Assessment of drowsiness based on ocular parameters detected by infrared reflectance oculography.
ABSTRACT: 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.A within subject prospective study.29 healthy adults (18 males; mean age 23.3 ± 4.6 years; range 18-34 years).N/A.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).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.
Project description: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.
Project description:The study examined the relationship between the circadian rhythm of 6-sulphatoxymelatonin (aMT6s) and ocular measures of sleepiness and neurobehavioral performance in shift workers undergoing a simulated night shift.Twenty-two shift workers (mean age 33.4, SD 11.8 years) were tested at approximately the beginning (20:00) and the end (05:55) of a simulated night shift in the laboratory. At the time point corresponding to the end of the simulated shift, 14 participants were classified as being within range of 6-sulphatoxymelatonin (aMT6s) acrophase--defined as 3 hours before or after aMT6s peak--and 8 were classified as outside aMT6s acrophase range. Participants completed the Karolinska Sleepiness Scale (KSS) and the auditory psychomotor vigilance task (aPVT). Waking electroencephalography (EEG) was recorded and infrared reflectance oculography was used to collect ocular measures of sleepiness: positive and negative amplitude/velocity ratio (PosAVR, NegAVR), mean blink total duration (BTD), the percentage of eye closure (%TEC), and a composite score of sleepiness levels (Johns Drowsiness Scale; JDS).Participants who were tested within aMT6s acrophase range displayed higher levels of sleepiness on ocular measures (%TEC, BTD, PosAVR, JDS), objective sleepiness (EEG delta power frequency band), subjective ratings of sleepiness, and neurobehavioral performance, compared to those who were outside aMT6s acrophase range.The study demonstrated that objective ocular measures of sleepiness are sensitive to circadian rhythm misalignment in shift workers.
Project description:STUDY OBJECTIVES: To assess whether changes in psychomotor vigilance during sleep deprivation can be estimated using heart rate variability (HRV). DESIGN: HRV, ocular, and electroencephalogram (EEG) measures were compared for their ability to predict lapses on the Psychomotor Vigilance Task (PVT). SETTING: Chronobiology and Sleep Laboratory, Duke-NUS Graduate Medical School Singapore. PARTICIPANTS: Twenty-four healthy Chinese men (mean age ± SD = 25.9 ± 2.8 years). INTERVENTIONS: Subjects were kept awake continuously for 40 hours under constant environmental conditions. Every 2 hours, subjects completed a 10-minute PVT to assess their ability to sustain visual attention. MEASUREMENTS AND RESULTS: During each PVT, we examined the electrocardiogram (ECG), EEG, and percentage of time that the eyes were closed (PERCLOS). Similar to EEG power density and PERCLOS measures, the time course of ECG RR-interval power density in the 0.02-0.08-Hz range correlated with the 40-hour profile of PVT lapses. Based on receiver operating characteristic curves, RR-interval power density performed as well as EEG power density at identifying a sleepiness-related increase in PVT lapses above threshold. RR-interval power density (0.02-0.08 Hz) also classified subject performance with sensitivity and specificity similar to that of PERCLOS. CONCLUSIONS: The ECG carries information about a person's vigilance state. Hence, HRV measures could potentially be used to predict when an individual is at increased risk of attentional failure. Our results suggest that HRV monitoring, either alone or in combination with other physiologic measures, could be incorporated into safety devices to warn drowsy operators when their performance is impaired.
Project description: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.
