Institute for Breathing & Sleep, Department of Respiratory & Sleep Medicine, Austin Health, Victoria, Australia.
J Clin Sleep Med. 2013 Dec 15;9(12):1315-24. doi: 10.5664/jcsm.3278.
Drowsiness is a major risk factor for motor vehicle and occupational accidents. Real-time objective indicators of drowsiness could potentially identify drowsy individuals with the goal of intervening before an accident occurs. Several ocular measures are promising objective indicators of drowsiness; however, there is a lack of studies evaluating their accuracy for detecting behavioral impairment due to drowsiness in real time.
In this study, eye movement parameters were measured during vigilance tasks following restricted sleep and in a rested state (n = 33 participants) at three testing points (n = 71 data points) to compare ocular measures to a gold standard measure of drowsiness (OSLER). The utility of these parameters for detecting drowsiness-related errors was evaluated using receiver operating characteristic curves (ROC) (adjusted by clustering for participant) and identification of optimal cutoff levels for identifying frequent drowsiness-related errors (4 missed signals in a minute using OSLER). Their accuracy was tested for detecting increasing frequencies of behavioral lapses on a different task (psychomotor vigilance task [PVT]).
Ocular variables which measured the average duration of eyelid closure (inter-event duration [IED]) and the ratio of the amplitude to velocity of eyelid closure were reliable indicators of frequent errors (area under the curve for ROC of 0.73 to 0.83, p < 0.05). IED produced a sensitivity and specificity of 71% and 88% for detecting ≥ 3 lapses (PVT) in a minute and 100% and 86% for ≥ 5 lapses. A composite measure of several eye movement characteristics (Johns Drowsiness Scale) provided sensitivities of 77% and 100% for detecting 3 and ≥ 5 lapses in a minute, with specificities of 85% and 83%, respectively.
Ocular measures, particularly those measuring the average duration of episodes of eye closure are promising real-time indicators of drowsiness.
困倦是机动车和职业事故的一个主要危险因素。实时客观的困倦指标可以潜在地识别困倦个体，并在事故发生前进行干预。一些眼部测量是有前途的困倦客观指标；然而，缺乏评估它们在实时检测因困倦而导致的行为障碍的准确性的研究。
在这项研究中，在限制睡眠后和休息状态下（n=33 名参与者）进行警觉任务期间测量眼动参数，并在三个测试点（n=71 个数据点）进行比较，将眼部测量与困倦的金标准测量（OSLER）进行比较。使用受试者聚类调整后的受试者工作特征曲线（ROC）评估这些参数检测困倦相关错误的效用，并确定识别频繁困倦相关错误的最佳截断水平（使用 OSLER 每分钟 4 次漏报）。在另一项任务（精神运动警觉任务[PVT]）上检测它们检测行为失误频率增加的准确性。
测量眼睑闭合平均持续时间（事件间持续时间[IED]）和眼睑闭合幅度与速度比的眼部变量是频繁错误的可靠指标（ROC 的曲线下面积为 0.73 至 0.83，p<0.05）。IED 对每分钟检测到≥3 次（PVT）和≥5 次（PVT）的敏感度和特异性分别为 71%和 88%和 100%和 86%。几个眼动特征的综合测量（Johns 困倦量表）对每分钟检测到 3 次和≥5 次的敏感度分别为 77%和 100%，特异性分别为 85%和 83%。
眼部测量，特别是测量眼睑闭合的平均持续时间的测量，是困倦的有前途的实时指标。
