Dissertations / Theses: 'Sleepiness'

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Jonsson, Maja, and Jennifer Brown. "Deep Learning for Driver Sleepiness Classification using Bioelectrical Signals and Karolinska Sleepiness Scale." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178082.

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Driver sleepiness contributes to a large amount of all road traffic crashes. Developing an objective measurement of driver sleepiness in order to prevent eventual traffic accidents is desirable. The aim of this master thesis was to investigate if deep learning can be used to provide a driver sleepiness classification from brain activity signals obtained by electroencephalography (EEG). The intention was to study the classification performance when using different representations of the input data and to examine how various deep neural network architectures and class weighting during training affect the classification. The data was collected from 12 experiments, where 269 participants (1187 driving sessions) were driving either on real roads or in a moving-base driving simulator, while electrophysiological data was recorded. Several deep neural network architectures were developed, depending on the representation of the input data. Regardless of which data representation that was used as input to the network, the datawas divided into three datasets: Training 60%, validation 20% and test 20%. The data from each participant, with associated driving sessions, were randomly assigned to the different datasets according to the given percentage, which resulted in a subject-independent sleepiness detection. The output was in the form of continuous regression further rounded to the closest integer and divided into five classes according to Karolinska Sleepiness Scale (KSS = 1-5, 6, 7, 8, 9). The best performance was obtained with a convolutional neural network (CNN) combined with Long Short-Term Memory (LSTM) architecture, with time series data as input. This gave an accuracy of 41.44%, a mean absolute error of 0.94 and a macro F1-score of 0.37. Overall, the models with time series data showed better classification results compared to those with time-frequency data. Class weighting, giving all classes inverse proportional weight to their appearance, compensated slightly for class imbalance, but all networks had in general difficulties with generalizing to new data.

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van, den Berg Johannes. "Indicators and predictors of sleepiness." Doctoral thesis, Umeå : Public Health and Clinical Medicine, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-708.

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Hodges, Amanda E. "Objective Quantification of Daytime Sleepiness." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/iph_theses/175.

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BACKGROUND: Sleep problems affect people of all ages, race, gender, and socioeconomic classifications. Undiagnosed sleep disorders significantly and adversely impact a person’s level of academic achievement, job performance, and subsequently, socioeconomic status. Undiagnosed sleep disorders also negatively impact both direct and indirect costs for employers, the national government, and the general public. Sleepiness has significant implications on quality of life by impacting occupational performance, driving ability, cognition, memory, and overall health. The purpose of this study is to describe the prevalence of daytime sleepiness, as well as other quantitative predictors of sleep continuity and quality. METHODS: Population data from the CDC program in fatigue surveillance were used for this secondary analysis seeking to characterize sleep quality and continuity variables. Each participant underwent a standard nocturnal polysomnography and a standard multiple sleep latency test (MSLT) on the subsequent day. Frequency and chi-square tests were used to describe the sample. One-Way Analysis of Variance (ANOVA) was used to compare sleep related variables of groups with sleep latencies of <5 >minutes, 5-10 minutes, and >10 minutes. Bivariate and multivariate logistic regression was used to examine the association of the sleep variables with sleep latency time. RESULTS: The mean (SD) sleep latency of the sample was 8.8 (4.9) minutes. Twenty-four individuals had ≥1 SOREM, and approximately 50% of participants (n = 100) met clinical criteria for a sleep disorder. Individuals with shorter sleep latencies, compared to those with longer latencies reported higher levels of subjective sleepiness, had higher sleep efficiency percentages, and longer sleep times. The Epworth Sleepiness Scale, sleep efficiency percentage, total sleep time, the presence of a sleep disorder, and limb movement index were positively associated with a mean sleep latency of <5 >minutes. CONCLUSIONS: The presence of a significant percentage of sleep disorders within our study sample validate prior suggestions that such disorders remain unrecognized, undiagnosed, and untreated. In addition, our findings confirm questionnaire-based surveys that suggest a significant number of the population is excessively sleepy, or hypersomnolent. Therefore, the high prevalence of sleep disorders and the negative public health effects of daytime sleepiness demand attention. Further studies are now required to better quantify levels daytime sleepiness, within a population based sample, to better understand their impact upon morbidity and mortality. This will not only expand on our current understanding of daytime sleepiness, but it will also raise awareness surrounding its significance and relation to public health.

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Puente, Guillen Pablo. "Predicting sleepiness from driving behaviour." Thesis, University of Leeds, 2016. http://etheses.whiterose.ac.uk/17938/.

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This research investigates the use of objective EEG analysis to determine multiple levels of sleepiness in drivers. In the literature, current methods propose a binary (awake or sleep) or ternary (awake, drowsy or sleep) classification of sleepiness. Having few classification of sleepiness increases the risk of the driver reaching dangerous levels of sleepiness before a safety system can prevent it. Also, these methods are based on subjective analysis of physiological variables, which leads to lack of reproducibility and loss of data, when a lack of consensus is reached amongst the EEG experts. Therefore, the doctoral challenge was to determine whether multiple levels of sleepiness could be defined with high accuracy, using an objective analysis of EEG, a reliable indicator of sleepiness. The study identified awake, post-awake, pre-sleep and sleep as the multiple levels of sleepiness through the objective analysis of EEG. The research used Neural Networks, a type of Machine Learning algorithm, to determine the accuracy of the proposed multiple levels of sleepiness. The Neural Networks were trained using driving and physiological behaviour. The EEG data and the driving and physiological variables were obtained through a series of experiments aimed to induce sleepiness, conducted in the driving simulator at the University of Leeds. As the Neural Network obtained high accuracy when differentiating between awake and sleep and between post-awake and pre-sleep, it led to the conclusion that the proposed objective classification based on objective EEG analysis was suitable. However, this study did not reach the highest levels of accuracy when the 4 levels of sleepiness are combined, nevertheless the solutions proposed by the researcher to be carried in future work can contribute towards increasing the accuracy of the proposed method.

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Manber, Rachel. "Daytime sleepiness and sleep-wake schedules." Diss., The University of Arizona, 1993. http://hdl.handle.net/10150/186454.

