Relevant bibliographies by topics / Sleepiness

Academic literature on the topic 'Sleepiness'

Author: Grafiati
Published: 4 June 2021
Last updated: 20 February 2023

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Journal articles on the topic "Sleepiness"

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Yousaf, Shagufta. "Sleepiness." Clinical Pediatrics 45, no. 2 (March 2006): 191–92. http://dx.doi.org/10.1177/000992280604500213.

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Ellenbogen, Jeffrey. "Sleepiness." Seminars in Neurology 36, no. 05 (September 23, 2016): 449–55. http://dx.doi.org/10.1055/s-0036-1586264.

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Åkerstedt, Torbjörn. "Sleepiness." Sleep Medicine Reviews 2, no. 1 (February 1998): 1–2. http://dx.doi.org/10.1016/s1087-0792(98)90049-1.

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SP, Sriram, Vinutha Shankar MS, and Shobha MV. "Identifying Excessive Daytime Sleepiness Using Epworth Sleepiness Scale in a Normal Healthy Population – A Pilot Study." JOURNAL OF CLINICAL AND BIOMEDICAL SCIENCES 08, no. 4 (December 15, 2018): 118–20. http://dx.doi.org/10.58739/jcbs/v08i4.4.

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Introduction: Excessive daytime sleepiness (EDS) is a common sign in obstructive sleep apnoea syndrome which is often missed during routine screening. Epworth Sleepiness Scale (ESS) is simple, reliable tool to assess daytime sleepiness. Thus, the aim of the study is to identify excessive daytime sleepiness using ESS in a normal healthy population. Methods: 40 Volunteers aged 30-70 yrs, without history of Diabetes, Coronary Artery Disease and diagnosed sleep disorders who are capable of comprehending the sleep ques-tionnaire (Epworth sleepiness scale) were recruited. Informed consent and institutional ethical clearance was taken before start of the study. Results: EDS is seen among 10% of the subjects. ESS score in males and females were 4.42±4.1 and 3.50±2.2 respectively with p value 0.425 suggesting no significant difference in ESS score between males and females. BMI was comparable between males and females No correlation was found between BMI and ESS. Conclusion: Epworth sleepiness scale can be recommended to the practising physician to screen the patients for EDS a sign of OSA (obstructive sleep apnoea). Key words: Excessive daytime sleepiness, Epworth Sleepiness scale, Obstructive sleep apnoea, obesity
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HORNE, J. A., and L. A. REYNER. "Driver sleepiness." Journal of Sleep Research 4 (December 1995): 23–29. http://dx.doi.org/10.1111/j.1365-2869.1995.tb00222.x.

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Miglis, Mitchell G., and Clete A. Kushida. "Daytime Sleepiness." Sleep Medicine Clinics 9, no. 4 (December 2014): 491–98. http://dx.doi.org/10.1016/j.jsmc.2014.08.007.

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&NA;. "Excessive Sleepiness." Nurse Practitioner 36, no. 5 (May 2011): 33–34. http://dx.doi.org/10.1097/01.npr.0000396634.36290.71.

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Tsai, Sheila C. "Excessive Sleepiness." Clinics in Chest Medicine 31, no. 2 (June 2010): 341–51. http://dx.doi.org/10.1016/j.ccm.2010.02.007.

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Mairesse, Olivier, Elke De Valck, Stijn Quanten, Daniel Neu, Aisha Cortoos, Nathalie Pattyn, Peter Theuns, Raymond Cluydts, and Joeri Hofmans. "Sleepiness phenomics: Modeling individual differences in subjective sleepiness profiles." International Journal of Psychophysiology 93, no. 1 (July 2014): 150–61. http://dx.doi.org/10.1016/j.ijpsycho.2013.03.021.