Project description:Drowsiness is an awake state with increased sleep drive, yet the neural correlates and underlying mechanisms remains unclear. Here, we established a mouse model of drowsiness, where mice are fasted for 1 day and then allowed to overeat high-fat food (to promote sleep) while positioned in an open-field box (to promote vigilance). They fall into a long-lasting drowsy state, as reflected by repeated and open-eyed nodding of the head while in a standing position. Simultaneous recording of electroencephalogram (EEG) and neck electromyogram (EMG) readouts revealed that this drowsy state including nodding state had multiple stages in terms of the relationship between the level of vigilance and head movement: delta oscillations decreased in power prior to the head-nodding period and increased during the non-nodding period. Cav3.1-knockout mice, which have reduced delta oscillations, showed frequent head nodding with reduced duration of nodding episodes compared to wild-type mice. This suggests that the balance of drive is tilted in favor of wakefulness, likely due to their previously proposed decrease in sleep-promoting functions. Our findings indicate that delta oscillations play a dominant role in controlling vigilance dynamics during sleep/wake competition and that our novel mouse model may be useful for studying drowsiness and related neurological disorders.
Project description:STUDY OBJECTIVES:Drowsiness leads to 20% of fatal road crashes, while inability to assess drowsiness has hampered drowsiness interventions. This study examined the accuracy of eye-blink parameters for detecting drowsiness related driving impairment in real time. METHODS:Twelve participants undertook two sessions of 2-hour track-driving in an instrumented vehicle following a normal night's sleep or 32 to 34 hours of extended wake in a randomized crossover design. Eye-blink parameters and lane excursion events were monitored continuously. RESULTS:Sleep deprivation increased the rates of out-of-lane driving events and early drive terminations. Episodes of prolonged eyelid closures, blink duration, the ratio of amplitude to velocity of eyelid closure, and John's Drowsiness Score (JDS, a composite score) were also increased following sleep deprivation. A time-on-task (drive duration) effect was evident for out-of-lane events rate and most eye-blink parameters after sleep deprivation. The JDS demonstrated the strongest association with the odds of out-of-lane events in the same minute, whereas measures of blink duration and prolonged eye closure were stronger indicators of risk for out-of-lane events over longer periods of 5 minutes and 15 minutes, respectively. Eye-blink parameters also achieved moderate accuracies (specificities from 70.12% to 84.15% at a sensitivity of 50%) for detecting out-of-lane events in the same minute, with stronger associations over longer timeframes of 5 minutes to 15 minutes. CONCLUSIONS:Eyelid closure parameters are useful tools for monitoring and predicting drowsiness-related driving impairment (out-of-lane events) that could be utilized for monitoring drowsiness and assessing the efficacy of drowsiness interventions. CLINICAL TRIAL REGISTRATION:This study is registered with the Australian New Zealand Clinical Trial Registry (ANCTR), http://www.anzctr.org.au/TrialSearch.aspx ACTRN12612000102875. CITATION:Shekari Soleimanloo S, Wilkinson VE, Cori JM,Westlake J, Stevens B, Downey LA, Shiferaw BA, Rajaratnam SMW, Howard ME. Eye-blink parameters detect on-road track-driving impairment following severe sleep deprivation. J Clin Sleep Med. 2019;15(9):1271-1284.
Project description:PURPOSE:Although shift work disorder (SWD) affects a major part of the shift working population, little is known about its manifestation in real life. This observational field study aimed to provide a detailed picture of sleep and alertness among shift workers with a questionnaire-based SWD, by comparing them to shift workers without SWD during work shifts and free time. METHODS:SWD was determined by a questionnaire. Questionnaires and 3-week field monitoring, including sleep diaries, actigraphy, the Karolinska Sleepiness Scale (KSS), EEG-based sleep recordings, and Psychomotor Vigilance Tasks (PVT), were used to study 22 SWD cases and 9 non-SWD workers. RESULTS:The SWD group had a shorter subjective total sleep time and greater sleep debt before morning shifts than the non-SWD group. Unlike the non-SWD group, the SWD group showed little compensatory sleep on days off. The SWD group had lower objective sleep efficiency and longer sleep latency on most days, and reported poorer relaxation at bedtime and sleep quality across all days than the non-SWD group. The SWD group's average KSS-sleepiness was higher than the non-SWD group's sleepiness at the beginning and end of morning shifts and at the end of night shifts. The SWD group also had more lapses in PVT at the beginning of night shifts than the non-SWD group. CONCLUSIONS:The results indicate that SWD is related to disturbed sleep and alertness in association with both morning and night shifts, and to less compensatory sleep on days off. SWD seems to particularly associate with the quality of sleep.
