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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: In this article , the authors examined sleep and cognitive data from the UK Biobank (N = 479,420) in middle-to-late life healthy individuals (age 38-73 years) and the relationship with brain structure in a sub-group (n = 37,553).
Abstract: Sleep is essential for life, including daily cognitive processes, yet the amount of sleep required for optimal brain health as we grow older is unclear. Poor memory and increased risk of dementia is associated with the extremes of sleep quantity and disruption of other sleep characteristics. We examined sleep and cognitive data from the UK Biobank (N = 479,420) in middle-to-late life healthy individuals (age 38-73 years) and the relationship with brain structure in a sub-group (N = 37,553). Seven hours of sleep per day was associated with the highest cognitive performance which decreased for every hour below and above this sleep duration. This quadratic relationship remained present in older individuals (>60 years, N = 212,006). Individuals who sleep between six-to-eight hours had significantly greater grey matter volume in 46 of 139 different brain regions including the orbitofrontal cortex, hippocampi, precentral gyrus, right frontal pole and cerebellar subfields. Several brain regions showed a quadratic relationship between sleep duration and volume while other regions were smaller only in individuals who slept longer. These findings highlight the important relationship between the modifiable lifestyle factor of sleep duration and cognition as well as a widespread association between sleep and structural brain health.

13 citations

Proceedings ArticleDOI
07 Sep 2014
TL;DR: This work presents a fine-grained sleep monitoring system which is capable of detecting the breathing rate by leveraging smartphones and exploits the readily available smartphone earphone that placed close to the user to capture the breath sound reliably.
Abstract: Sleep monitoring has drawn increasingly attention as the quality and quantity of the sleep are important for maintaining a person's health and well-being. For example, inadequate and irregular sleep are usually associated with serious health problems such as fatigue, depression and cardiovascular disease. Traditional sleep monitoring systems, such as PSG, involve wearable sensors with professional installations, and thus are limited to clinical usage. Recent work in using smartphone sensors for sleep monitoring can detect several events related to sleep, such as body movement, cough and snore. Such coarse-grained sleep monitoring however is unable to detect the breathing rate which is a vital sign and health indicator. This work presents a fine-grained sleep monitoring system which is capable of detecting the breathing rate by leveraging smartphones. Our system exploits the readily available smartphone earphone that placed close to the user to capture the breath sound reliably. Given the captured acoustic signal, our system performs noise reduction to remove environmental noise and then identifies the breathing rate based on the signal envelope detection. Our experimental evaluation of six subjects over six months time period demonstrates that the breathing rate monitoring is highly accurate and robust under various environments. This strongly indicates the feasibility of using the smartphone and its earphone to perform continuous and noninvasive fine-grained sleep monitoring.

13 citations

Book ChapterDOI
01 Jan 2019
TL;DR: A general purpose sleep monitoring system that can be used for the pressure ulcer risk assessment, to monitor bed exits, and to observe the influence of medication on the sleep behavior is presented.
Abstract: Long-term sleep quality assessment is essential to diagnose sleep disorders and to continuously monitor the health status. However, traditional polysomnography techniques are not suitable for long-term monitoring, whereas, methods able to continuously monitor the sleep pattern in an unobtrusive way are needed. In this paper, we present a general purpose sleep monitoring system that can be used for the pressure ulcer risk assessment, to monitor bed exits, and to observe the influence of medication on the sleep behavior. Moreover, we compare several supervised learning algorithms in order to determine the most suitable in this context. Experimental results obtained by comparing the selected supervised algorithms show that we can accurately infer sleep duration, sleep positions, and routines with a completely unobtrusive approach.

13 citations

Journal ArticleDOI
TL;DR: In this paper, an Internet of Things-based real-time sleep apnea monitoring system has been developed, which allows the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs.
Abstract: Sleep is an essential and vital element of a person’s life and health that helps to refresh and recharge the mind and body of a person. The quality of sleep is very important in every person’s lifestyle, removing various diseases. Bad sleep is a big problem for a lot of people for a very long time. People suffering from various diseases are dealing with various sleeping disorders, commonly known as sleep apnea. A lot of people die during sleep because of uneven body changes in the body during sleep. On that note, a system to monitor sleep is very important. Most of the previous systems to monitor sleeping problems cannot deal with the real time sleeping problem, generating data after a certain period of sleep. Real-time monitoring of sleep is the key to detecting sleep apnea. To solve this problem, an Internet of Things- (IoT-) based real-time sleep apnea monitoring system has been developed. It will allow the user to measure different indexes of sleep and will notify them through a mobile application when anything odd occurs. The system contains various sensors to measure the electrocardiogram (ECG), heart rate, pulse rate, skin response, and SpO2 of any person during the entire sleeping period. This research is very useful as it can measure the indexes of sleep without disturbing the person and can also show it in the mobile application simultaneously with the help of a Bluetooth module. The system has been developed in such a way that it can be used by every kind of person. Multiple analog sensors are used with the Arduino UNO to measure different parameters of the sleep factor. The system was examined and tested on different people’s bodies. To analyze and detect sleep apnea in real-time, the system monitors several people during the sleeping period. The results are displayed on the monitor of the Arduino boards and in the mobile application. The analysis of the achieved data can detect sleep apnea in some of the people that the system monitored, and it can also display the reason why sleep apnea happens. This research also analyzes the people who are not in the danger of sleeping problems by the achieved data. This paper will help everyone learn about sleep apnea and will help people detect it and take the necessary steps to prevent it.

13 citations

Journal ArticleDOI
TL;DR: Findings demonstrate unique associations between disparate sleep disturbance and reward responsiveness elements, highlighting new treatment mechanisms for anhedonia and depression.

13 citations


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