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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().


Papers
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Journal ArticleDOI
TL;DR: A systematic review and meta-analysis of reported polysomnography differences between AD patients and healthy controls was conducted in this paper , which revealed significant reductions in total sleep time, sleep efficiency, and percentage of slow-wave sleep (SWS) and rapid eye movement (REM) sleep, and increases in sleep latency, wake time after sleep onset, number of awakenings, and REM latency in AD compared to controls.
Abstract: Polysomnography (PSG) studies of sleep changes in Alzheimer's disease (AD) have reported but not fully established the relationship between sleep disturbances and AD. To better detail this relationship, we conducted a systematic review and meta-analysis of reported PSG differences between AD patients and healthy controls. An electronic literature search was conducted in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycINFO inception to Mar 2021. Twenty-eight studies were identified for systematic review, 24 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time, sleep efficiency, and percentage of slow-wave sleep (SWS) and rapid eye movement (REM) sleep, and increases in sleep latency, wake time after sleep onset, number of awakenings, and REM latency in AD compared to controls. Importantly, both decreased SWS and REM were significantly associated with the severity of cognitive impairment in AD patients. Alterations in electroencephalogram (EEG) frequency components and sleep spindles were also observed in AD, although the supporting evidence for these changes was limited. Sleep in AD is compromised with increased measures of wake and decreased TST, SWS, and REM sleep relative to controls. AD-related reductions in SWS and REM sleep correlate with the degree of cognitive impairment. Alterations in sleep EEG frequency components such as sleep spindles may be possible biomarkers with relevance for diagnosing AD although their sensitivity and specificity remain to be clearly delineated. AD-related sleep changes are potential targets for early therapeutic intervention aimed at improving sleep and slowing cognitive decline.

19 citations

Patent
09 Sep 2002
TL;DR: In this article, a sleep monitoring system is equipped with a sleeping state measuring device 11a which measures the sleeping state of a user, and a managing device 12a which monitors the sleeping states of the user.
Abstract: PROBLEM TO BE SOLVED: To provide a sleep monitoring system and a monitoring device with which the sleeping state of a subject can be accurately monitored by a third person. SOLUTION: The sleep monitoring system is equipped with a sleeping state measuring device 11a which measures the sleeping state of a user, and a managing device 12a which monitors the sleeping state of the user. A data generating unit 63 of the sleeping state measuring device 11a generates staying-in-bed data and body movement data based on sleeping information on the user which is detected by a biological information detecting unit 31. Then, a web server 47 of the managing device 12a enables the staying-in-bed data and the body movement data received from the sleeping state measuring device 12a, and in addition, the falling into sleep and the awakening of the user distinguished by a distinguishing unit 49 to be browsed by terminal units of the family which are registered in advance. COPYRIGHT: (C)2004,JPO

19 citations

Journal Article
TL;DR: An overview of a sleep simulation and sleep education tool, the SLEEP (Sleep Loss Effects on Everyday Performance) Model, which includes alcohol and caffeine along with sleep as variables that affect performance.
Abstract: Sleep management affects human productivity, safety, health, and efficiency in the performance of various tasks. Most people seem to be unaware of the value and role of sleep and the various risks that can occur when less than desired amounts of sleep are obtained. People especially seem to be unaware of the dangers associated with the long-term accumulation of reduced sleep. Thus, a need exists to provide tools to enhance sleep education. This paper presents an overview of a sleep simulation and sleep education tool, the SLEEP (Sleep Loss Effects on Everyday Performance) Model. The SLEEP Model includes alcohol and caffeine along with sleep as variables that affect performance. Assumptions and modeling concepts are discussed, and the major mathematical functions are presented as part of this paper. The unique features of the SLEEP Model are: 1) it is based on conservation of REM sleep; 2) it uses a ratio of current day's sleep to sleep need for the day to adjust performance predictors; 3) it is designed for easy input and output of information; and 4) it has a wide range of applications, including sleep, alcohol, and caffeine management. Generally the mathematical functions used in the SLEEP Model fit the calibration data set with an R2 of 0.8 or better and have statistical significance at least at the 0.05 probability level. Several figures of simulated results are presented with a discussion of references that report similar measured results. The SLEEP Model appears to be a practical tool to teach people the value of proper sleep management.

19 citations

Journal ArticleDOI
26 Aug 2022-Science
TL;DR: It is discovered that the direction and amplitude of rapid eye movements during REM sleep reveal the direction of the ongoing changes in virtual HD, thereby providing a window into the cognitive processes of the sleeping brain.
Abstract: Since the discovery of rapid eye movement (REM) sleep, the nature of the eye movements that characterize this sleep phase has remained elusive. Do they reveal gaze shifts in the virtual environment of dreams or simply reflect random brainstem activity? We harnessed the head direction (HD) system of the mouse thalamus, a neuronal population whose activity reports, in awake mice, their actual HD as they explore their environment and, in sleeping mice, their virtual HD. We discovered that the direction and amplitude of rapid eye movements during REM sleep reveal the direction and amplitude of the ongoing changes in virtual HD. Thus, rapid eye movements disclose gaze shifts in the virtual world of REM sleep, thereby providing a window into the cognitive processes of the sleeping brain. Description The meaning of rapid eye movement Sleep includes phases characterized by rapid eye movement (REM) that were known to be associated with dreaming. But are these eye movements related to the contents of consciousness in that sleep state? Senzai and Scanziani recorded head direction cells in the anterior dorsal nucleus of the thalamus in mice during wake and sleep (see the Perspective by De Zeeuw and Canto). The direction and amplitude of rapid eye movements encoded the direction and amplitude of the heading of mice in their virtual environment during REM sleep. It was possible to predict the actual heading in the real and virtual world of the mice during wake and REM sleep, respectively, using saccadic eye movements. —PRS Rapid eye movements during REM sleep represent gaze shifts in the virtual world of the sleeping brain.

19 citations

Proceedings ArticleDOI
25 May 2011
TL;DR: The proposed system uses a bed pressure mat to assess and report sleep patterns and is non-invasive, as it does not disrupt the user's usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.
Abstract: The monitoring of sleep patterns is of major importance for various reason such as, the detection and treatment of sleep disorders, the assessment of the effect of different medical conditions or medications on the sleep quality and the assessment of mortality risks associated with sleeping patterns in adults and children. Sleep monitoring by itself is a difficult problem due to both privacy and technical considerations. The proposed system uses a bed pressure mat to assess and report sleep patterns. To evaluate our system we used real data collected in Heracleia Lab's assistive living apartment. Our method is non-invasive, as it does not disrupt the user's usual sleeping behavior and it can be used both at the clinic and at home with minimal cost.

19 citations


Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202422
20233,172
20225,977
2021175
2020191
2019236