Detecting deteriorated vigilance using percentage of eyelid closure time during behavioral maintenance of wakefulness tests

Risk assessment of driver performance in the oil and gas transportation industry: Analyzing the relationship between driver vigilance, attention, reaction time, and safe driving practices

The increasing use of road traffic for land transportation has resulted in numerous road accidents and casualties, including those involving oil and gas tanker vehicles. Despite this, little empirical research has been conducted on the factors influencing tanker drivers' performance. This study aims to address this knowledge gap, particularly in the energy transportation industry, by examining the driving performance factors that affect tanker drivers and incorporating risk assessment measures. The model variables were identified from the literature and used to develop a survey questionnaire for the study. A total of 307 surveys were collected from Malaysian oil and gas tanker drivers, and the driving performance factors were contextually adjusted using the Exploratory Factor Analysis (EFA) approach. The driving performance model was developed using partial least squares structural equation modeling (PLS-SEM). The EFA results categorized driving performance into two constructs: 1) drivers' reaction time with β = 0.320 and 2) attention and vigilance with β value = 0.749. The proposed model provided full insight into how drivers' reaction time, attention, and vigilance impact drivers' performance in this sector, which can help identify potential risks and prevent accidents. The findings are significant in understanding the factors that affect oil and gas drivers' performance and can aid in enhancing oil and gas transportation management by including effective risk assessment measures to prevent fatal crashes.

Prediction of drowsiness using EEG signals in young Indonesian drivers

Indonesia is among the countries with the highest accident rates in the world. Fatigue and drowsiness are among the main causes of the increased risks of accidents in the road transport sector. Sleep-related factors (quality and quantity, time of day) and work-related factors significantly affect the development of fatigue. The EEG signal indicator is often referred to as the gold standard for measuring fatigue and drowsiness. However, previous studies focused primarily on the trends of EEG signals under certain conditions but overlooking the development of drowsiness indicators based on EEG signals. Furthermore, existing studies still do not agree on what parameters in the EEG signal indicator are best at detecting drowsiness. Thus, this study aims to design an EEG signal-based drowsiness indicator under simulated driving conditions. Drowsy drivers were monitored through EEG signal indicators and subjective assessments. The methods used in this study include statistical significance tests, logistic regression, and support vector machine. The results showed that sleep deprivation had a significant effect on increasing alpha, beta, and theta waves. In addition, driving duration significantly increased the theta power and all EEG ratios and decreased the beta power in the alert group. The ratio of (θ + α)/β and θ/β in the SD group also showed a considerable increase in the end of driving. Furthermore, sleep status and driving duration both influenced subjective sleepiness. EEG signals combined with sleep status and driving duration factors generated acceptable model accuracies (77.1% and 90.2% in training and testing, respectively), with 90.5% sensitivity and 90% specificity in data test. Support vector machine showed better classification than that of logistics regression, with the linear kernel as the best classifier. Theta power had the highest effect in the model compared with other EEG signals.

Eyelid Closure Behavior of Patients with Idiopathic and Nonorganic Hypersomnia, Narcolepsy-Cataplexy, and Healthy Controls in the Maintenance of Wakefulness Test

Purpose

Differential diagnosis of central disorders of hypersomnolence remains challenging, particularly between idiopathic (IH) and nonorganic hypersomnia (NOH). We hypothesized that eyelid closure behavior in the maintenance of wakefulness test (MWT) could be a valuable biomarker.

Patients and Methods

MWT recordings of patients with IH, NOH, narcolepsy-cataplexy (NC), and healthy sleep-deprived controls (H) were retrospectively analyzed (15 individuals per group). For each MWT trial, visual scoring of face videography for partial (50-80%) and full eyelid closure (≥80%) was performed from "lights off" to the first microsleep episode (≥3 s).

