The spontaneous eye-blink as sleepiness indicator in patients with obstructive sleep apnoea syndrome-a pilot study

Effect of CannEpil® on simulated driving performance and co-monitoring of ocular activity: A randomised controlled trial

BACKGROUND

Medicinal cannabis products containing Δ9-tetrahydrocannabinol (THC) are increasingly accessible. Yet, policy guidelines regarding fitness to drive are lacking, and cannabinoid-specific indexations of impairment are underdeveloped.

AIMS

To determine the impact of a standardised 1 mL sublingual dose of CannEpil^®, a medicinal cannabis oil containing 100 mg cannabidiol (CBD) and 5 mg THC on simulated driving performance, relative to placebo and whether variations in vehicle control can be indexed by ocular activity.

METHODS

A double-blind, within-subjects, randomised, placebo-controlled, crossover trial assessed 31 healthy fully licensed drivers (15 male, 16 female) aged between 21 and 58 years (M = 38.0, SD = 10.78). Standard deviation of lateral position (SDLP), standard deviation of speed (SDS) and steering variability were assessed over time and as a function of treatment during a 40 min simulated drive, with oculomotor parameters assessed simultaneously. Oral fluid and plasma were collected at 30 min and 2.5 h.

RESULTS

CannEpil did not significantly alter SDLP across the full drive, although increased SDLP was observed between 20 and 30 min (p < 0.05). CannEpil increased SDS across the full drive (p < 0.05), with variance greatest at 20-30 min (p < 0.001). CannEpil increased fixation duration (p < 0.05), blink rate (trend p = 0.051) and decreased blink duration (p < 0.001) during driving. No significant correlations were observed between biological matrices and performance outcomes.

CONCLUSIONS

CannEpil impairs select aspects of vehicle control (speed and weaving) over time. Alterations to ocular behaviour suggest that eye tracking may assist in determining cannabis-related driver impairment or intoxication. Australian and New Zealand Clinician Trials Registry, https://anzctr.org.au(ACTRN12619000932167).

Utilizing EEG and fNIRS for the detection of sleep-deprivation-induced fatigue and its inhibition using colored light stimulation

Drowsy driving is a common, but underestimated phenomenon in terms of associated risks as it often results in crashes causing fatalities and serious injuries. It is a challenging task to alert or reduce the driver's drowsy state using non-invasive techniques. In this study, a drowsiness reduction strategy has been developed and analyzed using exposure to different light colors and recording the corresponding electrical and biological brain activities. 31 subjects were examined by dividing them into 2 classes, a control group, and a healthy group. Fourteen EEG and 42 fNIRS channels were used to gather neurological data from two brain regions (prefrontal and visual cortices). Experiments shining 3 different colored lights have been carried out on them at certain times when there is a high probability to get drowsy. The results of this study show that there is a significant increase in HbO of a sleep-deprived participant when he is exposed to blue light. Similarly, the beta band of EEG also showed an increased response. However, the study found that there is no considerable increase in HbO and beta band power in the case of red and green light exposures. In addition to that, values of other physiological signals acquired such as heart rate, eye blinking, and self-reported Karolinska Sleepiness Scale scores validated the findings predicted by the electrical and biological signals. The statistical significance of the signals achieved has been tested using repeated measures ANOVA and t-tests. Correlation scores were also calculated to find the association between the changes in the data signals with the corresponding changes in the alertness level.

Drowsiness Detection Using Ocular Indices from EEG Signal

Drowsiness is one of the main causes of road accidents and endangers the lives of road users. Recently, there has been considerable interest in utilizing features extracted from electroencephalography (EEG) signals to detect driver drowsiness. However, in most of the work performed in this area, the eyeblink or ocular artifacts present in EEG signals are considered noise and are removed during the preprocessing stage. In this study, we examined the possibility of extracting features from the EEG ocular artifacts themselves to perform classification between alert and drowsy states. In this study, we used the BLINKER algorithm to extract 25 blink-related features from a public dataset comprising raw EEG signals collected from 12 participants. Different machine learning classification models, including the decision tree, the support vector machine (SVM), the K-nearest neighbor (KNN) method, and the bagged and boosted tree models, were trained based on the seven selected features. These models were further optimized to improve their performance. We were able to show that features from EEG ocular artifacts are able to classify drowsy and alert states, with the optimized ensemble-boosted trees yielding the highest accuracy of 91.10% among all classic machine learning models.

Eye blink parameters to indicate drowsiness during naturalistic driving in participants with obstructive sleep apnea: A pilot study

OBJECTIVES

To determine whether continuous eye blink measures could identify drowsiness in patients with obstructive sleep apnea (OSA) during a week of naturalistic driving.

DESIGN

Observational study comparing OSA patients and healthy controls.

SETTING

Regular naturalistic driving across one week.

