea file containing the waveform information). Each recording has been classified as either normal rhythm, atrial fibrillation, other rhythm, or "too noisy to classify". See the main Challenge page for more information.

A subset of this database (300 records) is designated as the "validation" set. This subset was used by the automated scoring system to verify that the participant's algorithm was working as intended. See Sandbox Evaluation of Entries in PhysioNet/Computing in Cardiology Challenges for more information.

Icon  Name                            Last modified      Size  Description
[PARENTDIR] Parent Directory - [DIR] training/ 2017-10-26 13:47 - [DIR] validation/ 2017-10-26 13:48 -

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Updated Friday, 28 October 2016 at 16:58 EDT

PhysioNet is supported by the National Institute of General Medical Sciences (NIGMS) and the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number 2R01GM104987-09.