Results

In all groups, the frequency and cumulative duration of periods with partial and full eyelid closure gradually increased toward the first microsleep episode. On the group level, significant differences occurred for the latency to the first microsleep episode (IH 21 min (18-33), NOH 23 min (17-35), NC 11 min (7-19), H 10 min (6-25); p = 0.009), the ratio between partial and full eyelid closure duration (IH 2.2 (0.9-3.1), NOH 0.5 (0-1.2), NC 2.8 (1.1-5), H 0.7 (0.4-3.3); p = 0.004), and the difference between full and partial eyelid closure duration in the five minutes prior to the first microsleep episode (∆full - partial eyelid closure duration: IH -16 s (-35 to 28); NOH 46 s (9-82); NC -6 s (-26 to 5); H 10 s (-4 to 18); p = 0.007). IH and NOH significantly differed comparing the ratio between partial and full eyelid closure (p = 0.005) and the difference between ∆full - partial eyelid closure duration in the five minutes prior to the first microsleep episode (p = 0.006).

Conclusion

In the MWT, eyelid closure behavior (∆full - partial) in the period prior to the first microsleep episode could be of value for discriminating NOH from other etiologies of excessive daytime sleepiness, particularly IH.

Non-intrusive Drowsiness Detection Techniques and Their Application in Detecting Early Dementia in Older Drivers

Drowsy drivers cause the most car accidents thus, adopting an efficient drowsiness detection system can alert the driver promptly and precisely which will reduce the numbers of accidents and also save a lot of money. This paper discusses many tactics and methods for drowsy driving warning. The non-intrusive nature of most of the strategies mentioned and contrasted means both vehicular and behavioural techniques are examined here. Thus, the latest strategies are studied and discussed for both groups, together with their benefits and drawbacks. The goal of this review was to identify a practical and low-cost approach for analysing elder drivers' behaviour.

Predicting and mitigating fatigue effects due to sleep deprivation: A review

The deleterious effects of insufficient sleep have been well-established in the literature and can lead to a wide range of adverse health outcomes. Some of the most replicated findings demonstrate significant declines in cognitive functions such as vigilance and executive attention, psychomotor and cognitive speed, and working memory. Consequently, these decrements often lead individuals who are in a fatigued state to engage in substandard performance on everyday tasks. In the interest of curtailing these effects, prior work has attempted to identify mechanisms that predict fatigue onset and develop techniques to mitigate its negative consequences. Nonetheless, these results are often confounded by variables such as an individual’s resistance to fatigue, sleep history, and unclear distinctions about whether certain performance decrements are present due to fatigue or due to other confounding factors. Similar areas of research have provided approaches to produce models for the prediction of cognitive performance decrements due to fatigue through the use of multi-modal recording and analysis of fatigue-related responses. Namely, gathering and combining response information from multiple sources (i.e., physiological and behavioral) at multiple timescales may provide a more comprehensive representation of what constitutes fatigue onset in the individual. Therefore, the purpose of this review is to discuss the relevant literature on the topic of fatigue-related performance effects with a special emphasis on a variety of physiological and behavioral response variables that have shown to be sensitive to changes in fatigue. Furthermore, an increasing reliance on sleep loss, meant to assist in meeting the demands of modern society, has led to an upsurge in the relevance of identifying dependable countermeasures for fatigued states. As such, we will also review methods for the mitigation of performance effects due to fatigue and discuss their usefulness in regulating these effects. In sum, this review aims to inspire future work that will create opportunities to detect fatigue and mitigate its effects prior to the onset of cognitive impairments.

Driver Fatigue Detection Based On Facial Feature Analysis

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

I think I'm sleepy, therefore I am - Awareness of sleepiness while driving: A systematic review

Driver drowsiness contributes to 10-20% of motor vehicle crashes. To reduce crash risk, ideally drivers would be aware of the drowsy state and cease driving. The extent to which drivers can accurately identify sleepiness remains under much debate. We systematically examined whether individuals are aware of sleepiness while driving, and whether this accurately reflects driving impairment, using meta-analyses and narrative review. Within this scope, there is high variability in measures of subjective sleepiness, driving performance and physiologically-derived drowsiness, and statistical analyses. Thirty-four simulated/naturalistic driving studies were reviewed. To summarise, drivers were aware of sleepiness, and this was associated to physiological drowsiness and driving impairment, such that high levels of sleepiness significantly predicted crash events and lane deviations. Subjective sleepiness was more strongly correlated (i) with physiological drowsiness compared to driving outcomes; (ii) under simulated driving conditions compared to naturalistic drives; and (iii) when examined using the Karolinska sleepiness scale (KSS) compared to other measures. Gaps remain in relation to how age, sex, and varying degrees of sleep loss may influence this association. This review provides evidence that drivers are aware of drowsiness while driving, and stopping driving when feeling 'sleepy' may significantly reduce crash risk.