PARTICIPANTS

Fifteen untreated moderate to severe OSA patients and 15 age (± 5 years) and sex (female = 6) matched healthy controls.

MEASUREMENTS

Participants wore an eye blink drowsiness recording device during their regular driving for one week.

RESULTS

During regular driving, the duration of time with no ocular movements (quiescence), was elevated in the OSA group by 43% relative to the control group (mean [95% CI] 0.20[0.17, 0.25] vs 0.14[0.12, 0.18] secs, P = .011). During long drives only, the Johns Drowsiness Scale was also elevated and increased by 62% in the OSA group relative to the control group (1.05 [0.76, 1.33] vs 0.65 [0.36, 0.93], P = .0495). Across all drives, critical drowsiness events (defined by a Johns Drowsiness Scale score ≥2.6) were twice as frequent in the OSA group than the control group (rate ratio [95% CI] =1.93 [1.65, 2.25], P ≤ .001).

CONCLUSIONS

OSA patients were drowsier than healthy controls according to some of the continuous real time eye blink drowsiness measures. The findings of this pilot study suggest that there is potential for eye blink measures to be utilized to assess fitness to drive in OSA patients. Future work should assess larger samples, as well as the relationship of eye blink measures to conventional fitness to drive assessments and crash risk.

Assessing the validity of eyelid parameters to detect impairment due to benzodiazepines

OBJECTIVE

Benzodiazepines impair driving ability and psychomotor function. Eyelid parameters accurately reflect drowsiness; however, the effects of benzodiazepines on these measures have not been extensively studied. The aim of this study was to investigate the effect of benzodiazepines on eyelid parameters and evaluate their accuracy for detecting psychomotor impairment.

METHODS

Eyelid parameters were recorded during a psychomotor vigilance task (PVT) and driving simulation over 2 days, baseline, and after 20-mg oral temazepam. The utility of eyelid parameters for detecting PVT lapses was evaluated using receiver operating characteristic curves, and cut-off levels indicating impairment (≥1 and ≥2 PVT lapses per min) were identified. The accuracy of these cut-off levels for detecting driving simulator crashes was then examined.

RESULTS

PVT and driving simulator performance was significantly impaired following benzodiazepine administration (p < .05). Average eyelid closure duration (inter-event duration) was a reliable indicator of PVT lapses (area under the curve [AUC] of 0.87-0.90). The cut-off value of eyelid closure duration derived from PVT AUC was able to predict driving simulator crashes with moderately high sensitivity and specificity (76.23% and 75.00%).

CONCLUSIONS

Eyelid parameters were affected by benzodiazepines and accurately detected the psychomotor impairment. In particular, eyelid closure duration is a promising real-time indicator of benzodiazepine impairment.

Sleep Deprivation Triggers Cognitive Control Impairments in Task-Goal Switching

STUDY OBJECTIVES

This study investigates the impact of sleep deprivation (SD) on task-goal switching, a key component of cognitive flexibility.

METHODS

Task-goal switching performance was tested after one night of regular sleep (n = 17 participants) or of total SD (n = 18). To understand the relationships between task-switching performance and other cognitive processes following SD, participants were tested for other key attentional (alertness and vigilance) and executive (inhibition and working memory) functions. Spontaneous eye blink rate (EBR) was also measured as an indirect marker of striatal dopaminergic function.

RESULTS

SD negatively affects task-goal switching as well as attentional and inhibition measures, but not working memory. Changes in task-goal switching performance were not significantly correlated with changes in objective and subjective markers of fatigue and sleepiness, response inhibition, or spontaneous EBR.

CONCLUSIONS

Altogether, our results show differentiated effects of SD on key executive functions such as working memory, inhibition, and task-goal switching.

The accuracy of eyelid movement parameters for drowsiness detection

STUDY OBJECTIVES

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.

METHODS

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]).

RESULTS

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.

CONCLUSIONS

Ocular measures, particularly those measuring the average duration of episodes of eye closure are promising real-time indicators of drowsiness.

Algorithm for automatic analysis of electro-oculographic data

BACKGROUND

Large amounts of electro-oculographic (EOG) data, recorded during electroencephalographic (EEG) measurements, go underutilized. We present an automatic, auto-calibrating algorithm that allows efficient analysis of such data sets.

METHODS

The auto-calibration is based on automatic threshold value estimation. Amplitude threshold values for saccades and blinks are determined based on features in the recorded signal. The performance of the developed algorithm was tested by analyzing 4854 saccades and 213 blinks recorded in two different conditions: a task where the eye movements were controlled (saccade task) and a task with free viewing (multitask). The results were compared with results from a video-oculography (VOG) device and manually scored blinks.

