Li Hongyang, Guan Yuanfang
Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA.
Commun Biol. 2021 Jan 4;4(1):18. doi: 10.1038/s42003-020-01542-8.
Sleep arousals are transient periods of wakefulness punctuated into sleep. Excessive sleep arousals are associated with symptoms such as sympathetic activation, non-restorative sleep, and daytime sleepiness. Currently, sleep arousals are mainly annotated by human experts through looking at 30-second epochs (recorded pages) manually, which requires considerable time and effort. Here we present a deep learning approach for automatically segmenting sleep arousal regions based on polysomnographic recordings. Leveraging a specific architecture that 'translates' input polysomnographic signals to sleep arousal labels, this algorithm ranked first in the "You Snooze, You Win" PhysioNet Challenge. We created an augmentation strategy by randomly swapping similar physiological channels, which notably improved the prediction accuracy. Our algorithm enables fast and accurate delineation of sleep arousal events at the speed of 10 seconds per sleep recording. This computational tool would greatly empower the scoring process in clinical settings and accelerate studies on the impact of arousals.
睡眠微觉醒是穿插于睡眠中的短暂觉醒期。过多的睡眠微觉醒与诸如交感神经激活、非恢复性睡眠和日间嗜睡等症状相关。目前，睡眠微觉醒主要由人类专家通过手动查看30秒的时段（记录页面）来标注，这需要大量的时间和精力。在此，我们提出一种基于多导睡眠图记录自动分割睡眠微觉醒区域的深度学习方法。利用一种将输入的多导睡眠图信号“转换”为睡眠微觉醒标签的特定架构，该算法在“你打盹，你就赢”生理信号挑战赛中排名第一。我们通过随机交换相似的生理通道创建了一种增强策略，显著提高了预测准确率。我们的算法能够以每个睡眠记录10秒的速度快速准确地描绘睡眠微觉醒事件。这种计算工具将极大地助力临床环境中的评分过程，并加速关于微觉醒影响的研究。
