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().

Device for the treatment of sleep-related conditions

Abstract: A device and related methods are provided. The device may be for the treatment and detection of various medical and non-medical conditions, including for detecting and treating snoring and sleep apnea, for detecting rapid eye movement of a sleeping person, and for detecting drowsiness or sleepiness of a person. The device may include an alert indicator for providing alerts to the person and a sensor configured for sensing one or more conditions of the person and communicating the condition to the alert indicator.

A neural circuit linking learning and sleep in Drosophila long-term memory

Abstract: Animals retain some but not all experiences in long-term memory (LTM). Sleep supports LTM retention across animal species. It is well established that learning experiences enhance post-learning sleep. However, the underlying mechanisms of how learning mediates sleep for memory retention are not clear. Drosophila males display increased amounts of sleep after courtship learning. Courtship learning depends on Mushroom Body (MB) neurons, and post-learning sleep is mediated by the sleep-promoting ventral Fan-Shaped Body neurons (vFBs). We show that post-learning sleep is regulated by two opposing output neurons (MBONs) from the MB, which encode a measure of learning. Excitatory MBONs-γ2α'1 becomes increasingly active upon increasing time of learning, whereas inhibitory MBONs-β'2mp is activated only by a short learning experience. These MB outputs are integrated by SFS neurons, which excite vFBs to promote sleep after prolonged but not short training. This circuit may ensure that only longer or more intense learning experiences induce sleep and are thereby consolidated into LTM.

Method for realizing sleep function in embedded system

Abstract: The invention discloses a method for realizing sleep function in an embedded type system, which comprises the following steps: a timer task is set for a protocol stack; a task to sleep calls a sleep function sending a massage with information of task to sleep to the timer task, calls the waiting function of an operation system to hang the task to sleep; the timer task starts a timer by the received massage, appoints a response function after the time of the timer is up, and establishes a matching relationship between the function of the timer and the function of the task to sleep; when the time of the timer is up, the appointed response function is called, the wakeup function of the operating system is called according to the matching relationship, and the hung task to sleep is waken up. According to the invention, the original resources of the systems can be used to realize Sleep mechanism, thus solving the conflict in protocol stack transplantation.

Sleep analysis system and analysis method thereof

Abstract: The invention relates to a sleep analysis system and analysis method thereof. The sleep analysis system includes an analysis device and a sleep sensing device, wherein the sleep sensing device includes an ECG signal collecting device, a multi-axis g-sensor, a wireless transmitting unit and a control unit. The ECG signal collecting device is used for obtaining an ECG signal regarding to a user. The multi-axis g-sensor is used for obtaining a multi-axis acceleration signal regarding to the user. The control unit controls the wireless transmitting unit to transmit the ECG signal and the multi-axis acceleration signal to the analysis device to analyze the sleep of the user.

A machine learning approach using EEG signals to measure sleep quality

Abstract: Sleep quality has a vital effect on good health and well-being throughout a life. Getting enough sleep at the right times can help protect mental health, physical health, quality of life, and safety. In this study, an electroencephalography (EEG)-based machine-learning approach is proposed to measure sleep quality. The advantages of this approach over standard Polysomnography (PSG) method are: 1) it measures sleep quality by recognizing three sleep categories rather than five sleep stages, thus higher accuracy can be expected; 2) three sleep categories are recognized by analyzing EEG signals only, so the user experience is improved because fewer sensors are attached to the body during sleep. Using quantitative features obtained from EEG signals, we developed a new automatic sleep-staging framework that consists of a multi-class support vector machine (SVM) classification based on a decision tree approach. We used polysomnographic data from PhysioBank database to train and evaluate and test the performance of the framework, where the sleep stages have been visually annotated. The results demonstrated that the proposed approach achieves high classification performance, which helps to measure sleep quality accurately. This framework can provide a robust and accurate sleep quality assessment that helps clinicians to determine the presence and severity of sleep disorders, and also evaluate the efficacy of treatments.