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Liu, Xianchen, Yanyun Yang, Zhenzhen Liu, and Cunxian Jia. "Associations between Insomnia, Daytime Sleepiness, and Depressive Symptoms in Adolescents: A Three-Wave Longitudinal Study." Journal of Clinical Medicine 11, no. 23 (November 23, 2022): 6912. http://dx.doi.org/10.3390/jcm11236912.

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Background: Insomnia, daytime sleepiness, and depressive symptoms are prevalent in adolescents. This three-wave prospective study examined the associations between the three symptoms in adolescents. Methods: A total of 6995 schoolchildren in 7th and 10th grades (Mean age = 14.86 years) participated in a longitudinal study of behavior and health in Shandong, China. Standardized rating scales were used to assess symptoms of insomnia, daytime sleepiness, and depression in November–December in 2015, 1 year later, and 2 years later. Results: Insomnia was cross-sectionally associated with 10–14-fold increased odds of daytime sleepiness and 5–9-fold increased odds of depression. Daytime sleepiness was associated with 4–5-fold increased odds of depression. Insomnia, daytime sleepiness, or depression at a later time point was significantly predicted by itself at earlier time points. Insomnia was a significant predictor of daytime sleepiness and depression and a mediator between depression and daytime sleepiness. Daytime sleepiness was a significant predictor of insomnia and a mediator between depression and insomnia. Depression was a significant predictor of insomnia and daytime sleepiness and a mediator between insomnia and daytime sleepiness. Conclusions: Insomnia, daytime sleepiness, and depressive symptoms were highly comorbid in adolescents. The associations of insomnia with daytime sleepiness and depression were bidirectional. Depression predicted daytime sleepiness, but not vice versa. Further research is needed to understand the underlying neurobiological mechanisms between insomnia, daytime sleepiness, and depression during adolescence.
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Dissertations / Theses on the topic "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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Books on the topic "Sleepiness"

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Thorpy, Michael J., and Michel Billiard, eds. Sleepiness. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511762697.

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H, Monk Timothy, ed. Sleep, sleepiness, and performance. Chichester: Wiley, 1991.

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Sleepiness and human impact assessment. Milan: Springer, 2014.

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Verster, Joris C., and Charles F. P. George. Sleep, sleepiness and traffic safety. Hauppauge, N.Y: Nova Science Publishers, 2010.

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Sleepiness: Causes, consequences, and treatment. Cambridge: Cambridge University Press, 2011.

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Garbarino, Sergio, Lino Nobili, and Giovanni Costa, eds. Sleepiness and Human Impact Assessment. Milano: Springer Milan, 2014. http://dx.doi.org/10.1007/978-88-470-5388-5.

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R, Barrett P., Reyner L. A, and Great Britain. Department for Transport, eds. Interactions between sleepiness and moderate alcohol intake in drivers. London: Dept. for Transport, 2006.

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Pavey, Jennifer J. Disruption of sleep continuity in mild to moderate, unclassified, excessive daytime sleepiness. [Guildford]: [University of Surrey], 1996.

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Milner, Catherine E. The role of daytime napping in sleepiness and cognitive function in 24-70 year olds. St. Catharines, Ont: Brock University, Dept. of Psychology, 2004.

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La matrone des sleepinges. [Paris]: Fleuve noir, 1993.

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Book chapters on the topic "Sleepiness"

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Horne, Jim. "Sleepiness." In Sleeplessness, 129–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30572-1_8.

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Sachdeva, Alok. "Sleepiness." In Sleep Disorders, 3–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65302-6_1.

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Ellenbroek, Bart, Alfonso Abizaid, Shimon Amir, Martina de Zwaan, Sarah Parylak, Pietro Cottone, Eric P. Zorrilla, et al. "Excessive Sleepiness." In Encyclopedia of Psychopharmacology, 508. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-68706-1_650.

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Horne, Jim. "Extreme Sleepiness." In Sleeplessness, 145–51. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-30572-1_9.

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Besset, A. "Assessing sleepiness." In Sleep, 169–84. Boston, MA: Springer US, 2003. http://dx.doi.org/10.1007/978-1-4615-0217-3_13.

