AF Classification from a Short Single Lead ECG Recording: the Physionet Computing in Cardiology Challenge 2017
Georgia Institute of Technology · Emory University · +2 more institutions
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 inter-expert 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…
Citation impact
- FWCI
- 51.17
- Percentile
- 100%
- References
- 5
Authors
8Topics & keywords
- Random forest
- Computer science
- Artificial intelligence
- Test set
- Machine learning
- Lasso (programming language)
- F1 score
- Training set