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().

Method and system for rapid entry into and for rapid exiting from sleep states for processors of a portable computing device

Abstract: A method and system for managing sleep states of a portable computing device are described. They include maintaining a sleep set of resource states and an active set of resource states in memory. A request may be issued for a processor to enter into a sleep state. This causes a controller to review a trigger set to determine if a shut down condition for the processor matches one or more conditions listed in the trigger set. Each trigger set may comprise a “trigger event” that may allow a controller to select a specific resource set which is desired by a particular processor based on a trigger event detected by a system power manager. If a trigger set matches a shut down condition, then switching states of one or more resources in accordance with the sleep set may be made by the controller without using a software handshake.

Method and system for assessing mental state

Abstract: A computer-implemented method of assessing a mental state of a subject (106) includes receiving (302), as input, a heartbeat record (200) of the subject. The heartbeat record comprises a sequence of heartbeat data samples obtained over a time span which includes a pre-sleep period (208), a sleep period (209) having a sleep onset time (224) and a sleep conclusion time (226), and a post-sleep period (210). At least the sleep onset time and the sleep conclusion time are identified (304) within the heartbeat record. A knowledge base (124) is then accessed (306), which comprises data obtained via expert evaluation of a training set of subjects and which embodies a computational model of a relationship between mental state and heart rate characteristics. Using information in the knowledge base, the computational model is applied (308) to compute at least one metric associated with the mental state of the subject, and to generate an indication of mental state based upon the metric. The indication of mental state is provided (310) as output.

Home-Use and Real-Time Sleep-Staging System Based on Eye Masks and Mobile Devices with a Deep Learning Model.

Abstract: Sleep is an important human activity. Comfortable sensing and accurate analysis in sleep monitoring is beneficial to many healthcare and medical applications. From 2020, owing to the COVID‑19 pandemic that spreads between people when they come into close physical contact with one another, the willingness to go to hospital for receiving care has reduced; care-at-home is the trend in modern healthcare. Therefore, a home-use and real-time sleep-staging system is developed in this paper. We developed and implemented a real-time sleep staging system that integrates a wearable eye mask for high-quality electroencephalogram/electrooculogram measurement and a mobile device with MobileNETV2 deep learning model for sleep-stage identification. In the experiments, 25 all-night recordings were acquired, 17 of which were used for training, and the remaining eight were used for testing. The averaged scoring agreements for the wake, light sleep, deep sleep, and rapid eye movement stages were 85.20%, 87.17%, 82.87%, and 89.30%, respectively, for our system compared with the manual scoring of PSG recordings. In addition, the mean absolute errors of four objective sleep measurements, including sleep efficiency, total sleep time, sleep onset time, and wake after sleep onset time were 1.68%, 7.56 min, 5.50 min, and 3.94 min, respectively. No significant differences were observed between the proposed system and manual PSG scoring in terms of the percentage of each stage and the objective sleep measurements. These experimental results demonstrate that our system provides high scoring agreements in sleep staging and unbiased sleep measurements owing to the use of EEG and EOG signals and powerful mobile computing based on deep learning networks. These results also suggest that our system is applicable for home-use real-time sleep monitoring.

Patient-Reported Outcomes for Measuring Sleep Disturbance in Pediatric Atopic Dermatitis: cross sectional study of PROMIS Pediatric Sleep Measures and Actigraphy.

Abstract: Background Most children with atopic dermatitis(AD) suffer from sleep disturbance, but reliable and valid assessment tools are lacking. Objectives To test PROMIS (Patient Reported Outcomes Measurement Information System) sleep measures in pediatric AD and to develop an algorithm to screen, assess and intervene to reduce sleep disturbance. Methods A cross-sectional study was conducted with AD children ages 5-17 years and one parent(n=61), who completed sleep, itch, and AD-specific questionnaires; clinicians assessed disease severity. All children wore actigraphy watches for 1-week-objective sleep assessment. Results PROMIS sleep disturbance parent-proxy-reliability was high (Cronbach's α=0.90) and differentiated among Patient Oriented Eczema Measure (POEM)-determined disease severity groups (mean±SD in mild vs. moderate vs. severe was 55.7±7.5 vs. 59.8±10.8 vs. 67.1±9.5, p Limitations This was a local sample. Conclusions Sleep disturbance in pediatric AD should be screened using the POEM sleep question, with further assessment using the PROMIS sleep disturbance measure or objective sleep monitoring if needed.