Respiratory, cardiac, EEG, BOLD signals and functional connectivity over multiple microsleep episodes

Falling asleep is common in fMRI studies. By using long eyelid closures to detect microsleep onset, we showed that the onset and termination of short sleep episodes invokes a systematic sequence of BOLD signal changes that are large, widespread, and consistent across different microsleep durations. The signal changes are intimately intertwined with shifts in respiration and heart rate, indicating that autonomic contributions are integral to the brain physiology evaluated using fMRI and cannot be simply treated as nuisance signals. Additionally, resting state functional connectivity (RSFC) was altered in accord with the frequency of falling asleep and in a manner that global signal regression does not eliminate. Our findings point to the need to develop a consensus among neuroscientists using fMRI on how to deal with microsleep intrusions. SIGNIFICANCE STATEMENT: Sleep, breathing and cardiac action are influenced by common brainstem nuclei. We show that falling asleep and awakening are associated with a sequence of BOLD signal changes that are large, widespread and consistent across varied durations of sleep onset and awakening. These signal changes follow closely those associated with deceleration and acceleration of respiration and heart rate, calling into question the separation of the latter signals as 'noise' when the frequency of falling asleep, which is commonplace in RSFC studies, correlates with the extent of RSFC perturbation. Autonomic and central nervous system contributions to BOLD signal have to be jointly considered when interpreting fMRI and RSFC studies.

Tracking intermediate performance of vigilant attention using multiple eye metrics

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.

Spontaneous, Voluntary, and Reflex Blinking in Clinical Practice

Blinking is one of the motor acts performed more frequently by healthy human subjects. It involves the reciprocal action of at least two muscles: the orbicularis oculi shows a brief phasic activation while the levator palpebrae shows transient inhibition. In clinical practice, noninvasive recording of the orbicularis oculi activity is sufficient to obtain useful information for electrodiagnostic testing. Blinking can be spontaneous, voluntary, or reflex. Although the analysis of spontaneous blinks can already furnish interesting data, most studies are based on reflex blinking. This article is a review of some of the alterations that can be observed in blinking, focusing in four patterns of abnormality that can be distinguished in the blink reflex: (1) afferent versus efferent, which allows characterization of trigeminal or facial lesions; (2) peripheral versus central, which distinguishes alterations in nerve conduction from those involving synaptic delay; (3) upper versus lower brainstem lesions, which indicates the lesions involving specific circuits for trigeminal and somatosensory blink reflexes; and (4) asymmetric abnormal excitability pattern, which shows a unilateral alteration in the descending control of excitability in brainstem circuits. The blink reflex excitability recovery curve to paired stimuli may provide information about other modulatory inputs to trigemino-facial circuits, such as those proposed for the connection between basal ganglia and trigeminal neurons. Finally, prepulse inhibition of blink reflex reflects the motor surrogate of subcortical gating on sensory volleys, which is still another window by which electrodiagnosis can document motor control mechanisms and their abnormalities in neurologic diseases.

Technology to Detect Driver Sleepiness

This article reports on sleepiness, drowsiness, tiredness, and fatigue. An assessment of sleepiness can be done with electroencephalograms, electrooculograms, and electromyograms in validated tests, such as the multiple sleep latency test and the maintenance of wakefulness test. These 2 tests serve as references for quantitative assessment of daytime sleepiness and drowsiness. Correlates for sleepiness, such as reaction time tests, can be used but are less reliable. Questionnaires are self-administered and popular measures for perceived sleepiness. Driver drowsiness assessment is an important part of sleep laboratory testing, because European Union regulations require assessments due to risk of accidents in patients with sleep disorders.