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The present study assessed the differential effects of three manipulations of the sleep-wake schedules of college students on their levels of daytime sleepiness as measured by daily subjective ratings. The study was longitudinal and prospective. Following a baseline period (12 days), three experimental conditions were introduced. In the first group students were asked to sleep at least 7.5 hours at night and to avoid taking naps. In the second group, students were asked in addition to follow a regular sleep wake schedule. In the third group students were asked to sleep at least 7 hours at night and to take daily naps. The experimental phase lasted four weeks and overall, compliance was good. A follow up phase (one week) began five weeks past the termination of the experimental phase. The findings indicate that when nocturnal sleep is not deprived, regularization of the sleep-wake schedules lead to reduced sleepiness and improved psychological and cognitive functioning. Subjects in the regular schedule condition experienced greater and longer lasting improvements in their alertness compared with subjects in the other two groups combined. Napping was not found to produce any change in daytime sleepiness, but subjects who had greater increases in the regularity of nap frequency experienced greater decrease in daytime sleepiness. Subjects with evening tendencies benefited most from regularizing their sleep schedules whereas subjects with morning tendencies benefited most from taking naps.

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Yan, Haiyan. "Quantitative EEG changes in excessive daytime sleepiness." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0017/MQ57169.pdf.

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Eriksen, Claire Anne. "Sleepiness - night work, time zones and activity /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-790-1/.

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Filtness, Ashleigh J. "Obstructive sleep apnoea and daytime driver sleepiness." Thesis, Loughborough University, 2011. https://dspace.lboro.ac.uk/2134/8338.

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Driver sleepiness is known to be a major contributor to road traffic incidents (RTIs). An initial literature review identified many studies reporting untreated obstructive sleep apnoea (OSA) sufferers as having impaired driving performance and increased RTI risk. It is consistently reported that treatment with continuous positive air pressure (CPAP) improves driving performance and decreases RTI risk, although most of these studies are conducted less than one year after starting treatment. UK law allows treated OSA patients to continue driving if their doctor states that treatment has been successful. Despite the wealth of publications surrounding OSA and driving, 6 key areas were identified from the literature review as not fully investigated, the: (i) prevalence of undiagnosed OSA in heavy goods vehicle (HGV) drivers in the UK; (ii) impact of sleep restriction on long term CPAP treated OSA compared with healthy controls; (iii) ability of treated OSA participants to identify sleepiness when driving; (iv) impact of one night CPAP withdrawal on driving performance; (v) individual difference in driving performance of long term CPAP treated OSA participants; (vi) choice of countermeasures to driver sleepiness by two groups susceptible to driver sleepiness, OSA and HGV drivers. Key areas (i) and (vi) were assessed using questionnaires. 148 HGV drivers were surveyed to assess OSA symptoms and preference of countermeasures to driver sleepiness. All participants completing the driving simulator study were also surveyed. 9.5% of HGV drivers were found to have symptoms of suspected undiagnosed OSA. Additionally the OSA risk factors were more prevalent for HGV drivers than reported in national statistics reports for the general population. The most effective countermeasures to driver sleepiness (caffeine and a nap) were not the most popular. Being part of a susceptible group (OSA or HGV driver) and prior experience of driver sleepiness did not promote effective choice of countermeasure. Key areas (ii) to (v) were assessed using a driving simulator. Driving simulators present a safe environment to test participants in a scenario where they may experience sleepiness without endangering other road users.

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Persson, Anna. "Heart rate variability for driver sleepiness assessment." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157187.

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Studies have reported that around 20 % of all traffic accidents are caused by a sleepy driver. Sleepy driving has been compared to drunk driving. A sleepy driver is also an issue in the case of automated vehicles in the future. Handing back the control of the vehicle to a sleepy driver is a serious risk. This has increased the need for a sleepiness estimation system that can be used in the car to warn the driver when driving is not recommended. One commonly used method to estimate sleepiness is to study the heart rate variability, HRV, which is said to reflect the activity of the autonomous nervous system, the ANS. The HRV can be expressed through different measures obtained from a signal of RR-intervals. The aim with the thesis is to investigate how well the HRV translates into sleepiness estimation and how the experimental setup might affect the results. In this study, HRV data from 85 sleep deprived drivers was collected together with the drivers’ own ratings of their sleepiness according to the nine graded Karolinska sleepiness scale, KSS. An ANOVA test showed statistical significance for almost all of the used HRV measures when the Driver ID was set as random variable. In order to reduce the number of HRV measures, a feature selection step was performed before training a Support Vector Machine (SVM) used for classification of the data. SVM classifiers are trained to use the input features describing the data to optimize hyperplanes separating the discrete set of classes. Previous research has shown good results in using HRV for sleepiness detection, but common issues are the small data sets used and that most experiments are performed in a simulator instead of at real roads. In some cases, no sleep deprivation is used. The result from the classification in this study is a mean accuracy of around 58-59 %, mean sensitivity of 50-51 %, mean specificity of 75-76 % and mean F1 score of 50-51 % over the three classes ’Alert’, ’Getting sleepy’ and ’Sleepy’. This together with the results of the ANOVA test shows that the HRV measures performed relatively poor when used for classification of the data and that there are large inter-individual differences. This suggests the use of personalized algorithms when developing a sleepiness estimation system and an investigation regarding how other confounding factors could affect the estimation is also motivated.

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Okundolor, Sunday Iken. "Promoting Nurses Management of Night Shift Sleepiness." ScholarWorks, 2019. https://scholarworks.waldenu.edu/dissertations/6466.

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Nurses are largely unaware of the problems of night-shift-nurse sleepiness and available strategies to manage night-shift sleepiness. The purpose of this project was to examine nurses' self-perception, awareness of sleepiness, and current strategies to manage this problem in the emergency medicine department of a major academic hospital in the western United States. The validated de-identified Karolinska Sleepiness Scale (KSS) was used to measure the prevalence and intensity of night shift nurses' sleepiness prior to the development of an educational program on strategies to manage sleepiness. Of the 164 registered nurses surveyed, 72 (43.9%) reported sleepiness greater than 7 on the KSS. An educational program was developed and evaluated by a panel of 6 experts who were selected on their clinical, educational, quality improvement, and research in sleep studies. Expert reviews indicated that the education program was 100% relevant, appropriate, and understandable, and provided adequate information on the topic with no recommended changes. The education program was presented to 16 night shift nurses with a pre/posttest survey completed by 14 nurses. Results indicated that participating nurses increased their knowledge of managing strategies for sleepiness from 69% (agree or strongly agree) preintervention to 92% postintervention. Postintervention, there was a 50% increase in the number of nurses who reported benefits from the education intervention. The findings of this project contribute to positive social change by improving nurses' health and quality patient care by advancing nurses' awareness of night shift sleepiness and countermeasure management strategies.

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Insana, Salvatore. "Sleep and sleepiness among first-time postpartum parents." Morgantown, W. Va. : [West Virginia University Libraries], 2010. http://hdl.handle.net/10450/10968.

