Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, Berkeley, United States.
Elife. 2021 Oct 14;10:e70092. doi: 10.7554/eLife.70092.
The clinical and societal measurement of human sleep has increased exponentially in recent years. However, unlike other fields of medical analysis that have become highly automated, basic and clinical sleep research still relies on human visual scoring. Such human-based evaluations are time-consuming, tedious, and can be prone to subjective bias. Here, we describe a novel algorithm trained and validated on +30,000 hr of polysomnographic sleep recordings across heterogeneous populations around the world. This tool offers high sleep-staging accuracy that matches human scoring accuracy and interscorer agreement no matter the population kind. The software is designed to be especially easy to use, computationally low-demanding, open source, and free. Our hope is that this software facilitates the broad adoption of an industry-standard automated sleep staging software package.
近年来,人类睡眠的临床和社会测量呈指数级增长。然而,与其他已经高度自动化的医学分析领域不同,基础和临床睡眠研究仍然依赖于人工视觉评分。这种基于人工的评估既耗时又乏味,并且容易受到主观偏见的影响。在这里,我们描述了一种新的算法,该算法是在全球不同人群的 30,000 多小时多导睡眠记录上进行训练和验证的。该工具提供了高精度的睡眠分期,无论人群类型如何,其准确性都可与人工评分的准确性和评分者间的一致性相匹配。该软件旨在易于使用,计算要求低,开源且免费。我们希望该软件能够促进行业标准的自动睡眠分期软件包的广泛采用。