Project description:There are strong individual differences in performance during sleep deprivation. We assessed whether baseline features of Psychomotor Vigilance Test (PVT) performance can be used for classifying participants' relative attentional vulnerability to total sleep deprivation. In a laboratory, healthy adults (n?=?160, aged 18-30 years) completed a 10-min PVT every 2?h while being kept awake for ?24?hours. Participants were categorized as vulnerable (n?=?40), intermediate (n?=?80), or resilient (n?=?40) based on their number of PVT lapses during one night of sleep deprivation. For each baseline PVT (taken 4-14?h after wake-up time), a linear discriminant model with wrapper-based feature selection was used to classify participants' vulnerability to subsequent sleep deprivation. Across models, classification accuracy was about 70% (range 65-76%) using stratified 5-fold cross validation. The models provided about 78% sensitivity and 86% specificity for classifying resilient participants, and about 70% sensitivity and 89% specificity for classifying vulnerable participants. These results suggest features derived from a single 10-min PVT at baseline can provide substantial, but incomplete information about a person's relative attentional vulnerability to total sleep deprivation. In the long term, modeling approaches that incorporate baseline performance characteristics can potentially improve personalized predictions of attentional performance when sleep deprivation cannot be avoided.
Project description:There is a long-standing debate about the best way to characterize performance deficits on the psychomotor vigilance test (PVT), a widely used assay of cognitive impairment in human sleep deprivation studies. Here, we address this issue through the theoretical framework of the diffusion model and propose to express PVT performance in terms of signal-to-noise ratio (SNR).From the equations of the diffusion model for one-choice, reaction-time tasks, we derived an expression for a novel SNR metric for PVT performance. We also showed that LSNR-a commonly used log-transformation of SNR-can be reasonably well approximated by a linear function of the mean response speed, LSNRapx. We computed SNR, LSNR, LSNRapx, and number of lapses for 1284 PVT sessions collected from 99 healthy young adults who participated in laboratory studies with 38 hr of total sleep deprivation.All four PVT metrics captured the effects of time awake and time of day on cognitive performance during sleep deprivation. The LSNR had the best psychometric properties, including high sensitivity, high stability, high degree of normality, absence of floor and ceiling effects, and no bias in the meaning of change scores related to absolute baseline performance.The theoretical motivation of SNR and LSNR permits quantitative interpretation of PVT performance as an assay of the fidelity of information processing in cognition. Furthermore, with a conceptual and statistical meaning grounded in information theory and generalizable across scientific fields, LSNR in particular is a useful tool for systems-integrated fatigue risk management.
Project description:Neuroimaging studies of the Psychomotor Vigilance Task (PVT) have revealed brain regions involved in attention lapses in sleep-deprived and well-rested adults. Those studies have focused on individual brain regions, rather than integrated brain networks, and have overlooked adolescence, a period of ongoing brain development and endemic short sleep. This study used functional MRI (fMRI) and a contemporary analytic approach to assess time-resolved peri-stimulus response of key brain networks when adolescents complete the PVT, and test for differences across attentive versus inattentive periods and after short sleep versus well-rested states. Healthy 14-17-year-olds underwent a within-subjects randomized protocol including 5-night spans of extended versus short sleep. PVT was performed during fMRI the morning after each sleep condition. Event-related independent component analysis (eICA) identified coactivating functional networks and corresponding time courses. Analysis of salient time course characteristics tested the effects of sleep condition, lapses, and their interaction. Seven eICA networks were identified supporting attention, executive control, motor, visual, and default-mode functions. Attention lapses, after either sleep manipulation, were accompanied by broadly increased response magnitudes post-stimulus and delayed peak responses in some networks. Well-circumscribed networks respond during the PVT in adolescents, with timing and intensity impacted by attentional lapses regardless of experimentally shortened or extended sleep.