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Pizza, Fabio. "Sleepiness Assessment." In Sleepiness and Human Impact Assessment, 313–24. Milano: Springer Milan, 2014. http://dx.doi.org/10.1007/978-88-470-5388-5_30.

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Peter, Helga. "Sleepiness accidents." In Springer Reference Medizin, 1. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-54672-3_904-1.

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Hein, Holger. "Objectifying Sleepiness." In Sleep Apnea, 43–46. Basel: KARGER, 2006. http://dx.doi.org/10.1159/000093142.

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Wright, Helen, and Leon Lack. "Epworth Sleepiness Scale." In Encyclopedia of Quality of Life and Well-Being Research, 1950–51. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_908.

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Shen, Yun, and Chun-Feng Liu. "Excessive Daytime Sleepiness." In Sleep Disorders in Parkinson’s Disease, 67–81. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2481-3_8.

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Conference papers on the topic "Sleepiness"

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Horne, J. A. "Drivers' sleepiness." In IEE Colloquium on Sleep Monitoring. IEE, 1995. http://dx.doi.org/10.1049/ic:19951586.

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Shinoda, Kazuhiko, Masahiko Yoshii, Hayato Yamaguchi, and Hirotaka Kaji. "Daytime Sleepiness Level Prediction Using Respiratory Information." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/827.

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Daytime sleepiness is not only the cause of productivity decline and accidents, but also an important metric of health risks. Despite its importance, the long-term quantitative analysis of sleepiness in daily living has hardly been done due to time and effort required for the continuous tracking of sleepiness. Although a number of sleepiness detection technologies have been proposed, most of them focused only on driver’s drowsiness. In this paper, we present the first step towards the continuous sleepiness tracking in daily living situations. We explore a methodology for predicting subjective sleepiness levels utilizing respiration and acceleration data obtained from a novel wearable sensor. A class imbalance handling technique and hidden Markov model are combined with supervised classifiers to overcome the difficulties in learning from an imbalanced and time series dataset. We evaluate the performance of our models through a comprehensive experiment.
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Noriega Machado, Paulo, Rafael Nascimento, Francisco Rebelo, José Carvalhais, Teresa Cotrim, and Elisangela Vilar. "Fatigue, sleepiness and workload in train traffic controllers." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001945.

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The levels of fatigue, sleepiness and mental effort in workers could compromise transport system’s security. The objective of this observational field study was to analyze the variation of fatigue, sleepiness and mental effort by shift (morning, afternoon, night), difficulty of work station, age group and consecutive days of work in train traffic controllers. Fatigue was measured by simple reaction time (SRT) and Samn-Perelli perceived fatigue scale; sleepiness by Karolinska Sleepiness Scale (KSS); mental effort with the Rating Scale of Mental Effort (RSME). 93 measurement series were made. The night watch registered the higher values of SRT, fatigue perception scale, KSS and RSME. Workers with 45 years or under rate the work in more difficult work stations with higher values of mental effort. Perceived fatigue and KSS increase with work days’ accumulation. The perceived fatigue, sleepiness and mental effort were maintained at medium/low levels, not appearing to be concerning factors.
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Moreira, Susana, Richard Staats, João Valença, Fatima Caeiro, Patricia Rodrigues, Andreia Colaço, Ana Marques, Luis Moita, and Antonio Bugalho de Almeida. "Comparison Between Two Measures Of Excessive Daytime Sleepiness: Stanford Sleepiness Scale Versus Pupillography." In American Thoracic Society 2010 International Conference, May 14-19, 2010 • New Orleans. American Thoracic Society, 2010. http://dx.doi.org/10.1164/ajrccm-conference.2010.181.1_meetingabstracts.a5072.

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Ma, Zheren, Brandon C. Li, Zeyu Yan, Dongmei Chen, and Wei Li. "Wearable Sleepiness Detection Based on Characterization of Physiological Dynamics." In ASME 2016 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/dscc2016-9849.

