Comparative analysis of electroencephalographic tests of alpha activity attenuation in evaluation of involuntary falling asleep in healthy adults

Introduction. Decrease of daily alertness is a common cause of accidents in the work place, especially traffic accidents. Therefore, an increasing interest exists to determine reliable indicators of a tendency to fall asleep involuntarily. Objective. To determine an optimal electroencephalographic (EEG) indicator of an involuntary tendency to fall asleep, we performed a study on neurologically healthy subjects, after one night of sleep deprivation. Total sleep deprivation was aimed at increasing daily sleepiness in healthy subjects, providing us with an opportunity to test different methods of evaluation. Methods. We applied a visual analogue scale for sleepiness (VASS), EEG registration with the specific test of alpha activity attenuation (TAA) in 87 healthy subjects. The test was performed in a standard way (sTAA) as well as in accordance with new modifications related to changes of EEG filter width in the range from 5 to 32 Hz (mTAA). Results. After sleep deprivation, we observed involuntary falling asleep in 54 subjects. The comparison of VASS results showed no differences, contrary to a more objective TAA. Between two variants of TAA, the modified test provided us with a better prediction for subjects who would fall asleep involuntarily. Conclusion. The application of a more objective EEG test in evaluation of daily alertness represents the optimal method of testing. Modified TAA attracts special attention, offering a simple solution for reliable testing of decreased daily alertness in medical services related to professional aircraft personnel.

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Analysis of Sleep Deprivation Effect to Driving Performance Using ReactionTest Simulation

Jurnal Teknologi ◽

10.11113/jt.v72.3915 ◽

2015 ◽

Vol 72 (4) ◽

Author(s):

Ika Nurlaili Isnainiyah ◽

Febriliyan Samopa ◽

Hatma Suryotrisongko ◽

Edwin Riksakomara

Keyword(s):

Sleep Deprivation ◽

Traffic Accidents ◽

Driving Simulator ◽

Driving Performance ◽

Deprivation Condition ◽

Falling Asleep ◽

Sleep Condition ◽

Lack Of Sleep ◽

The Difference ◽

Social Habits

Sleep deprivation condition might lead to falling asleep through inappropriate situations, such as driving. Driving in a state of fatigue or drowsy from lack of sleep will be far worse than driving after alcohol consumption. Hence, the authors develop a driving simulator using Java to modify the control and rules of OpenDS application in order to simulate and calculate the automatic ReactionTest for 25 respondents simulating in both normal conditions and sleepy conditions when driving. Through this study, the authors obtained that the difference of driving performance in terms of reaction rate when driving the car in sleep deprivation condition and the normal condition, is equal to 1.08 seconds. The results also shown that the risk of loss of control that can occur to the driver of the car in units of meters (m), is equal to 0.3024 x the car’s speed. This study aims to reduce the number of traffic accidents caused by sleep deprivation that occur in society by giving a recommendation to the driver that forced to drive in lack of sleep condition. In top of that, the authors propose to create an understanding for changing the social habits of driving toward a better way.

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Influence of sleep deprivation on plasma ghrelin levels in healthy subjects

Experimental and Clinical Endocrinology & Diabetes ◽

10.1055/s-2003-817579 ◽

2003 ◽

Vol 111 (06) ◽

Cited By ~ 1

Author(s):

A Dzaja ◽

A Schuld ◽

M Uhr ◽

A Yassouridis ◽

T Pollmächer

Keyword(s):

Sleep Deprivation ◽

Healthy Subjects

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α‐tACS over the somatosensory cortex enhances tactile spatial discrimination in healthy subjects with low alpha activity

Brain and Behavior ◽

10.1002/brb3.2019 ◽

2021 ◽

Author(s):

Kei Saito ◽

Naofumi Otsuru ◽

Hirotake Yokota ◽

Yasuto Inukai ◽

Shota Miyaguchi ◽

...

Keyword(s):

Somatosensory Cortex ◽

Healthy Subjects ◽

Alpha Activity ◽

Spatial Discrimination

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Role of falling asleep and awakening in the appearance of paroxysms in the sleep-EEG after sleep deprivation

Electroencephalography and Clinical Neurophysiology ◽

10.1016/0013-4694(85)90778-3 ◽

1985 ◽

Vol 61 (3) ◽

pp. S204

Author(s):

D. Klingler ◽

H. Tragner ◽

D. Deisenhammer

Keyword(s):

Sleep Deprivation ◽

Sleep Eeg ◽

Falling Asleep

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Tracking intermediate performance of vigilant attention using multiple eye metrics

SLEEP ◽

10.1093/sleep/zsz219 ◽

2020 ◽

Vol 43 (3) ◽

Cited By ~ 2

Author(s):

Takashi Abe ◽

Kazuo Mishima ◽

Shingo Kitamura ◽

Akiko Hida ◽

Yuichi Inoue ◽

...

Keyword(s):

Sleep Deprivation ◽

Human Error ◽

Reaction Times ◽

Simultaneous Recording ◽

Time On Task ◽

The Novel ◽

Eyelid Closure ◽

Psychomotor Vigilance Test ◽

Falling Asleep ◽

Novel Algorithm

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.

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