Ghassemi Mohammad M, Moody Benjamin E, Lehman Li-Wei H, Song Christopher, Li Qiao, Sun Haoqi, Mark Roger G, Westover M Brandon, Clifford Gari D
Institute for Medical Engineering & Science, Massachusetts Institute of Technology, USA.
Malone Center for Engineering in Healthcare, Johns Hopkins University, USA.
Comput Cardiol (2010). 2018 Sep;45. doi: 10.22489/cinc.2018.049. Epub 2019 Jun 24.
The PhysioNet/Computing in Cardiology Challenge 2018 focused on the use of various physiological signals (EEG, EOG, EMG, ECG, SaO) collected during polysomnographic sleep studies to detect sources of arousal (non-apnea) during sleep. A total of 1,983 polysomnographic recordings were made available to the entrants. The arousal labels for 994 of the recordings were made available in a public training set while 989 labels were retained in a hidden test set. Challengers were asked to develop an algorithm that could label the presence of arousals within the hidden test set. The performance metric used to assess entrants was the area under the precision-recall curve. A total of twenty-two independent teams entered the Challenge, deploying a variety of methods from generalized linear models to deep neural networks.
2018年生理网/心脏病学计算挑战赛聚焦于利用多导睡眠图睡眠研究期间收集的各种生理信号（脑电图、眼电图、肌电图、心电图、血氧饱和度）来检测睡眠期间的觉醒源（非呼吸暂停）。共有1983份多导睡眠图记录提供给参赛者。其中994份记录的觉醒标签在一个公共训练集中提供，而989个标签保留在一个隐藏测试集中。要求挑战者开发一种算法，该算法可以对隐藏测试集中的觉醒情况进行标记。用于评估参赛者的性能指标是精确率-召回率曲线下的面积。共有22个独立团队参加了挑战赛，采用了从广义线性模型到深度神经网络的各种方法。