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Thesis (Ph. D.)--West Virginia University, 2010.
Title from document title page. Document formatted into pages; contains xii, 125 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 66-77).

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Baulk, Stuart D. "Experimental studies of driver sleepiness in young adults." Thesis, Loughborough University, 2002. https://dspace.lboro.ac.uk/2134/34062.

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Motorists are slowly becoming aware that they are legally and morally responsible for ensuring that they are fully rested and not at risk from sleepiness when driving, while vehicle manufacturers continue to attempt to find fail-safe warning systems. What further practical and theoretical advice can we give to drivers in order to reduce sleepiness-related accidents? Are technological countermeasures a viable alternative? Can we further predict the types of people who are most at risk by examining individual differences? This thesis outlines a series of experimental studies to investigate possible answers to these questions, and discusses the philosophy behind them.

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Mackenzie, Janelle Ellen. "Mothers' sleepiness and driving in the postpartum period." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/95190/1/Janelle_Mackenzie_Thesis.pdf.

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The postpartum period is typically a time of increased sleepiness, however little research has investigated mothers' sleepiness whilst driving during this period. The research presented in this thesis details three studies systematically designed to assess postpartum mothers' sleepiness and driving, followed by the utilisation of this information in the development of an information-based program designed to convey pertinent evidence-based information about postpartum sleepiness, sleep, and sleepy driving.

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Keelan, Oliver, and Henrik Mårtensson. "Feature Engineering and Machine Learning for Driver Sleepiness Detection." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-142001.

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Falling asleep while operating a moving vehicle is a contributing factor to the statistics of road related accidents. It has been estimated that 20% of all accidents where a vehicle has been involved are due to sleepiness behind the wheel. To prevent accidents and to save lives are of uttermost importance. In this thesis, given the world’s largest dataset of driver participants, two methods of evaluating driver sleepiness have been evaluated. The first method was based on the creation of epochs from lane departures and KSS, whilst the second method was based solely on the creation of epochs based on KSS. From the epochs, a number of features were extracted from both physiological signals and the car’s controller area network. The most important features were selected via a feature selection step, using sequential forward floating selection. The selected features were trained and evaluated on linear SVM, Gaussian SVM, KNN, random forest and adaboost. The random forest classifier was chosen in all cases when classifying previously unseen data.The results shows that method 1 was prone to overfit. Method 2 proved to be considerably better, and did not suffer from overfitting. The test results regarding method 2 were as follows; sensitivity = 80.3%, specificity = 96.3% and accuracy = 93.5%.The most prominent features overall were found in the EEG and EOG domain together with the sleep/wake predictor feature. However indications have been made that complexities might contribute to the detection of sleepiness as well, especially the Higuchi’s fractal dimension.

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Reyner, Louise Ann. "Sleep, sleep disturbance and daytime sleepiness in normal subjects." Thesis, Loughborough University, 1995. https://dspace.lboro.ac.uk/2134/27108.

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The concept of sleep disturbance is rather vague. Many people claim to suffer from sleep disturbance, but yet find it hard to describe exactly what they mean by the label in subjective terms. Sleep researchers have a similar problem, it is difficult to describe what is meant by sleep disturbance either in an objective or a subjective way, and harder still to relate sleep disturbance to sleepiness shown the following day.

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Aguirre, Gutiérrez Marisa Mayela. "Behavioural and physiological indices of normal and pathological sleepiness." Thesis, University of Ottawa (Canada), 1985. http://hdl.handle.net/10393/4942.

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Newman, Janice. "Pupillometric assessment of excessive daytime sleepiness in narcolepsy-cataplexy." Thesis, University of Ottawa (Canada), 1991. http://hdl.handle.net/10393/7692.

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Ten untreated patients with narcolepsy-cataplexy and ten age and sex matched normals between the ages of 20 and 71 underwent pupillometric analyses immediately prior to each of five Multiple Sleep Latency Test (MSLT) sessions. Although narcoleptics were sleepier in terms of both their Stanford Sleepiness Scale (SSS) ratings and their latencies to sleep onset, the baseline pupil diameter, pupillary light reflex and pupillary orienting response did not differentiate between groups. Narcoleptics did, however, exhibit a significantly greater frequency of spontaneous oscillations in the dark-adapted state than did controls. These findings indicate that pupillary stability may serve as a supplementary diagnostic tool for narcolepsy-cataplexy. The results are discussed with the view that psychosensory restoration of alertness, among other extraneous variables, must be controlled for when utilizing pupillometric techniques. A review of the literature indicates a variety of methodological and statistical shortcomings that must be amended. Suggestions are made for improving the reliability and validity of the pupillometric approach.

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Wales, Alan. "Investigating sleepiness and distraction in simple and complex tasks." Thesis, Loughborough University, 2009. https://dspace.lboro.ac.uk/2134/19123.

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The cost of sleepiness-related accidents runs into tens of billions of dollars per year in America alone (Leger, 1994), and can play a contributing role in motor vehicle accidents and large-scale industrial disasters (Reason, 1990). Likewise, the effects of an ill-timed distraction or otherwise lack of attention to a main task can be the difference between elevated risk, or simply a lack of productivity. The interaction between sleepiness and distraction is poorly researched, and little is known about the mechanisms and scale of the problems associated by this interaction. Therefore, we sought to determine the effects of sleepiness and distraction using overnight and daytime sleepiness with various levels of distraction on three tasks ranging from a simple vigilance task to a challenging luggage x-ray inspection task. The first and second studies examined overnight sleepiness (7pm to 7am) for twenty-four healthy participants (m = 23.2yrs old - same for both studies) using a psychomotor task compared to a systems monitoring task, while also manipulating peripheral distraction through a television playing a comedy series. The results showed significant effects of sleepiness on the psychomotor task and evidence for interactive effects of distraction, whereas the systems monitoring task showed no changes with either sleepiness or distraction. Subjects were far more prone to distraction when sleepy for both tasks, and EEG findings suggest that the alpha frequency (8-13Hz) power increases reflect impairments of performance. There is a decaying . exponential relationship between the probability of a subject's eyes being open as the response time increases, such that longer responses above three seconds are 95% likely to have occurred with the eyes closed. The third study used a sample of twelve young (m = 20.8yrs) and twelve older (m = 60.0yrs) participants, and examined the effects of sleep restriction (< 5hrs vs normal sleep) with three levels of distraction (no distraction, peripheral in the form of television and cognitive distraction as a simulated conversation by means of verbal fluency task). The task used was an x-ray luggage search simulator that is functionally similar to the task used for airport security screening. The practice day showed that speed and accuracy on the task improved with successive sessions, but that the older group were markedly slower and less accurate than the younger group even before the experimental manipulations. There was no effect of daytime sleep restriction for either the younger or older groups between the two experimental days. However, distraction was found to impair the performance of both young and old, with the cognitive distraction proving to be the most difficult condition. Overall, it is concluded that overnight sleepiness impairs performance in monotonous tasks, but these risks can be diminished by making tasks more engaging. Distractions can affect performance, but may be difficult to quantify as subjects create strategies that allow themselves to attend to distractions during the undemanding moments of a task. Continuous cognitive distraction does affect performance, particularly in older subjects, who are less able to manage concurrent demands effectively. Humans appear capable of coping Sleepiness and Distraction iv with a 40% loss of their usual sleep quota or 24-hours of sleep restriction on complex tasks, but performance degrades markedly on monotonous tasks. Performances for simple and complex tasks are impaired by distracters when the effect of distraction is large enough, but the magnitude of impairment depends on how challenging the task is or how well the subject is able to cope with the distractions.