Effects of Sleep Deprivation on Surgeons Dexterity

Sleep deprivation is an ordinary aspect in the global society and its prevalence is increasing. Chronic and acute sleep deprivation have been linked to diabetes and heart diseases as well as depression and enhanced impulsive behaviors. Surgeons are often exposed to long hour on call and few hours of sleep in the previous days. Nevertheless, few studies have focused their attention on the effects of sleep deprivation on surgeons and more specifically on the effects of sleep deprivation on surgical dexterity, often relying on virtual surgical simulators. A better understanding of the consequences of sleep loss on the key surgical skill of dexterity can shed light on the possible risks associated to a sleepy surgeon. In this paper, the authors aim to provide a comprehensive review of the relationship between sleep deprivation and surgical dexterity.

Task-related hemodynamic responses are modulated by reward and task engagement

Hemodynamic recordings from visual cortex contain powerful endogenous task-related responses that may reflect task-related arousal, or "task engagement" distinct from attention. We tested this hypothesis with hemodynamic measurements (intrinsic-signal optical imaging) from monkey primary visual cortex (V1) while the animals' engagement in a periodic fixation task over several hours was varied through reward size and as animals took breaks. With higher rewards, animals appeared more task-engaged; task-related responses were more temporally precise at the task period (approximately 10-20 seconds) and modestly stronger. The 2-5 minute blocks of high-reward trials led to ramp-like decreases in mean local blood volume; these reversed with ramp-like increases during low reward. The blood volume increased even more sharply when the animal shut his eyes and disengaged completely from the task (5-10 minutes). We propose a mechanism that controls vascular tone, likely along with local neural responses in a manner that reflects task engagement over the full range of timescales tested.

Electrophysiological signatures of the resting-state fMRI global signal: A simultaneous EEG-fMRI study

BACKGROUND

The global signal of resting-state functional magnetic resonance imaging (fMRI) constitutes an intrinsic fluctuation and presents an opportunity to characterize and understand the activity of the whole brain. Recently, evidence that the global signal contains neurophysiologic information has been growing, but the global signal of electroencephalography (EEG) has never been determined.

NEW METHODS

We developed a new method to obtain the EEG global signal. The EEG global signal was reconstructed by the reference electrode standardization technique and represented the outer cortical electrophysiological activity. To investigate its relationship with the global signal of resting-state fMRI, a simultaneous EEG-fMRI signal was recorded, and this was analyzed in 24 subjects.

RESULTS

We found that the global signal of resting-state fMRI showed a positive correlation with power fluctuations of the EEG global signal in the γ band (30-45 Hz) and a negative correlation in the low-frequency band (4-20 Hz).

COMPARISON WITH EXISTING METHOD(S)

Compared with the global signal of fMRI, the global signal of EEG provides more temporal information about outer cortical neural activity.

CONCLUSIONS

These results provide new evidence for the electrophysiology information of the global signal of resting-state fMRI. More importantly, due to its high correlation with the fMRI global signal, the EEG global signal may serve as a new biomarker for neurological disorders.

Experimental study on visual detection for fatigue of fixed-position staff

Fatigue can lead to decreased work performance and poorer safety and health condition. Fatigue is ubiquitous in production and in life, while the research on it is mainly concentrated in the automotive driving, aircraft piloting and other fields, and it is insufficient to study on the fatigue of fixed-position staff. This paper puts forward a non-contact visual image method, which can monitor the extent of fatigue of fixed-position staff. Fatigue threshold used in judgment is obtained by processing the recorded data of visual images of the experimental subjects when fatiguing and by analyzing eye closure time, percentage of eyelid closure (PERCLOS) value, frequency and number of blinks. The results show that there is significant difference among the four indicators before and after experiment subjects undergo fatigue. The fatigue of experimental subjects is obvious when eye closure time is 3.5 s/min, PERCLOS value 6%, and blink frequency 0.4 times/s. This provides a reference for a wider range of detection of fatigue and a method for avoiding mistakes and accidents.