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Sleepiness has been considered as one of the major contributors to driver error that causes many automobile accidents. Among various technologies developed to address this issue, the electrooculography (EOG) signal is considered most suitable for sleepiness detection. It is simple, and resilient to environmental factors such as light intensity and driver movement. Most importantly, the physiological signal changes in an early stage and can be used to detect the on-set of human sleepiness. In this paper, we introduce the development of a wearable sleepiness detection system based on analyzing EOG signal dynamics. The system includes wearable sensors, amplifying and transmitting circuits, and a smart phone that could alarm the driver if sleepiness is detected. In this system, the EOG signal is considered as a neurophysiological response of the oculomotor system. Blink signatures are extracted from the EOG signal. It was found that the poles of a linearized blinking motion associated with an alert state are different from those associated with a sleepy state. Based on this understanding, an algorithm to detect the driver’s sleepiness was developed. A proof of concept device design has been completed. This system will help a driver to correct the behavior, and ultimately saves lives.
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Phucharoen, Phuttharaksa, Thamthiwat Nararatwanchai, Chaiyavat Chaiyasut, Sasithorn Sirilun, and Phakkharawat Sittiprapaporn. "A Preliminary Study of Relationship between Epworth Sleepiness Scale and Excessive Sleepiness in Shift Workers." In 2019 16th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON). IEEE, 2019. http://dx.doi.org/10.1109/ecti-con47248.2019.8955323.

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Vargas-Cuentas, Natalia I., and Avid Roman-Gonzalez. "Facial image processing for sleepiness estimation." In 2017 2nd International Conference on Bio-Engineering for Smart Technologies (BioSMART). IEEE, 2017. http://dx.doi.org/10.1109/biosmart.2017.8095346.

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Lamprou, Kallirroi, Konstantinos Chaidas, Konstantinos Stamopoulos, Hussein Chrief, John Stradling, and Annabel Nickol. "Patient and partner reporting sleepiness in patients with obstructive sleep apnoea using the Epworth Sleepiness Scale." In ERS International Congress 2021 abstracts. European Respiratory Society, 2021. http://dx.doi.org/10.1183/13993003.congress-2021.pa2496.

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Boyes, Jennifer, Panagis Drakatos, Ian Jarrold, Judy Harris, and Joerg Steier. "Excessive daytime sleepiness in the general population: Experiences from the use of an online Epworth sleepiness scale." In ERS International Congress 2016 abstracts. European Respiratory Society, 2016. http://dx.doi.org/10.1183/13993003.congress-2016.pa2305.

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Tielbeek, B., S. Voncken, U. Karaca, A. Kolfoort-Otte, and M. De Kruif. "Partners cannot help improve the accuracy of the Epworth Sleepiness Scale for assessment of Excessive Daytime Sleepiness." In ERS International Congress 2022 abstracts. European Respiratory Society, 2022. http://dx.doi.org/10.1183/13993003.congress-2022.3978.

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Reports on the topic "Sleepiness"

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Matteson, L. T., T. L. Kelly, H. Babkoff, S. Hauser, and P. Naitoh. Methylphenidate and Pemoline: Effects on Sleepiness and Mood during Sleep Deprivation. Fort Belvoir, VA: Defense Technical Information Center, March 1991. http://dx.doi.org/10.21236/ada234659.

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Babkoff, H., T. L. Kelly, L. T. Mattseon, S. Gomez, and A. Lopez. Pemoline and Methylphenidate: Interaction With Mood, Sleepiness, and Cognitive Performance During 64 Hours of Sleep Deprivation. Fort Belvoir, VA: Defense Technical Information Center, July 1992. http://dx.doi.org/10.21236/ada256601.

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Torres, Claudia Fernandez, and Alvaro Zubizarreta Macho. Mandibular advancement appliances to treat apnea: an update of the most used currently. A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0034.

