Mohammad M Ghassemi, Benjamin E Moody, Li-Wei H Lehman, Christopher Song, Qiao Li, Haoqi Sun, Roger G Mark, M Brandon Westover, Gari D Clifford
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引用次数: 107
摘要
PhysioNet/Computing in Cardiology Challenge 2018专注于使用在睡眠多导睡眠图研究中收集的各种生理信号(EEG, EOG, EMG, ECG, SaO2)来检测睡眠期间唤醒(非呼吸暂停)的来源。参赛者共获得1,983份多导睡眠图记录。994个录音的唤醒标签在公共训练集中可用，而989个标签保留在隐藏测试集中。挑战者被要求开发一种算法，可以在隐藏的测试集中标记唤醒的存在。用于评估进入者的绩效指标是精确召回曲线下的面积。共有22个独立团队参加了挑战赛，部署了从广义线性模型到深度神经网络的各种方法。
You Snooze, You Win: the PhysioNet/Computing in Cardiology Challenge 2018.
The PhysioNet/Computing in Cardiology Challenge 2018 focused on the use of various physiological signals (EEG, EOG, EMG, ECG, SaO2) 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.
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