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Johansson, Ida, and Frida Lindqvist. "Deep learning to classify driver sleepiness from electrophysiological data." Thesis, Linköpings universitet, Institutionen för medicinsk teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157498.

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Driver sleepiness is a cause for crashes and it is estimated that 3.9 to 33 % of all crashes might be related to sleepiness at the wheel. It is desirable to get an objective measurement of driver sleepiness for reduced sensitivity to subjective variations. Using deep learning for classiﬁcation of driver sleepiness could be a step toward this objective. In this master thesis, deep learning was used for investigating classiﬁcation of electrophysiological data, electroencephalogram (EEG) and electrooculogram (EOG), from drivers into levels of sleepiness. The EOG reﬂects eye position and EEG reﬂects brain activity. Initially, the intention was to include electrocardiogram (ECG), which reﬂects heart activity, in the research but this data were later excluded. Both raw time series data and data transformed into time-frequency domain representations were fed into the developed neural networks and for comparison manually extracted features were used in a shallow neural network architecture. Investigation of using EOG and EEG data separately as input was performed as well as a combination as input. The data were labeled using the Karolinska Sleepiness Scale, and the scale was divided into two labels "fatigue" and "alert" for binary classiﬁcation or in ﬁve labels for comparison of classiﬁcation and regression. The eﬀect of example length was investigated using 150 seconds, 60 seconds and 30 seconds data. Diﬀerent variations of the main network architecture were used depending on the data representation and the best result was given when using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network with time distributed 150 seconds EOG data as input. The accuracy was in this case 80.4 % and the majority of both alert and fatigue epochs were classiﬁed correctly with 85.7 % and 66.7 % respectively. Using the optimal threshold from the created receiver operating characteristics (ROC) curve resulted in a more balanced classiﬁer with 76.3 % correctly classiﬁed alert examples and 79.2 % correctly classiﬁed fatigue examples. The results from the EEG data, both in terms of accuracy and distribution of correctly classiﬁed examples, were shown to be less promising compared to EOG data. Combining EOG and EEG signals was shown to slightly increase the proportion of correctly classiﬁed fatigue examples. However, more promising results were obtained when balancing the classiﬁer for solely EOG signals. The overall result from this project shows that there are patterns in the data connected to sleepiness that the neural network can ﬁnd which makes further work on applying deep learning to the area of driver sleepiness interesting.

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Watling, Christopher N. "The sleep and wake drives : exploring the genetic and psychophysiological aspects of sleepiness, motivation, and performance." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/98754/4/Christopher_Watling_Thesis.pdf.

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This research program examined the factors of motivation and genetic variations for their effects on sleepiness and performance. The results suggest that certain genetic variations were found to influence aspects of physiological and subjective sleepiness as well as performance outcomes. Motivation had no effect on performance when partially sleep deprived, but motivation improved task performance on a low-order cognitive task when fully rested. The results suggest sleepiness is resistant to motivation to improve performance. As such, drivers who continue to drive while sleepy by applying extra effort to the task of driving are engaging in a risky driving behaviour.

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Berglund, Jens. "In-Vehicle Prediction of Truck Driver Sleepiness : Steering Related Variables." Thesis, Linköping University, Department of Electrical Engineering, 2007. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-8673.

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In this master thesis project quantitative testing in a truck simulator with 22 participants were conducted during which ten in-vehicle variables were measured. Examples of measured variables are steering wheel torque, lateral position and yaw angle. These measured variables were then used to calculate 17 independent variables that all to some extent explain the sleepiness level of the driver. The drivers’ sleepiness level was measured using the Karolinska Sleepiness Scale (KSS) in order to judge the performance of the independent variables. The combination of the 17 independent variables that best explain the sleepiness level of the driver is then extracted using multiple regression analysis with forward selection.

Sometimes some of the independent variables are not defined; therefore different models were created to handle all possible combinations of valid and invalid independent variables. The final system uses six different models to predict the sleepiness level of the driver.

The performance of the final system showed promising results. The system can correctly classify the drivers in approximately 87% of the cases. The number of occasions when the system classify the driver as sleepy when he/she is still alert is very low, approximately 0.7%.

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Sira, Claire S. "Countering the effects of sleepiness in a simulated driving task." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq22397.pdf.

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Jackson, Brian Joshua. "Cognitive function and excessive daytime sleepiness in methamphetamine-dependent individuals." Diss., Restricted to subscribing institutions, 2008. http://proquest.umi.com/pqdweb?did=1679374131&sid=1&Fmt=2&clientId=1564&RQT=309&VName=PQD.

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Danielsson, Fanny. "NON-CONTACT BASED PERSON’S SLEEPINESS DETECTION USING HEART RATE VARIABILITY." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-44620.

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Today many strategies of monitoring health status and well-being are done through measurementmethods that are connected to the body, e.g. sensors or electrodes. These are often complicatedand requires personal assistance in order to use, because of advanced hardware and attachmentissues. This paper proposes a new method of making it possible for a user to self-monitoring theirwell-being and health status over time by using a non-contact camera system. The camera systemextracts physiological parameters (e.g. Heart Rate (HR), Respiration Rate (RR), Inter-bit-Interval(IBI)) based on facial color variations, due to blood circulation in facial skin. By examining anindividual’s physiological parameters, one can extract measurements that can be used in order tomonitor their well-being. The measurements used in this paper is features of heart rate variability(HRV) that are calculated from the physiological parameter IBI. The HRV features included andtested in this paper is SDNN, RMSSD, NN50 and pNN50 from Time Domain and VLF, LF andLF/HF from Frequency Domain. Machine Learning classification is done in order to classifyan individual’s sleepiness from the given features. The Machine Learning classification modelwhich gave the best results, in forms of accuracy, were Support Vector Machines (SVM). The bestmean accuracy achieved was 84,16% for the training set and 81,67% for the test set for sleepinessdetection with SVM. This paper has great potential for personal health care monitoring and can befurther extended to detect other factors that could help a user to monitor their well-being, such asmeasuring stress level

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Wells, Anita Sara. "Effects of dietary fat and carbohydrate on daytime sleepiness and mood." Thesis, University of Sheffield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.319438.