RESULTS

The algorithm achieved 93% detection sensitivity for blinks with 4% false positive rate. The detection sensitivity for horizontal saccades was between 98% and 100%, and for oblique saccades between 95% and 100%. The classification sensitivity for horizontal and large oblique saccades (10 deg) was larger than 89%, and for vertical saccades larger than 82%. The duration and peak velocities of the detected horizontal saccades were similar to those in the literature. In the multitask measurement the detection sensitivity for saccades was 97% with a 6% false positive rate.

CONCLUSION

The developed algorithm enables reliable analysis of EOG data recorded both during EEG and as a separate metrics.

Slow eye movement as a possible predictor of reaction delays to auditory warning alarms in a drowsy state

In recently developed intelligent vehicles, warning alarms are often used to prompt avoidance behaviours from drivers facing imminent hazardous situations. However, when critical reaction delays to auditory stimulation are anticipated, the alarm should be activated earlier to compensate for such delays. It was found that reaction times to an auditory stimulus significantly increased in the presence of slow eye movement (SEM), which is known to occur frequently during the wake-sleep transition. The reaction delay could not be attributed to temporal effects such as fatigue and was invariant regardless of response effectors (finger or foot). Moreover, it was found that applied pedal force decreased immediately after an auditory stimulus interrupted SEM. Consequently, it was concluded that SEM can be a good predictor of reaction delays to auditory warning alarms when drivers are in a drowsy state. STATEMENT OF RELEVANCE: The present study demonstrated that simple auditory reaction time significantly increased when SEM emerged. In the design of vehicle safety systems using warning alarms to prompt avoidance behaviours from drivers, such reaction delays during SEM must be taken into account.

[Clinical features of idiopathic restless legs syndrome in Japanese patients]

BACKGROUND

Little is known about the diagnosis and management of restless legs syndrome (RLS) in Japanese neurology clinics.

OBJECTIVE

To validate the diagnostic criteria of the International RLS Study Group (IRLSSG) and the treatment algorithm of the Mayo Clinic in a Japanese neurology clinic setting and to clarify the features of Japanese patients with idiopathic RLS.

METHODS

Patients with RLS symptoms were examined by a neurologist and the assessment included neurological examination, tests for periodic limb movements (PLM) and dopaminergic response, and the clinical diagnosis was made according to IRLSSG diagnostic criteria. Patients diagnosed with idiopathic RLS were treated with dopaminergic agents and the efficacy was evaluated.

RESULTS

The study subjects were 151 Japanese patients who presented with RLS symptoms. Idiopathic RLS was diagnosed in 113 patients, secondary RLS in 16 and RLS mimics in 22. The cause of RLS mimics was either myelopathy, radiculopathy or neuropathy in 11 patients. The mean age of patients with idiopathic RLS was 50.1 (SD 20.0) years, 63% were woman, 97% had daily RLS, 31% had family history (40% of the early-onset subgroup), 86% reported unpleasant sensations in the lower legs, 43% had PLM in the daytime suggested immobilization test, 81% suffered from insomnia, 49% had limitations of work and activities, 71% reported impaired mood, 27% had consulted physicians about their symptoms, 4% had been diagnosed with RLS, 73% improved after dopaminergic treatments, and 33% experienced complete remission.

CONCLUSIONS

The clinical features of Japanese patients with idiopathic RLS were identical to those reported in western countries, which suggests that IRLSSG diagnostic criteria and Mayo Clinic treatment algorism are valid in Japanese neurology clinics. Both patients and physicians were not fully aware of RLS in this country. Neurological examination was important in excluding RLS mimics and making a diagnosis of RLS.

Clinical trials in restless legs syndrome--recommendations of the European RLS Study Group (EURLSSG)

The European Restless Legs Syndrome (RLS) Study Group (EURLSSG) is an association of European RLS experts who are actively involved in RLS research. A major aim of the Study Group is the development and continuous improvement of standards for diagnosis and treatment of RLS. Several members developed study designs and evaluation methods in investigator-initiated trials early in the 1990s, and all members have since contributed to many pivotal and nonpivotal drug trials for the treatment of RLS. The recommendations on clinical investigations of pharmacological treatment of RLS patients summarize the group's expertise and knowledge acquired through clinical trials. The recommendations primarily address how to plan and conduct confirmatory, randomized clinical studies in patients with idiopathic RLS. Advice is presented for the diagnosis of RLS and clinical and polysomnographic inclusion and exclusion criteria. Primary and secondary endpoints for an evaluation of efficacy are based on a critical description of validated methods for both short- and long-term trials, also in special populations (children, pregnant women, elderly patients). The recommendations include the assessment of augmentation. Finally, general issues including the evaluation of safety and tolerability, as well as specific neurological and cardiovascular risks and sleep attacks/daytime somnolence, are discussed. The aim of these recommendations is to support research groups or pharmaceutical companies in the design of optimized study protocols.