Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy
复制标题
睡眠阶段的神经网络分析可以有效诊断发作性睡病
DOI：
10.1038/s41467-018-07229-3
复制
发表时间：
2018-12-06
影响因子：
16.6
通讯作者：
Mignot, Emmanuel
中科院分区：
文献类型：
--
作者：
Stephansen, Jens B.;Olesen, Alexander N.;Mignot, Emmanuel
用于诊断睡眠障碍如1型发作性睡病（T1 N）的睡眠分析目前需要由受过训练的评分技术人员对多导睡眠图记录进行目视检查。在这里，我们在大约3,000个正常和异常睡眠记录中使用神经网络来自动化睡眠阶段评分，产生一个睡眠密度图-一个比经典睡眠图传达更多信息的概率分布。睡眠阶段评分的准确性进行了验证，在70名受试者由6个评分。最佳模型的表现优于任何个人评分者（87%与共识）。它也可靠地评分睡眠下降到5秒，而不是30秒的评分时期。基于不寻常的睡眠阶段重叠的T1 N标记物达到了96%的特异性和91%的灵敏度，在独立的数据集验证。增加HLA-DQB 1 *06：02分型将特异性提高至99%。我们的方法可以减少在睡眠诊所花费的时间，并自动化T1 N诊断。它还打开了使用家庭睡眠研究诊断T1 N的可能性。
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph—a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.