AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017

AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017

The PhysioNet/Computing in Cardiology (CinC) Challenge 2017 focused on differentiating AF from noise, normal or other rhythms in short term (from 9-61 s) ECG recordings performed by patients. A total of 12, 186 ECGs were used: 8, 528 in the public training set and 3, 658 in the private hidden test s...

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Bibliographic Details
Main Authors: Moody, Benjamin Edward, Lehman, Li-Wei, Silva, Ikaro, Johnson, A., Mark, Roger G
Other Authors: Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Format: Article
Language:English
Published: Computing in Cardiology 2020
Online Access:https://hdl.handle.net/1721.1/126563

Internet

https://hdl.handle.net/1721.1/126563

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