Sleep (system call)

About: Sleep (system call) is a research topic. Over the lifetime, 2633 publications have been published within this topic receiving 27806 citations. The topic is also known as: Sleep() & sleep().

Support vector machine and simple recurrent network based automatic sleep stage classification of fuzzy kernel

Abstract: Recently, sleep disorder is taken as a serious issue in people living. Normally people cerebrum passes through variety of static physiological steps or changes for the duration of sleep. Biomedical signal such as EEG, ECG, EOG and EMG setup and signals used to recognize sleep disorders. This work proposes better technique that can be designed to discriminate the stages of sleep which can help physicians to do an analysis and examination of related sleep disorders. In order to identify a modification inside brain, EEG signal partitioned with 5 frequency bands: delta, theta, alpha, beta and gamma. After signal acquisition, Band pass filter is applied to discriminate the input EEG signal of Fpz–Cz electrodes into frequency bands. Statistical specific features are extracted from distinctiveness impression of EEG signal. Then classification is required for classifying the sleep stages automatically with fuzzy kernel support vector machine and simple recurrent network (SRN). In SRN, statistical features were extracted and allocate 30 s period to 5 possible levels in sleep; wakefulness, Non Rapid Eye Movement Sleep Stage 1 (NREMSS 1), NREMSS 2, NREMSS 3 and NREMSS 4, Rapid Eye Movement Sleep Stage (REMSS). These signal acquired from sleep-EDF repository from PhysioBank (PB) used to validate our proposed scheme. Simple recurrent network classification performance rate is found as 90.2% than that of other new classifiers such as feed forward neural network (FNN) and probabilistic neural network (PNN) next it was compared and results are experimented in proposed work.

Sleep function: an evolutionary perspective

Abstract: Prospective epidemiological studies in industrial societies indicate that 7 h of sleep per night in people aged 18 years or older is optimum, with higher and lower amounts of sleep predicting a shorter lifespan. Humans living a hunter-gatherer lifestyle (eg, tribal groups) sleep for 6-8 h per night, with the longest sleep durations in winter. The prevalence of insomnia in hunter-gatherer populations is low (around 2%) compared with the prevalence of insomnia in industrial societies (around 10-30%). Sleep deprivation studies, which are done to gain insights into sleep function, are often confounded by the effects of stress. Consideration of the duration of spontaneous daily sleep across species of mammals, which ranges from 2 h to 20 h, can provide important insights into sleep function without the stress of deprivation. Sleep duration is not related to brain size or cognitive ability. Rather, sleep duration across species is associated with their ecological niche and feeding requirements, indicating a role for wake-sleep balance in food acquisition and energy conservation. Brain temperature drops from waking levels during non-rapid eye movement (non-REM) sleep and rises during REM sleep. Average daily REM sleep time of homeotherm orders is negatively correlated with average body and brain temperature, with the largest amount of REM sleep in egg laying (monotreme) mammals, moderate amounts in pouched (marsupial) mammals, lower amounts in placental mammals, and the lowest amounts in birds. REM sleep might, therefore, have a key role in the regulation of temperature and metabolism of the brain during sleep and in the facilitation of alert awakening.

SleepGuardian: An RF-Based Healthcare System Guarding Your Sleep from Afar

Abstract: The ever accelerating process of urbanization urges more and more population into the swelling cities. While city residents are enjoying an entertaining life supported by advanced informatics techniques like 5G and cloud computing, the same technologies have also gradually deprived their sleep, which is crucial for their wellness. Therefore, sleep monitoring has drawn significant attention from both the research and industry communities. In this article, we first review the sleep monitoring issue and point out three essential properties of an ideal sleep healthcare system, that is, realtime guarding, fine-grained logging, and cost-effectiveness. Based on the analysis, we present SleepGuardian, a Radio Frequency (RF) based sleep healthcare system leveraging signal processing, edge computing and machine learning. SleepGuardian offers an offline sleep logging service and an online abnormality warning service. The offline service provides a fine-grained sleep log like timing and regularity of bed time, onset of sleep and night time awakenings. The online service keeps guarding the subject for any abnormal behaviors during sleep like intensive body twitches and a sudden seizure attack. Once an abnormality happens, it will automatically warn the designated contacts like a nearby emergency room or a close-by relative. We prototype SleepGuardian with low-cost WiFi devices and evaluate it in real scenarios. Experimental results demonstrate that SleepGuardian is very effective.

Adjusting alarms based on sleep onset latency

Abstract: In some implementations, a mobile device can adjust an alarm setting based on the sleep onset latency duration detected for a user of the mobile device. For example, sleep onset latency can be the amount of time it takes for the user to fall asleep after the user attempts to go to sleep (e.g., goes to bed). The mobile device can determine when the user intends or attempts to go to sleep based on detected sleep ritual activities. Sleep ritual activities can include those activities a user performs in preparation for sleep. The mobile device can determine when the user is asleep based on detected sleep signals (e.g., biometric data, sounds, etc.). In some implementations, the mobile device can determine recurring patterns of long or short sleep onset latency and present suggestions that might help the user sleep better or feel more rested.

Promoting Sleep Health Among Families of Young Children in Head Start: Protocol for a Social-Ecological Approach.

Abstract: Inadequate or poor quality sleep in early childhood impairs social-emotional and cognitive function via effects on the developing brain and increases obesity risk via hormonal and endocrine effects. The prevalence of short sleep duration, behavioral sleep problems, and sleep-disordered breathing among children aged 3 to 5 years is 20% to 50%. Healthy sleep habits increase sleep duration and prevent behavioral sleep problems. Awareness of sleep-disordered breathing symptoms leads to its timely treatment. We designed a study that aims to empower families whose children are in early childhood programs with the knowledge and skills needed to obtain healthy sleep and to recognize a sleep problem. We used the social-ecological framework to guide individual, interpersonal, organizational, community, and policy interventions. This study builds on the Sweet Dreamzzz, Inc, Early Childhood Sleep Education Program (ECSEP) in Head Start. A stepped-wedge-cluster randomized trial will test effects on child, parent, and classroom outcomes; a policy evaluation will assess the impact of knowledge-translation strategies. The study has 3 aims. The first is to adapt educational materials into multimedia formats and build the capacity of Head Start agencies to implement the study. The second aim is to enroll 540 parent-child dyads in a primary prevention trial of sleep health promotion in Head Start and to analyze effects on children's sleep duration (primary outcome); parents' knowledge, attitudes, self-efficacy, and behavior; and children's sleep difficulties. The third aim is to conduct a secondary prevention feasibility study of screening and guidance for sleep problems. Secondary outcomes are changes in classroom behaviors and policies. Integrating sleep health literacy into early childhood programs could affect the life-course development of millions of children.