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().

Detection and monitoring of stress events during sleep

Abstract: A computer-implemented method for monitoring a patient (22) includes receiving physiological signals from the patient during sleep and processing at least one of the signals to detect a spontaneous stress event. One or more of the signals following the stress event are analyzed so as to evaluate a stress response of the patient.

Heart rate variability with sleep detection

Abstract: A system uses continuous tracking of sleep activity and heart rate activity to evaluate heart rate variability immediately before transitioning to an awake state, e.g., at the end of the last phase of deep sleep. In particular, a wearable, continuous physiological monitoring system as described herein includes one or more sensors to detect sleep states, the transitions between sleep states, and the transitions from a sleep state to an awake state for a user. This information can be used in conjunction with continuously monitored heart rate data to calculate heart rate variability of the user at the end of the last phase of sleep preceding the user waking up. By using the history of heart rate data in conjunction with sleep activity in this manner, an accurate and consistent recovery score can be calculated based on heart rate variability.

Positioning signal receiving apparatus

Abstract: A GPS receiver is alternately switched between sleep conditions under which between sleep conditions under which a temperature-compensated crystal oscillator is shut off for a specific sleep time while a real-time clock section is kept operational and normal operating conditions under which both the temperature-compensated crystal oscillator and the real-time clock section are kept operational. The GPS receiver determines the ratio of the number of pulses of a reference clock signal to the number of pulses of a low-frequency clock signal counted during a specific period of time preceding the sleep time. The GPS receiver estimates a count value which should have been reached by a reference clock counter at the end of the sleep time if the temperature-compensated crystal oscillator continuously generated the reference clock signal based on the number of pulses of the low-frequency clock signal counted during the sleep time.

Longitudinal study of infant sleep: results of 14 subjects studied at monthly intervals.

Abstract: The sleep of 14 normal infants was studied every month throughout the first year. A computer program incorporated an analysis of multiple variables from polygraphic data as recommended by a recently standardized new born scoring manual. Under our recording conditions, quiet sleep increased during the first 3 months but indeterminate sleep did not decline over the year as expected. This finding is incompatible with the notion that indeterminate sleep reflects immaturity or “undifferentiation” in sleep organization.

Multi-Modal Physiological Signals Based Squeeze-and-Excitation Network With Domain Adversarial Learning for Sleep Staging

Abstract: Sleep staging is the basis of sleep medicine for diagnosing psychiatric and neurodegenerative diseases. However, the existing sleep staging methods ignore the fact that multi-modal physiological signals are heterogeneous, and different modalities contribute to sleep staging with distinct impacts on specific stages. Therefore, how to model the heterogeneity of multi-modal signals and adaptively utilize the multi-modal signals for sleep staging remains challenging. Moreover, existing methods suffer from the individual variance of physiological signals. How to generalize the sleep staging model for the variance among subjects is also challenging. To address the above challenges, we propose the multi-modal physiological signals based Squeeze-and-Excitation Network with Domain Adversarial Learning (SEN-DAL) to capture the features of electroencephalogram (EEG) and electrooculogram (EOG) for sleep staging. The SEN-DAL is made up of two independent feature extraction networks for modeling the heterogeneity, a Multi-modal Squeeze-and-Excitation feature fusion module for adaptively utilizing the multi-modal signals, and a Domain Adversarial Learning module to extract subject-invariant sleep feature. Experiments demonstrate that the SEN-DAL is superior to the baseline models on a public sleep staging dataset, reaching an F1-Score of 82.1%, which is similar to human experts. Through the ablation experiments, we found that the proposed mechanisms, including modal-independent feature extraction, adaptive utilization of multi-modal signals, and domain adversarial learning, are effective for sleep staging tasks. The code of SEN-DAL is available at https://github.com/xiyangcai/SEN-DAL.