Clifford Gari D, Liu Chengyu, Moody Benjamin, Lehman Li-Wei H, Silva Ikaro, Li Qiao, Johnson A E, Mark Roger G
Department of Biomedical Informatics, Emory University, Atlanta, USA.
Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, USA.
Comput Cardiol (2010). 2017 Sep;44. doi: 10.22489/CinC.2017.065-469. Epub 2018 Apr 5.
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 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.
2017年生理网/心脏病学计算（CinC）挑战赛聚焦于在患者进行的短期（9至61秒）心电图记录中，区分房颤与噪声、正常心律或其他心律。总共使用了12186份心电图：8528份用于公共训练集，3658份用于私有隐藏测试集。由于很大一部分专家标注之间存在高度的专家间分歧，我们在比赛中期采用了一种自采样法对数据进行专家重新标注，利用表现最佳的挑战赛参赛团队的算法来识别有争议的标注。共有75个独立团队使用了从随机森林到应用于频谱域原始数据的深度学习方法等各种传统和新颖方法参加了挑战赛。四个团队以0.83的同等高F1分数（在所有类别上平均）赢得了挑战赛，尽管排名前11的算法得分在该分数的2%以内。使用套索算法识别出的45种算法的组合实现了0.87的F1分数，这表明投票方法可以提高性能。
