| Literature DB >> 32040590 |
Takashi Abe1,2,3, Kazuo Mishima2,3,4, Shingo Kitamura3, Akiko Hida3, Yuichi Inoue5, Koh Mizuno1,6, Kosuke Kaida7, Kyoko Nakazaki3, Yuki Motomura3,8, Kazushi Maruo9, Toshiko Ohta1, Satoshi Furukawa1, David F Dinges10, Katsuhiko Ogata1.
Abstract
Vigilance deficits account for a substantial number of accidents and errors. Current techniques to detect vigilance impairment measure only the most severe level evident in eyelid closure and falling asleep, which is often too late to avoid an accident or error. The present study sought to identify ocular biometrics of intermediate impairment of vigilance and develop a new technique that could detect a range of deficits in vigilant attention (VA). Sixteen healthy adults performed well-validated Psychomotor Vigilance Test (PVT) for tracking vigilance attention while undergoing simultaneous recording of eye metrics every 2 hours during 38 hours of continuous wakefulness. A novel marker was found that measured VA when the eyes were open-the prevalence of microsaccades. Notably, the prevalence of microsaccades decreased in response to sleep deprivation and time-on-task. In addition, a novel algorithm for detecting multilevel VA was developed, which estimated performance on the PVT by integrating the novel marker with other eye-related indices. The novel algorithm also tracked changes in intermediate level of VA (specific reaction times in the PVT, i.e. 300-500 ms) during prolonged time-on-task and sleep deprivation, which had not been tracked previously by conventional techniques. The implication of the findings is that this novel algorithm, named "eye-metrical estimation version of the PVT: PVT-E," can be used to reduce human-error-related accidents caused by vigilance impairment even when its level is intermediate. © Sleep Research Society 2020. Published by Oxford University Press on behalf of the Sleep Research Society. All rights reserved. For permissions, please e-mail journals.permissions@oup.com.Entities:
Keywords: Bayesian inference; PERCLOS; biomarkers; blink; computerized analysis; eye movements; eyelid movements; fatigue; neurobehavioral performance; pupil; sleep deprivation
Mesh:
Year: 2020 PMID: 32040590 DOI: 10.1093/sleep/zsz219
Source DB: PubMed Journal: Sleep ISSN: 0161-8105 Impact factor: 5.849