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Wadeley, A. "Cognitive correlates of sleepiness and sleep disruption in everyday domestic settings." Thesis, Bath Spa University, 2014. http://researchspace.bathspa.ac.uk/5200/.

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Sleepiness and sleep disruption caused by cohabitees could have deleterious cognitive consequences in everyday life. Research in this area is scarce, thus cognitive correlates of varying degrees of sub-optimal sleep patterns in five groups of healthy adults in domestic settings were studied. The groups studied included adults living with healthy partners, adults living with partners with a chronic, sleep-disrupting illness (Parkinson's disease), and mothers of young children.

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Moore, Melisa. "The Relationship Between Sleep Time, Sleepiness, and Psychological Functioning in Adolescents." Case Western Reserve University School of Graduate Studies / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=case1147196371.

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Goldin, Deana Shevit. "Factors that Predit Levels of Sleepiness of Advanced Practice Nursing Students." Diss., NSUWorks, 2017. https://nsuworks.nova.edu/hpd_con_stuetd/43.

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Background: Due to arduous demands of graduate education, advanced practice nursing (APN) students who are classified as adult learners are at risk for suffering sleep deprivation. Factors contributing to sleep deprivation include stress, expected academic challenges, and everyday life stressors. Purpose: This study investigated if APN students’ grade-point average (GPA), gender, and employment status predicted levels of daytime sleepiness. Theoretical Framework. The psychological well-being model selected for this study was consistent with the theory that sleep is a resource essential to well-being; adequate sleep is the resource needed to optimally manage stressful life demands. Methods. Bivariate and multiple regression were employed to examine the relationship between GPA, gender, and employment status with daytime sleepiness on a sample of APN students (N = 123) in their second academic year. The Epworth Sleepiness Scale and a demographic questionnaire were used to record data on GPA, gender, and employment status. Results. Results showed ESS and GPA were negatively correlated and statistically significant (r = -.24, p < .05). This indicates that as the tendency for sleepiness increased, GPA decreased, thereby supporting the alternative hypothesis. Although not statistically significant, employed participants reported greater daytime sleepiness, as did women. Conclusions: When GPA, gender, and employment were combined, multiple correlation showed a statistically significant shared variance of 8% with daytime sleepiness, due primarily to the correlation between GPA and daytime sleepiness. The effect size of shared variance was between small and medium with respect to magnitude of importance.

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Lundin, Maria, and Lena Kanstrup. "Method for detection of sleepiness : - measurement of interaction between driver and vehicle." Thesis, Linköping University, Department of Mechanical Engineering, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-7714.

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As more and more people conduct vigilance-based activities at times other than the traditional daytime work hours, the time utilization will continue to escalate in the next century and will further increase the risks of sleepiness-related accidents.

This project, which is commissioned by Scania CV AB, is to nvestigate the potential of a method for sleepiness detection belonging to esium AB. Our objective is to examine whether Scania CV AB should continue with the investigation of the patent method, and in that case, which patent parameters, that indicate sleepiness, should be more closely inquired. The purpose with the method of patent is to discover a sleepy driving behaviour. This method is based on the interaction that appears between the driver and the vehicle. The interaction consists of small spontaneous corrections with the steering wheel that in this report is called micro communication. How well the interaction is functioning can be measured in degree of interaction, which shows how well the driver and the truck interact with each other. The interaction between the driver and the vehicle is in this report looked upon as answers and questions with a certain reaction time, which appears with a certain answered question frequency. The differences in the signal’s amplitudes are measured in variation in amplitudes.

Experiments to collect relevant signals have to be conducted in order to investigate the potential with the method of the patent. It is eligible to collect data from a person falling asleep, which implies experiments conducted in a simulator. The experiments are executed in

a simulator, one test when they are alert and one when they are sleep deprived. Tests are also executed in a Scania truck. The purpose with these experiments is to collect data of the subject’s normal driving pattern in a truck and to investigate if it is possible to obtain

acceptable data in a truck.

The sleepiness experiments have indicated that the micro communication takes place in a frequency range of 0.25 to 6.0 Hz. The variables that have been found to detect sleepiness with high reliability are the reaction time and the degree of interaction presented in spectra.

The validation experiments have shown it is possible to collect exact and accurate data from the lateral acceleration and the steering wheel torque. But, there is more noise in the signals from truck then there is in the signals from the simulator.

This method for sleepiness detection has, according to the authors, a great potential. However, more experiments have to be conducted. The authors suggest further sleepiness experiments only conducted during night time. The subjects are sufficiently alert in the beginning of the test to receive data from normal driving behaviour. Physiological measurement could be interesting to have by the side of the subjective assessments as an additional base for comparison.

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Mullington, Janet. "Sleepiness and daytime sleep in narcolepsy-cataplexy: Chronobiological, napping and performance aspects." Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10317.

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This dissertation deals with the chronobiology of sleep and sleepiness in narcolepsy-cataplexy. The text consists of a series of papers: three research papers, and a theoretical paper, with a technical paper appended. The first research paper presents an ambulatory EEG study in which subjects were free to go about their routine home activities, wearing the portable recorder for 24 hours. The timing and duration of sleep episodes were calculated relative to nocturnal midsleep time. Results demonstrated that the most frequent timing of naps was about 1-1.5 h in advance of that found for normal healthy subjects who show greatest sleep propensity 180 degrees out of phase with nocturnal midsleep time. The second paper is a theoretical review of circasemidean sleep-wake propensity and proposes a new modelling approach. The third and fourth papers represent companion papers from a 3-condition within-subjects experiment on the effects of scheduled naps on performance. Sleep schedules were based on habitual total sleep time amounts and experimental sleep schedules devised for each subject. A no-nap condition scheduled 100% of total sleep time at night, and nap conditions scheduled 25% of total sleep time in either a single long nap or 5 equidistantly spaced short naps. The first of these papers measures the efficacy of naps in terms of their effects on performance over the whole day and by time-of-day category divisions. Results indicated that for reaction time, performance in the single long nap condition was significantly improved over a no-nap control condition, attributable to post-nap improvements in performance. However, logical reasoning test results were actually better in the no-nap condition, but this may indicate that a longer nocturnal sleep period may be necessary for optimal performance on this task. The timing of unscheduled sleep episodes was again seen to be in advance of the most frequent nap time for normal subjects. The second of these papers examines the related sleep inertia effects. Sleep inertia was found after the short naps as measured by the descending subtraction task, is evident following all but the first and is most prolonged following the third, short nap. Sleep inertia was also found for reaction time variables following short, but was absent following the long nap. Sleep inertia effects on reaction time were significantly greater on SWS arousals. A paper on the technical details of the sleep-wake scheduling and performance testing software is appended.