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Review question / Objective: Mandibular advancement devices used to treat obstructive sleep apnea. Condition being studied: Obstructive sleep apnea is characterized by episodes of a complete (apnea) or partial collapse (hypopnea) of the upper airway with an associated decrease in oxygen saturation or arousal from sleep. This disturbance results in fragmented, nonrestorative sleep. Other symptoms include loud, disruptive snoring, witnessed apneas during sleep, and excessive daytime sleepiness. OSA has significant implications for cardiovascular health, mental illness, quality of life, and driving safety.
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Basis, Najwa, and Tamar Shochat. Associations between religion and sleep: A systematic review of observational studies in the adult population. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0057.

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Review question / Objective: The aim of this systematic review is to investigate observational studies on the association between religion and sleep in the adult population. To this end, the proposed systematic review will address the following question: What is the role religion plays in shaping an individual's sleep health? Condition being studied: Sleep is a fundamental biological process increasingly recognized as a critical indicator of development and overall health. Generally, insufficient sleep is associated with depressed mood, daytime fatigue, poor daytime functioning and daytime sleepiness, increased risk of cancer, cardiovascular problems, diabetes, and the cause of the higher risk of mortality. Furthermore, changes in sleep architecture and quality have been related to cognitive deterioration, including dementia and Alzheimer's disease. Here we will identify the role of religion in elements of sleep health, to include sleep duration and sleep quality, and associated health outcomes in the adult population.
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Czerwaty, Katarzyna, Karolina Dżaman, Krystyna Maria Sobczyk, and Katarzyna Irmina Sikrorska. The Overlap Syndrome of Obstructive Sleep Apnea and Chronic Obstructive Pulmonary Disease: A Systematic Review. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2022. http://dx.doi.org/10.37766/inplasy2022.11.0077.

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Review question / Objective: To provide the essential findings in the field of overlap syndrome of chronic obstructive pulmonary disease and obstructive sleep apnea, including prevalence, possible predictors, association with clinical outcomes, and severity compared to both chronic obstructive pulmonary disease and obstructive sleep apnea patients. Condition being studied: OSA is characterized by complete cessation (apnea) or significant decrease (hy-popnea) in airflow during sleep and recurrent episodes of upper airway collapse cause it during sleep leading to nocturnal oxyhemoglobin desaturations and arousals from rest. The recurrent arousals which occur in OSA lead to neurocognitive consequences, daytime sleepiness, and reduced quality of life. Because of apneas and hypopneas, patients are experiencing hypoxemia and hypercapnia, which result in increasing levels of catecholamine, oxidative stress, and low-grade inflammation that lead to the appearance of cardio-metabolic consequences of OSA. COPD is a chronic inflammatory lung disease defined by persistent, usually pro-gressive AFL (airflow limitation). Changes in lung mechanics lead to the main clini-cal manifestations of dyspnea, cough, and chronic expectoration. Furthermore, patients with COPD often suffer from anxiety and depression also, the risk of OSA and insomnia is higher than those hospitalized for other reasons. Although COPD is twice as rare as asthma but is the cause of death eight times more often.
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Sluggish Cognitive tempo; circadian preference, sleep, and daytime sleepiness. ACAMH, March 2022. http://dx.doi.org/10.13056/acamh.19594.

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In this podcast, we are joined by Dr. Joey Fredrick to tackle the question ‘Is sluggish cognitive tempo associated with circadian preference, sleep, and daytime sleepiness in adolescence?’. Joey is the first author of a paper on this topic published in the JCPP.
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Continuous positive airway pressure led to less daytime sleepiness in older adults with sleep apnoea. National Institute for Health Research, August 2015. http://dx.doi.org/10.3310/signal-000108.

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You might also be interested in the extended bibliographies on the topic 'Sleepiness' for particular source types:
Journal articles Dissertations / Theses Books
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