AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017
Name
nihms955302.pdf
Description
Accepted version
Size
476.75 KB
Format
Adobe PDF
Checksum (MD5)
443fa10437a2b1e743a765646c700dcd
Author(s)
Moody, Benjamin Edward
Lehman, Li-Wei
Silva, Ikaro
Johnson, A.
Mark, Roger G
Date Issued
September 2017
Journal
Computing in cardiology
Publisher
Computing in Cardiology
Citation
Clifford, Gari D. et al. “AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017.” Computing in cardiology, vol. 44, 2017 © 2017 The Author(s)
Version
Author's final manuscript
Abstract
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 set. Due to the high degree of interexpert disagreement between a significant fraction of the expert labels we implemented a mid-competition bootstrap approach to expert relabeling of the data, levering the best performing Challenge entrants' algorithms to identify contentious labels. A total of 75 independent teams entered the Challenge using a variety of traditional and novel methods, ranging from random forests to a deep learning approach applied to the raw data in the spectral domain. Four teams won the Challenge with an equal high F1 score (averaged across all classes) of 0.83, although the top 11 algorithms scored within 2% of this. A combination of 45 algorithms identified using LASSO achieved an F1 of 0.87, indicating that a voting approach can boost performance.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.22489/CINC.2017.065-469