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Rivera, Miguel. "Monitoring of Micro-sleep and Sleepiness for the Drivers Using EEG Signal." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-21292.

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Nowadays sleepiness at the wheel is a problem that affects to the society at large rather more than it at first seemed. The purpose of this thesis is to detect sleepiness and micro-sleeps, which are the source that subsequently leads to drowsiness, through the study and analysis of EEG signals. Initially, raw data have some artifacts and noises which must be eliminated through a band pass filter. In this thesis EEG signals from different persons are analyzed and the feature extraction is carried out through the method Fast Fourier Transform (FFT). After that, the signals are classified to get the best result. To do this, the method Support Vector Machine (SVM) is used where the feature vectors, which have been extracted previously, are the input. The data are trained and tested to get a result with an accuracy of 77% or higher. It shows that EEG data could be used helping experts in the development of an intelligent system to classify different sleeping conditions i.e., micro-sleep and sleepiness.

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Dahlgren, Anna. "Work stress and overtime work - effects on cortisol, sleep, sleepiness and health." Doctoral thesis, Stockholm : Dept. of Psychology, Stockholm University, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-1355.

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Barrett, Pauline R. "Interactions between moderate alcohol consumption and sleepiness : the effect on driver performance." Thesis, Loughborough University, 2005. https://dspace.lboro.ac.uk/2134/15748.

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Both alcohol and sleepiness are known to be major contnbutors to road traffic accidents m the UK. There has been much debate on whether the current legal blood alcohol concentration (BAC) limit for driving (008%) should be lowered to 005%, like several other countries in the European Union. The present limit may be sabsfactory when a driver IS fully alert, however the pressures of today's society mean that an mcreasmg number of people may be sleep deprived. The consequences of a sleepy person drivmg after drinking a current legally acceptable amount of alcohol have not been fully investigated. An initial literature review idenbfied specific areas that needed to be investigated throughout this programme of work The research took the form of a "hfelike" scenario, with only moderate sleep restnction (5h in bed at night) and moderate alcohol consumpbOn, producmg BACs of approximately half the UK legal driving limit The drive, on a Simulated dual carriageway, lasted for 2h and was very monotonous The research programme was split into four main areas (i) young men (the most at risk group of drivers for sleep related crashes) driving in the afternoon (a time when the number of sleep related crashes are known to increase), under a 2 x 2 experimental deSIgn, With and without alcohol at lunch-time and with and without the prior night's sleep restncted to 5h, (u) an identical gender comparison usmg young women, (hi) a time-of-day companson using young men, but with the drive and alcohol consumption takmg place m the early evening (a bme of day when we are naturally more alert); (iv) a near-zero BAC, when young men have the same alcohol intake as in (I) but earlier, such that their BACs have reduced to nearly zero before startIng the afternoon drive. Dunng the afternoon circadian trough the driving performance of both men and women is severely impaired when moderate sleep restriction and alcohol consumption are combined Of particular concern, is that men seem to be unable to perceive this greater impairment Women generally appear to have better perception of alcohol impairment, even without sleep loss. Unlike men, women's driving is less impaired by modest amounts of alcohol when they are alert, which seems to be because they know their performance IS affected and thus apply more compensatory effort. On the other hand, their rrnpamnent after alcohol when combmed WIth sleep loss is well in excess of any compensatory effort. Trrne of day also affects imp3lrment after alcohol and/or sleep loss. Driving performance IS generally better during the early evening holtrS, when we are nat\lfally more alert, compared with the afternoon, and for all conditions. Moderate alcohol intake does not impair drivmg performance during the early evening, unlike during the afternoon. However, if combined with sleepiness, mcreased driving impamnent does become apparent during the early evening, although, not to the extent that it does durmg the afternoon. BACs are not a good indicator of alcohol-related driving impairment, especially when combmed with sleepiness. During the afternoon, even when BACs fall almost to zero at the start of a drive, sleepy drivers are still more impaired for the first hour of the drive if they have consumed this modest amount of alcohol at lunchtime An unexpected rebound improvement m dnvmg performance is seen ID the second hour of the drive In non-sleep deprived, alert drivers, these same near zero BAC levels did not affect driving performance or significantly increase subjective sleepiness. Overall the results indicate that, combined WIth modest sleepiness, the current legal dnnk drive limtt (008%) is too htglt Thts outcome supports recent and extensIve findmgs WIth fatal and senous road crashes in France (Philip et al., 2001). During the afternoon, a time of day when people are nat\lfally less alert BACs of less than half this UK limit will impair driving even in non-sleep depnved people If drivers are also sleepy, this combmation produces dangerous levels of Impairment durmg the afternoon; the combination also leads to impairment (but to a lesser extent) in the early evening The research was carried out with only moderate levels of sleepiness and alcohol consumptIon, It is fair to conclude that driving impairment would be greater if the sleep loss was greater and/or BACs were htgher, but just under the legal liemt Greater public awareness is required on the knowledge that driving after consuming any alcohol when tired or sleepy is extremely dangerous.

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Bennett, Lesley Samantha. "Sleep fragmentation predictors of daytime sleepiness and health status in sleep apnoea." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299534.

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Manseau, Claude Carleton University Dissertation Psychology. "Severe traumatic brain injury: long term effects on sleep, sleepiness and performance." Ottawa, 1994.

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36

Sharwood, Lisa Nicole. "SLEEPINESS, SLEEP APNOEA AND STIMULANT USE IN LONG DISTANCE COMMERCIAL VEHICLE DRIVERS." Thesis, The University of Sydney, 2013. http://hdl.handle.net/2123/9524.