Sweat pore reactivity as a surrogate measure of sympathetic nervous system activity in trauma-exposed individuals with and without posttraumatic stress disorder

Stress analysis by FLIR (forward-looking infrared) evaluation (SAFE) has been demonstrated to monitor sweat pore activation (SPA) as a novel surrogate measure of sympathetic nervous system (SNS) activity in a normal population. SNS responses to a series of 15 1-s, 82 dB, white noise bursts were measured by skin conductance (SC) and SAFE monitoring of SPA on the fingers (FiP) and face (FaP) in 10 participants with posttraumatic stress disorder (PTSD) and 16 trauma-exposed participants without PTSD (Mage  = 48.92 ± 12.00 years; 26.9% female). Within participants, SC and FiP responses across trials were strongly correlated (r = .92, p < .001). Correlations between SC and FaP (r = .76, p = .001) and between FiP and FaP (r = .47, p = .005) were smaller. The habituation of SNS responses across the 15 trials was substantial (SC: d = -2.97; FiP: d = -2.34; FaP: d = -1.02). There was a strong correlation between habituation effects for SC and FiP (r = .76, p < .001), but not for SC and FaP (r = .15, p = .45) or FiP and FaP (r = .29, p = .16). Participants with PTSD showed larger SNS responses to the first loud noise than those without PTSD. PTSD reexperiencing symptoms assessed by the PTSD Checklist on the day of testing were associated with the SNS responses to the first loud noise measured by SC (d = 1.19) and FiP (d = .99), but not FaP (d = .10). This study confirms convergence of SAFE and SC as valid measures of SNS activity. SAFE FiP and SC responses were highly predictive of self-rated PTSD reexperiencing symptoms. SAFE may offer an attractive alternative for applications in PTSD and similar populations.

Tracking brain arousal fluctuations with fMRI

Changes in brain activity accompanying shifts in vigilance and arousal can interfere with the study of other intrinsic and task-evoked characteristics of brain function. However, the difficulty of tracking and modeling the arousal state during functional MRI (fMRI) typically precludes the assessment of arousal-dependent influences on fMRI signals. Here we combine fMRI, electrophysiology, and the monitoring of eyelid behavior to demonstrate an approach for tracking continuous variations in arousal level from fMRI data. We first characterize the spatial distribution of fMRI signal fluctuations that track a measure of behavioral arousal; taking this pattern as a template, and using the local field potential as a simultaneous and independent measure of cortical activity, we observe that the time-varying expression level of this template in fMRI data provides a close approximation of electrophysiological arousal. We discuss the potential benefit of these findings for increasing the sensitivity of fMRI as a cognitive and clinical biomarker.

Eye blinking-based method for detecting driver drowsiness

Drowsy driving is a major cause of traffic accidents. Eye blinking is considered as important evidence of driver drowsiness. In this paper, a portable and low cost device for monitoring a driver's drowsiness is proposed. The proposed system consists of two main parts that detect eye blinking based on IR sensors mounted on eyewear. Depending on the reflected and absorbed IR radiation, this system detects and classifies the eye blinking into normal blinking (NB) or prolonged blinking (PB). The detected prolonged blinking is used to trigger an audio/visual alarm system which draws the driver's attention back. The system was simulated initially by LabVIEW® software. Moreover, the system was bench tested on 15 adult volunteers; eye blinking were detected and classified successfully for all subjects. The results of this research are promising and additional investigation is required to further improve the method.

Temporal dynamics of ocular indicators of sleepiness across sleep restriction

The current study characterized the temporal dynamics of ocular indicators of sleepiness during extended sleep restriction. Ten male participants (mean age ± SD = 23.3 ± 1.6 years) underwent 40 h of continuous wakefulness under constant routine (CR) conditions; they completed the Karolinska Sleepiness Scale (KSS) and a 10-min auditory psychomotor vigilance task (aPVT) hourly. Waking electroencephalography (EEG) and ocular measures were recorded continuously throughout the CR. Infrared-reflectance oculography was used to collect the ocular measures positive and negative amplitude-velocity ratio, mean blink duration, the percentage of eye closure, and a composite score of sleepiness levels (Johns Drowsiness Scale). All ocular measures, except blink duration, displayed homeostatic and circadian properties. Only circadian effects were detected in blink duration. Significant, phase-locked cross-correlations (p < 0.05) were detected between ocular measures and aPVT reaction time (RT), aPVT lapses, KSS, and EEG delta-theta (0.5-5.5 Hz), theta-alpha (5.0-9.0 Hz), and beta (13.0-20.0 Hz) activity. Receiver operating characteristic curve analysis demonstrated reasonable sensitivity and specificity of ocular measures in correctly classifying aPVT lapses above individual baseline thresholds (initial 16 h of wakefulness). Under conditions of sleep restriction, ocular indicators of sleepiness paralleled performance impairment and self-rated sleepiness levels, and demonstrated their potential to detect sleepiness-related attentional lapses. These findings, if reproduced in a larger sample, will have implications for the use of ocular-based sleepiness-warning systems in operational settings.

