Skorucak Jelena, Hertig-Godeschalk Anneke, Achermann Peter, Mathis Johannes, Schreier David R
Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland.
Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, Switzerland.
Front Neurosci. 2020 Jan 23;14:8. doi: 10.3389/fnins.2020.00008. eCollection 2020.
Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes. In the diagnostic sleep-wake and fitness-to-drive assessment, accurate and early identification of sleepiness is essential. However, in the absence of a standardised definition and a time-efficient scoring method of MSEs, these short fragments are not assessed in clinical routine. Based on data of moderately sleepy patients, we recently developed the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring of MSEs and corresponding machine learning algorithms for automatic MSE detection, both mainly based on the electroencephalogram (EEG). The present study aimed to investigate the relationship between automatically detected MSEs and driving performance in a driving simulator, recorded in parallel with EEG, and to assess algorithm performance for MSE detection in severely sleepy participants. Maintenance of wakefulness test (MWT) and driving simulator recordings of 18 healthy participants, before and after a full night of sleep deprivation, were retrospectively analysed. Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants. Driving performance was measured by the standard deviation of lateral position and the occurrence of off-road events. In comparison to visual scoring, automatic detection of MSEs in participants with severe sleepiness showed good performance (Cohen's kappa = 0.66). The MSE rate in the MWT correlated with the latency to the first MSE in the driving simulator ( = -0.54, < 0.05) and with the cumulative MSE duration in the driving simulator ( = 0.62, < 0.01). No correlations between MSE measures in the MWT and driving performance measures were found. In the driving simulator, multiple correlations between MSEs and driving performance variables were observed. Automatic MSE detection worked well, independent of the degree of sleepiness. The rate and the cumulative duration of MSEs could be promising sleepiness measures in both the MWT and the driving simulator. The correlations between MSEs in the driving simulator and driving performance might reflect a close and time-critical relationship between sleepiness and performance, potentially valuable for the fitness-to-drive assessment.
微睡眠发作（MSEs）是短暂的睡眠片段（1 - 15秒），可能导致危险情况并产生潜在的致命后果。在诊断性睡眠 - 觉醒和驾驶适宜性评估中，准确且早期识别嗜睡至关重要。然而，由于缺乏MSEs的标准化定义和高效的评分方法，这些短暂片段在临床常规中并未得到评估。基于中度嗜睡患者的数据，我们最近开发了用于MSEs视觉评分的伯尔尼连续高分辨率觉醒 - 睡眠（BERN）标准以及用于自动检测MSEs的相应机器学习算法，两者主要基于脑电图（EEG）。本研究旨在调查在驾驶模拟器中自动检测到的MSEs与驾驶性能之间的关系，同时记录EEG，并评估在严重嗜睡参与者中MSE检测的算法性能。回顾性分析了18名健康参与者在整夜睡眠剥夺前后的维持觉醒测试（MWT）和驾驶模拟器记录。在10名参与者的MWT记录中，将自动检测的性能与遵循BERN标准的MSEs视觉评分进行了比较。通过横向位置的标准差和越野事件的发生情况来测量驾驶性能。与视觉评分相比，在严重嗜睡的参与者中自动检测MSEs表现良好（科恩kappa系数 = 0.66）。MWT中的MSE率与驾驶模拟器中首次出现MSE的潜伏期相关（r = -0.54，P < 0.05），并与驾驶模拟器中MSE的累积持续时间相关（r = 0.62，P < 0.01）。未发现MWT中的MSE测量值与驾驶性能测量值之间存在相关性。在驾驶模拟器中，观察到MSEs与驾驶性能变量之间存在多重相关性。自动MSE检测效果良好，与嗜睡程度无关。MSEs的发生率和累积持续时间在MWT和驾驶模拟器中可能都是有前景的嗜睡测量指标。驾驶模拟器中MSEs与驾驶性能之间的相关性可能反映了嗜睡与性能之间密切且时间关键的关系，这对驾驶适宜性评估可能具有重要价值。
