使用深度生成式学习对睡眠统计数据的不同评级者之间的分歧影响的建模
IEEE journal of biomedical and health informatics
|August 10, 2023
概括
我们开发了U-Flow,这是一种用于睡眠分阶段的新型深度学习方法,它准确地模拟了睡眠统计中的评分者间的分歧. 这种技术捕获了在自动睡眠评分中经常丢失的宝贵信息,有助于睡眠医学研究.
科学领域:
- * 睡眠医学 睡眠医学
- * 计算神经科学 计算神经科学
- * 生物医学工程 * 生物医学工程
背景情况:
- *使用深度学习的自动睡眠分阶段显示出希望,但在衍生睡眠统计中难以捕捉得分者之间的变化.
- * 人类睡眠评分是劳动密集型,昂贵的,并因内在的得分者之间存在分歧而受到影响,影响睡眠统计数据的可靠性.
- * 睡眠统计数据中评分者间的分歧可能包含有关睡眠结构和个人睡眠模式的临床相关信息.
研究的目的:
- * 介绍U-Flow,一种新的条件生成模型,利用规范化流量来明确模拟夜间睡眠统计数据中的评分者间的分歧.
- *与现有的自动化方法相比,评估U-Flow在睡眠分期准确性和捕捉评分器间变化的能力方面的表现.
- * 展示模拟评审者间分歧的潜力,以提高睡眠医学和研究的洞察力.
主要方法:
- * 开发U-Flow,一种基于规范化流量的条件生成模型,旨在量化睡眠统计中的评分员间的分歧.
- * U-Flow与各种数据集上的其他自动睡眠评分技术的比较.
- *使用70名受试者组成的持久测试集进行验证,每个受试者由6名人类得分者独立得分.
主要成果:
- *U-Flow在与多数投票的催眠仪进行评估时,实现了与现有方法可比的睡眠分阶段性能 (准确性,科恩的卡帕).
- *U-Flow在准确建模夜间睡眠统计数据中存在的评价者间的不一致性方面明显优于其他方法.
- * 该模型在捕捉和表示得分者之间的变化方面表现出了效率.
结论:
- *U-Flow提供了一种强大的新方法,用于自动化睡眠评分,通过结合评级者之间的分歧的建模.
- * 捕获评分器之间的变异性提供了对睡眠结构和潜在的诊断信息的更全面的了解.
- * 这种方法可以通过利用分歧作为有价值数据来源,推进睡眠医学和基本睡眠研究.
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