AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017

@article{Clifford2017AFCF,
  title={AF classification from a short single lead ECG recording: The PhysioNet/computing in cardiology challenge 2017},
  author={Gari D. Clifford and Chengyu Liu and Benjamin Moody and Li-wei H. Lehman and Ikaro Silva and Qiao Li and Alistair Edward William Johnson and Roger G. Mark},
  journal={2017 Computing in Cardiology (CinC)},
  year={2017},
  pages={1-4}
}
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… CONTINUE READING

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