Sleepiness and Safety: Where Biology Needs Technology

Maintaining human alertness and behavioral capability under conditions of sleep loss and circadian misalignment requires fatigue management technologies due to: (1) dynamic nonlinear modulation of performance capability by the interaction of sleep homeostatic drive and circadian regulation; (2) large differences among people in neurobehavioral vulnerability to sleep loss; (3) error in subjective estimates of fatigue on performance; and (4) to inform people of the need for recovery sleep. Two promising areas of technology have emerged for managing fatigue risk in safety-sensitive occupations. The first involves preventing fatigue by optimizing work schedules using biomathematical models of performance changes associated with sleep homeostatic and circadian dynamics. Increasingly these mathematical models account for individual differences to achieve a more accurate estimate of the timing and magnitude of fatigue effects on individuals. The second area involves technologies for detecting transient fatigue from drowsiness. The Psychomotor Vigilance Test (PVT), which has been extensively validated to be sensitive to deficits in attention from sleep loss and circadian misalignment, is an example in this category. Two shorter-duration versions of the PVT recently have been developed for evaluating whether operators have sufficient behavioral alertness prior to or during work. Another example is online tracking the percent of slow eyelid closures (PERCLOS), which has been shown to reflect momentary fluctuations of vigilance. Technologies for predicting and detecting sleepiness/fatigue have the potential to predict and prevent operator errors and accidents in safety-sensitive occupations, as well as physiological and mental diseases due to inadequate sleep and circadian misalignment.

Assessment of drowsiness based on ocular parameters detected by infrared reflectance oculography

STUDY OBJECTIVES

Numerous ocular parameters have been proposed as reliable physiological markers of drowsiness. A device that measures many of these parameters and then combines them into a single metric (the Johns Drowsiness Scale [JDS]) is being used commercially to assess drowsiness in professional drivers. Here, we examine how these parameters reflect changes in drowsiness, and how they relate to objective and subjective indices of the drowsy state in a controlled laboratory setting.

DESIGN

A within subject prospective study.

PARTICIPANTS

29 healthy adults (18 males; mean age 23.3 ± 4.6 years; range 18-34 years).

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

Over the course of a 30-h extended wake vigil under constant routine (CR) conditions, participants were monitored using infrared reflectance oculography (Optalert) and completed bi-hourly neurobehavioral tests, including the Karolinska Sleepiness Scale (KSS) and Psychomotor Vigilance Task (PVT). Ocular-defined increases in drowsiness were evident with extended time awake and during the biological night for all ocular parameters; JDS being the most sensitive marker of drowsiness induced by sleep regulatory processes (p < 0.0001). In addition, the associations between JDS in the preceding 10-min period and subsequent PVT lapses and KSS were stronger (AUC 0.74/0.80, respectively) than any other ocular metric, such that PVT lapses, mean response time (RT), and KSS increased in a dose-response manner as a function of prior JDS score (p < 0.0001).

CONCLUSIONS

Ocular parameters captured by infrared reflectance oculography detected fluctuations in drowsiness due to time awake and during the biological night. The JDS outcome was the strongest predictor of drowsiness among those tested, and showed a clear association to objective and subjective measures of drowsiness. Our findings indicate this real-time objective drowsiness monitoring system is an effective tool for monitoring changes in alertness and performance along the alert-drowsy continuum in a controlled laboratory setting.

