使用U-Sleep:一个卷积神经网络的自动儿科睡眠阶段分类的评估:一个卷积神经网络
Ajay Kevat1,2, Rylan Steinkey2, Sadasivam Suresh1,2
1Department of Respiratory and Sleep Medicine, Queensland Children's Hospital, Brisbane, Queensland, Australia.
概括
使用U-Sleep进行的自动睡眠分阶段测定显示了与儿童中人类得分器相当的性能. 然而,在年幼的儿童和睡眠呼吸障碍或并发症的儿童中,准确性下降,需要专家审查.
科学领域:
- 睡眠医学 睡眠医学
- 医疗保健中的人工智能
- 儿科睡眠分析的方法
背景情况:
- U-Sleep 是一个公开可用的自动化睡眠分期工具.
- 在儿科患者群体中缺乏独立验证.
- 这项研究解决了对儿童的验证需求.
研究的目的:
- 为了验证U-Sleep与训练有素的人类得分者的表现,使用儿科多睡眠图.
- 确定影响儿童U-Sleep准确性的人口和临床因素.
- 为了评估U-Sleep与专家人类睡眠分期的等价性.
主要方法:
- 使用了一组协同数据集 (50 个儿科多眠图片摘录) 和一组临床数据集 (3,114 个多眠图).
- 将U-Sleep的5阶段睡眠分期与使用科恩的卡帕的"黄金"标准得分进行了比较.
- 采用Wilcoxon 2 1面试验进行等价性测试和多变量回归进行因子分析.
主要成果:
- 在一致性数据集中,U-Sleep在统计学上实现了与受过训练的人类相当的性能 (kappa = 0.79 vs 0.78).
- 临床数据集中kappa协议的中位数为0.69.
- 在2岁以下的儿童,患有并发症,睡眠效率下降或睡眠呼吸障碍的儿童中观察到更低的准确性.
结论:
- 在统计学上,U-Sleep的表现与训练有素的得分器相等,用于儿科睡眠分阶段.
- 在特定的儿科亚组中,精度降低,包括婴儿和睡眠呼吸障碍或并发症患者.
- 当通过专家临床医生审查补充时,U-Sleep适用于儿科的临床使用.
相关概念视频
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