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Long distance commercial vehicle driving is an occupation associated with multiple challenges, such as the vast distances travelled, and tight and time dependent delivery schedules. These drivers predominantly work shifts with extended driving schedules, often into or during the night; with monotony on the road often for hours on end, adding to the challenge of staying awake. Obstructive sleep apnoea (OSA) has been found in higher rates in commercial drivers than the general population; and in general drivers OSA can increase the risk of crash by up to seven fold. The purpose of this study was to estimate the prevalence of previously undiagnosed obstructive sleep apnoea among a population of long distance commercial vehicle drivers, identify risk and protective factors for crash including the use of caffeinated substances for maintaining alertness while driving, and considers these findings in the light of Australian guidelines concerning driving fitness and fatigue management. The prevalence of previously undiagnosed OSA was 41%, and only 12% of drivers reported a positive Epworth Sleepiness Scale score; further, there was minimal correlation between these groups. Thirty-six percent of drivers were overweight and a further 50% obese; 49% of drivers were cigarette smokers. After adjusting for relevant factors drivers who consumed caffeine to help them stay awake were 63% less likely to crash than drivers who did not take caffeinated substances. It remains evident that long distance commercial vehicle drivers suffer a difficult work environment and less than ideal health; a multitude of factors interplay to confer increased risk of crashing, which adds significant burden to all road users. There are solutions available to mitigate these risks; however, these will work best where there is close interaction and collaboration between regulatory bodies, occupational health and safety groups, road safety stakeholders, medical professionals and importantly the drivers themselves.

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Davies, David Paul. "Snoring, obstructive sleep apnoea and stroke." Thesis, University of Newcastle Upon Tyne, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.364858.

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Neu, Daniel. "Clinical contribution to the study of slow wave sleep in chronic fatigue." Doctoral thesis, Universite Libre de Bruxelles, 2018. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/270979.

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Objectives: To investigate slow wave sleep (SWS) spectral power proportions in distinct clinical conditions sharing non-restorative sleep and fatigue complaints without excessive daytime sleepiness (EDS), namely the Chronic Fatigue Syndrome (CFS) and Primary Insomnia (PI). Impaired sleep homeostasis has been suspected in both CFS and PI. Methods: We compared perceived sleep quality, fatigue and sleepiness symptom-intensities, polysomnography (PSG) and SWS spectral power distributions of drug-free CFS and PI patients without comorbid sleep or mental disorders, with a good sleeper control group.Results: Higher fatigue without EDS and impaired perceived sleep quality were confirmed in both patient groups. PSG mainly differed in sleep fragmentation and SWS durations. Spectral analysis revealed a similar decrease in central ultra slow power (0.3-0.79Hz) proportion during SWS for both CFS and PI and an increase in frontal power proportions of faster frequencies during SWS in PI only. The latter was correlated to affective symptoms whereas lower central ultra slow power proportions were related to fatigue severity and sleep quality impairment. Conclusions: In combination with normal (PI) or even increased SWS durations (CFS), we found consistent evidence for lower proportions of slow oscillations during SWS in PI and CFS. Significance:Observing normal or increased SWS durations but lower proportions of ultra slow power, our findings suggest a possible quantitative compensation of altered homeostatic regulation.
Doctorat en Sciences de la motricité
info:eu-repo/semantics/nonPublished

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Proctor, Keith E. "Answer Distortion on the Epworth Sleepiness Scale During the Commercial Driver Medical Examination." Scholar Commons, 2010. https://scholarcommons.usf.edu/etd/1744.

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Commercial vehicle drivers are required to maintain Department Of Transportation medical certification which entails a Commercial Driver Medical Examination (CDME) and optimally leads to a two-year certification. The examination must be performed by a licensed "medical examiner" administered by a variety of health care providers including physicians, advanced registered nurse practitioners, physician assistants and doctors of chiropractic. Unfavorable findings in the examination can yield either a shortened medical certification period or denial of certification. Sleep disorders including sleep apnea are assessed by a single question located in the health history portion of the CDME form which is filled-out by the examinee. A positive response to this single item often prompts the medical examiner to further supplement this question using a subjective questionnaire, such as the Epworth Sleepiness Scale. This particular questionnaire generates a total score based on the examinee's subjective responses to eight items regarding the propensity to doze-off or fall asleep in different scenarios, thus indicating daytime sleepiness. Commercial drivers depend on the medical certification for their livelihood and it is hypothesized that subjective responses regarding daytime sleepiness are distorted in an effort to attain optimal DOT certification.

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Cresswell, Paul William. "Analysis and modelling of excessive daytime sleepiness in narcolepsy cataplexy syndrome and myotonic dystrophy." Thesis, University of Liverpool, 2007. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.445955.

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Herrmann, Uli Simon. "Sleepiness is not always perceived prior to falling asleep in healthy sleep deprived subjects /." Bern : [s.n.], 2008. http://opac.nebis.ch/cgi-bin/showAbstract.pl?sys=000277046.

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Sallinen, Mikael. "Event-related brain potentials to changes in the acoustic environment during sleep and sleepiness." Jyväskylä : University of Jyväskylä, 1997. http://catalog.hathitrust.org/api/volumes/oclc/39009942.html.

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Platten, Charlotte Ruth. "Individual differences in daytime sleepiness after night sleep extension versus afternoon napping and caffeine." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/35230.

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Recent research has suggested that 7.5h sleep a night may not be sufficient to maintain adequate levels of alertness during the day. Two of the main arguments used in support of this theory are the ease with which many individuals fall asleep during the day and the ability of many to extend their nocturnal sleep length on demand. The first argument has been used to indicate an elevated level of daytime sleepiness, which may lead to decrements in performance throughout the waking day. The second argument uses the concept that all sleep is as a result of a physiological need, and so the ability to obtain additional sleep could indicate the repayment of a previous sleep debt. The first part of this thesis addresses the benefit of nocturnal sleep extension in terms of objective and subjective sleepiness in a group of young, healthy adults.

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Shekari, Soleimanloo Shamsi. "Effects of light and caffeine on human sleepiness and alertness: A simulated driving experiment." Thesis, Queensland University of Technology, 2016. https://eprints.qut.edu.au/95888/1/Shamsi_Shekari%20Soleimanloo_Thesis.pdf.

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This thesis examined the effects of a novel blue-green light intervention, together with caffeine, on sleepiness and driving performance in young adults, a population that is overrepresented in sleepiness-related road crashes. Light, caffeine, and the combination of light and caffeine each improved alertness after chronic-partial sleep deprivation to a greater extent than did a placebo. Each condition improved subjective sleepiness, objective psychomotor performance, and objective driving performance, with the greatest effect found for light and caffeine in combination. These findings have implications for interventions to reduce mortality and morbidity associated with road crashes.