Neurophysiological and behavioral responses to music therapy in vegetative and minimally conscious States

Assessment of awareness for those with disorders of consciousness is a challenging undertaking, due to the complex presentation of the population. Debate surrounds whether behavioral assessments provide greatest accuracy in diagnosis compared to neuro-imaging methods, and despite developments in both, misdiagnosis rates remain high. Music therapy may be effective in the assessment and rehabilitation with this population due to effects of musical stimuli on arousal, attention, and emotion, irrespective of verbal or motor deficits. However, an evidence base is lacking as to which procedures are most effective. To address this, a neurophysiological and behavioral study was undertaken comparing electroencephalogram (EEG), heart rate variability, respiration, and behavioral responses of 20 healthy subjects with 21 individuals in vegetative or minimally conscious states (VS or MCS). Subjects were presented with live preferred music and improvised music entrained to respiration (procedures typically used in music therapy), recordings of disliked music, white noise, and silence. ANOVA tests indicated a range of significant responses (p ≤ 0.05) across healthy subjects corresponding to arousal and attention in response to preferred music including concurrent increases in respiration rate with globally enhanced EEG power spectra responses (p = 0.05-0.0001) across frequency bandwidths. Whilst physiological responses were heterogeneous across patient cohorts, significant post hoc EEG amplitude increases for stimuli associated with preferred music were found for frontal midline theta in six VS and four MCS subjects, and frontal alpha in three VS and four MCS subjects (p = 0.05-0.0001). Furthermore, behavioral data showed a significantly increased blink rate for preferred music (p = 0.029) within the VS cohort. Two VS cases are presented with concurrent changes (p ≤ 0.05) across measures indicative of discriminatory responses to both music therapy procedures. A third MCS case study is presented highlighting how more sensitive selective attention may distinguish MCS from VS. The findings suggest that further investigation is warranted to explore the use of music therapy for prognostic indicators, and its potential to support neuroplasticity in rehabilitation programs.

Now you hear me, now you don't: eyelid closures as an indicator of auditory task disengagement

STUDY OBJECTIVES

Eyelid closures in fatigued individuals signify task disengagement in attention-demanding visual tasks. Here, we studied how varying degrees of eyelid closure predict responses to auditory stimuli depending on whether a participant is well rested or sleep deprived. We also examined time-on-task effects and how more and less vulnerable individuals differed in frequency of eye closures and lapses.

DESIGN

Six repetitions of an auditory vigilance task were performed in each of two sessions: rested wakefulness (RW) and total sleep deprivation (TSD) (order counterbalanced).

SETTING

Sleep laboratory.

PARTICIPANTS

Nineteen healthy young adults (mean age: 22 ± 2.8 y; 11 males).

INTERVENTION

Approximately 24 h of TSD.

MEASUREMENTS AND RESULTS

Eyelid closure was rated on a 9-point scale (1 = fully closed to 9 = fully opened) using video segments time-locked to the auditory event. Eyes-open trials predominated during RW, but different degrees of eye closure were uniformly distributed during TSD. The frequency of lapses (response time > 800 ms or nonresponses) to auditory stimuli increased dramatically with greater degrees of eye closure, but the association was strong only during TSD. There were significant within-run time-on-task effects on eye closure and auditory lapses that were exacerbated by TSD. Participants who had more auditory lapses during TSD (more vulnerable) had greater variability in their eyelid closures.

CONCLUSIONS

Eyelid closures are a strong predictor of auditory task disengagement in the sleep-deprived state but are less relevant during rested wakefulness. Individuals relatively more impaired in this auditory vigilance task during total sleep deprivation display oculomotor evidence for greater state instability.

Sleep deprivation and neurobehavioral dynamics

Lifestyles involving sleep deprivation are common, despite mounting evidence that both acute total sleep deprivation and chronically restricted sleep degrade neurobehavioral functions associated with arousal, attention, memory and state stability. Current research suggests dynamic differences in the way the central nervous system responds to acute versus chronic sleep restriction, which is reflected in new models of sleep-wake regulation. Chronic sleep restriction likely induces long-term neuromodulatory changes in brain physiology that could explain why recovery from it may require more time than from acute sleep loss. High intraclass correlations in neurobehavioral responses to sleep loss suggest that these trait-like differences are phenotypic and may include genetic components. Sleep deprivation induces changes in brain metabolism and neural activation that involve distributed networks and connectivity.

