Kjaer Troels Wesenberg, Rank Mike Lind, Hemmsen Martin Christian, Kidmose Preben, Mikkelsen Kaare
Visiting professor, Department of Neuroscience, University of Copenhagen, Denmark.
T&W Engineering, Lynge, Denmark.
PLOS Digit Health. 2022 Oct 27;1(10):e0000134. doi: 10.1371/journal.pdig.0000134. eCollection 2022 Oct.
While polysomnography (PSG) is the gold standard to quantify sleep, modern technology allows for new alternatives. PSG is obtrusive, affects the sleep it is set out to measure and requires technical assistance for mounting. A number of less obtrusive solutions based on alternative methods have been introduced, but few have been clinically validated. Here we validate one of these solutions, the ear-EEG method, against concurrently recorded PSG in twenty healthy subjects each measured for four nights. Two trained technicians scored the 80 nights of PSG independently, while an automatic algorithm scored the ear-EEG. The sleep stages and eight sleep metrics (Total Sleep Time (TST), Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset, REM latency, REM fraction of TST, N2 fraction of TST, and N3 fraction of TST) were used in the further analysis. We found the sleep metrics: Total Sleep Time, Sleep Onset Latency, Sleep Efficiency, Wake After Sleep Onset were estimated with high accuracy and precision between automatic sleep scoring and manual sleep scoring. However, the REM latency and REM fraction of sleep showed high accuracy but low precision. Further, the automatic sleep scoring systematically overestimated the N2 fraction of sleep and slightly underestimated the N3 fraction of sleep. We demonstrate that sleep metrics estimated from automatic sleep scoring based on repeated ear-EEG in some cases are more reliably estimated with repeated nights of automatically scored ear-EEG than with a single night of manually scored PSG. Thus, given the obtrusiveness and cost of PSG, ear-EEG seems to be a useful alternative for sleep staging for the single night recording and an advantageous choice for several nights of sleep monitoring.
虽然多导睡眠图（PSG）是量化睡眠的金标准，但现代技术带来了新的替代方法。PSG具有侵入性，会影响其旨在测量的睡眠，且安装需要技术支持。基于替代方法的一些侵入性较小的解决方案已被引入，但很少有经过临床验证的。在此，我们针对20名健康受试者同时记录的PSG，对其中一种解决方案——耳部脑电图（ear-EEG）方法进行验证，每位受试者测量四晚。两名经过培训的技术人员独立对80晚的PSG进行评分，而一种自动算法对耳部脑电图进行评分。进一步分析中使用了睡眠阶段和八个睡眠指标（总睡眠时间（TST）、入睡潜伏期、睡眠效率、睡眠中觉醒、快速眼动潜伏期、快速眼动睡眠时间占总睡眠时间的比例、N2睡眠时间占总睡眠时间的比例以及N3睡眠时间占总睡眠时间的比例）。我们发现，在自动睡眠评分和人工睡眠评分之间，总睡眠时间、入睡潜伏期、睡眠效率、睡眠中觉醒等睡眠指标的估计具有较高的准确性和精确性。然而，快速眼动潜伏期和快速眼动睡眠时间占比显示出较高的准确性但精确性较低。此外，自动睡眠评分系统地高估了N2睡眠时间占比，略微低估了N3睡眠时间占比。我们证明，在某些情况下，基于重复耳部脑电图的自动睡眠评分所估计的睡眠指标，与单晚人工评分的PSG相比，通过多晚自动评分的耳部脑电图进行估计更为可靠。因此，考虑到PSG的侵入性和成本，耳部脑电图似乎是单晚记录睡眠分期的有用替代方法，也是多晚睡眠监测的有利选择。