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Iannos, Helena. "The effects of severe sleep deprivation on daytime sleepiness, sleep and recovery of young adults /." Title page and abstract only, 2005. http://web4.library.adelaide.edu.au/theses/09SB/09sbi117.pdf.

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Wilson, Susan Jenifer. "The effect of psychotropic medication on sleep and daytime sleepiness in volunteers and depressed patients." Thesis, University of Bristol, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.297973.

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Van, Fiona. "The effects of alcohol and time of day on psychophysiological measures of sleepiness and performance." Thesis, Manchester Metropolitan University, 1993. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333662.

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Ewing, Donna. "The role of sleep problems and sleepiness in cognitive and behavioural processes of childhood anxiety." Thesis, University of Sussex, 2014. http://sro.sussex.ac.uk/id/eprint/53492/.

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Sleep in children is important for the functioning of a range of cognitive processes, including memory, attention, arousal, executive functioning, and the processing of emotional experiences. This, in addition to the high comorbidity between sleep problems and anxiety, may suggest that sleep plays a role in the cognitive and behavioural processes associated with childhood anxiety. Although a body of research exists which considers the associations between sleep problems and anxiety, there is currently little research evidence available for the effect of children's sleepiness on anxiety, or for the effect of childhood sleep problems or sleepiness on anxiety related processes. To address this, this thesis begins with a meta-analysis exploring the efficacy of transdiagnostic cognitive-behavioural therapy (CBT) for the treatment of childhood anxiety (Paper 1). CBT is generally the treatment of choice for childhood anxiety, and targets the processes that the subsequent papers in this thesis consider in relation to children's sleepiness and sleep problems. Papers two to five consider the effect of sleepiness on a range of cognitive and behavioural processes, including vicariously learning and unlearning fear (Paper 2), ambiguity resolution (Paper 3), emotion recognition (Paper 4), and habituation and avoidance (Paper 5). The final paper considers sleep problems in relation to a CBT intervention for childhood anxiety (Paper 6). Overall, while sleep problems and usual sleepiness were found to be associated with childhood anxiety, current sleepiness was not. On the other hand, sleepiness (usual and current), and reduced sleep, affected children's behavioural processes when exposed to anxiety provoking stimuli, but were not found to affect children's anxietyrelated cognitive processes. Sleep problems interacted with vicarious learning processes, but not with ambiguity resolution or emotion recognition processes, or with change in anxiety symptoms following a CBT intervention for childhood anxiety. Implications for treatment and future research directions are discussed.

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Watling, Christopher Noel. "Stop and revive? : the effectiveness of nap and active rest breaks for reducing driver sleepiness." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/50641/1/Christopher_Watling_Thesis.pdf.

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The incidence of sleep-related crashes has been estimated to account for approximately 20% of all fatal and severe crashes. The use of sleepiness countermeasures by drivers is an important component to reduce the incidence rates of sleep-related crashes. Taking a brief nap and stopping for a rest break are two highly publicised countermeasures for driver sleepiness and are also believed by drivers to be the most effective countermeasures. Despite this belief, there is scarce evidence to support the utility of these countermeasures for reducing driver sleepiness levels. Therefore, determining the effectiveness of these countermeasures is an important road safety concern. The current study utilised a young adult sample (N = 20) to investigate the effectiveness of a nap and an active rest break. The countermeasures effects were evaluated by physiological, behavioural (hazard perception skill), and subjective measures previously found sensitive to sleepiness. Participants initially completed two hours of a simulated driving task followed by a 15 minute nap opportunity or a 15 minute active rest break that included 10 minutes of brisk walking. After the break, participants completed one final hour of the simulated driving task. A within-subjects design was used so that each participant completed both the nap and the active rest break conditions on separate occasions. The analyses revealed that only the nap break provided any meaningful reduction in physiological sleepiness, reduced subjective sleepiness levels, and maintained hazard perception performance. In contrast, the active rest break had no effect for reducing physiological sleepiness and resulted in a decrement in hazard perception performance (i.e., an increase of reaction time latencies), with a transient reduction in subjective sleepiness levels. A number of theoretical, empirical and practical issues were identified by the current study.

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Wong, Keith Keat Huat. "Measuring sleep and neurobiological functional parameters in patients with obstructive sleep apnea." University of Sydney, 2008. http://hdl.handle.net/2123/2245.

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Doctor of Philosophy (Medicine)
Sleepiness is an important source of morbidity in the community, with potentially catastrophic consequences of occupational or driving injuries or accidents. Although many measures of sleepiness exist, there is no gold standard. The electroencephalograph (EEG) has been studied as an indicator of sleep pressure in the waking organism, or sleep depth. A mathematical model has been developed, relating the observed EEG to interactions between groups of neurons in the cortex and thalamus (Robinson, Rennie, Rowe, O'Connor, & Gordon, 2005; Robinson, Rennie, & Wright, 1997). These interactions are thought to be important in the transition from wake to sleep. Sleepiness is common in obstructive sleep apnea (OSA). The measurement of sleepiness would have great utility in quantifying the disease burden, measuring treatment response, or determining fitness for work or driving. This study will evaluate parameters derived from the EEG mathematical model as a measure of sleepiness. It is divided into the following four parts: 1. Subjects with likely OSA based on symptoms and demographics from an international database were compared with matched non-OSA controls. The OSA group showed deficits in executive function and abnormalities on evoked response potential testing. 2. Outcomes from a cross-sectional study in a sleep-clinic OSA population were aggregated by factor analysis into a five summary variables relevant to sleepiness: subjective sleepiness, mood & anxiety, memory & learning, driving, and executive functioning. 3. EEG mathematical model parameters from wake EEG recordings were related to the five summary outcomes. Executive function correlated with a parameter Z, representing the negative feedback loop between the thalamic reticular nucleus and the thalamocortical relay nuclei. 4. EEG model parameters during first NREM sleep cycle of 8 subjects with regular sleep architecture were studied. Net cortical excitation (parameter X) is predicted to increase across the cycle, while there was, as predicted, a greater inhibitory effect of the thalamic reticular nucleus upon thalamocortical relay cells (parameter Z). In this preliminary assessment, EEG model parameters reflecting thalamocortical interactions are sensitive to prefrontal lobe tasks such as executive function, which are known to be vulnerable to sleep loss and sleepiness, and these parameters also show variation with increasing sleep depth.

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Dissertations / Theses on the topic 'Sleepiness'

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