Optical flow and driver's kinematics analysis for state of alert sensing

Road accident statistics from different countries show that a significant number of accidents occur due to driver's fatigue and lack of awareness to traffic conditions. In particular, about 60% of the accidents in which long haul truck and bus drivers are involved are attributed to drowsiness and fatigue. It is thus fundamental to improve non-invasive systems for sensing a driver's state of alert. One of the main challenges to correctly resolve the state of alert is measuring the percentage of eyelid closure over time (PERCLOS), despite the driver's head and body movements. In this paper, we propose a technique that involves optical flow and driver's kinematics analysis to improve the robustness of the driver's alert state measurement under pose changes using a single camera with near-infrared illumination. The proposed approach infers and keeps track of the driver's pose in 3D space in order to ensure that eyes can be located correctly, even after periods of partial occlusion, for example, when the driver stares away from the camera. Our experiments show the effectiveness of the approach with a correct eyes detection rate of 99.41%, on average. The results obtained with the proposed approach in an experiment involving fifteen persons under different levels of sleep deprivation also confirm the discriminability of the fatigue levels. In addition to the measurement of fatigue and drowsiness, the pose tracking capability of the proposed approach has potential applications in distraction assessment and alerting of machine operators.

Mars 520-d mission simulation reveals protracted crew hypokinesis and alterations of sleep duration and timing

The success of interplanetary human spaceflight will depend on many factors, including the behavioral activity levels, sleep, and circadian timing of crews exposed to prolonged microgravity and confinement. To address the effects of the latter, we used a high-fidelity ground simulation of a Mars mission to objectively track sleep-wake dynamics in a multinational crew of six during 520 d of confined isolation. Measurements included continuous recordings of wrist actigraphy and light exposure (4.396 million min) and weekly computer-based neurobehavioral assessments (n = 888) to identify changes in the crew's activity levels, sleep quantity and quality, sleep-wake periodicity, vigilance performance, and workload throughout the record-long 17 mo of mission confinement. Actigraphy revealed that crew sedentariness increased across the mission as evident in decreased waking movement (i.e., hypokinesis) and increased sleep and rest times. Light exposure decreased during the mission. The majority of crewmembers also experienced one or more disturbances of sleep quality, vigilance deficits, or altered sleep-wake periodicity and timing, suggesting inadequate circadian entrainment. The results point to the need to identify markers of differential vulnerability to hypokinesis and sleep-wake changes during the prolonged isolation of exploration spaceflight and the need to ensure maintenance of circadian entrainment, sleep quantity and quality, and optimal activity levels during exploration missions. Therefore, successful adaptation to such missions will require crew to transit in spacecraft and live in surface habitats that instantiate aspects of Earth's geophysical signals (appropriately timed light exposure, food intake, exercise) required for temporal organization and maintenance of human behavior.

Classifying performance impairment in response to sleep loss using pattern recognition algorithms on single session testing

There is currently no "gold standard" marker of cognitive performance impairment resulting from sleep loss. We utilized pattern recognition algorithms to determine which features of data collected under controlled laboratory conditions could most reliably identify cognitive performance impairment in response to sleep loss using data from only one testing session, such as would occur in the "real world" or field conditions. A training set for testing the pattern recognition algorithms was developed using objective Psychomotor Vigilance Task (PVT) and subjective Karolinska Sleepiness Scale (KSS) data collected from laboratory studies during which subjects were sleep deprived for 26-52h. The algorithm was then tested in data from both laboratory and field experiments. The pattern recognition algorithm was able to identify performance impairment with a single testing session in individuals studied under laboratory conditions using PVT, KSS, length of time awake and time of day information with sensitivity and specificity as high as 82%. When this algorithm was tested on data collected under real-world conditions from individuals whose data were not in the training set, accuracy of predictions for individuals categorized with low performance impairment were as high as 98%. Predictions for medium and severe performance impairment were less accurate. We conclude that pattern recognition algorithms may be a promising method for identifying performance impairment in individuals using only current information about the individual's behavior. Single testing features (e.g., number of PVT lapses) with high correlation with performance impairment in the laboratory setting may not be the best indicators of performance impairment under real-world conditions. Pattern recognition algorithms should be further tested for their ability to be used in conjunction with other assessments of sleepiness in real-world conditions to quantify performance impairment in response to sleep loss.

Detecting driver drowsiness based on sensors: a review

In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.