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Showing 1 to 18 of 18 for “"sleep stages"”.

  1. Recognition of sleep stages from sensor data

    Sleep is an essential activity for humans. It affects our physical and mental health. So monitoring sleep continuously can help detect any changes in sleep patterns that may be caused by sleep disorders or other diseases. For a long term sleep monitoring system, the most important requirement is …

    missouri Repository record for Recognition of sleep stages from sensor data (opens in a new tab)

  2. Automatic Sleep Assessment from Nocturnal Breathing and Its Applications for Contactless Monitoring

    The ability to assess sleep at home, capture sleep stages, and detect the occurrence of apnea (without on-body sensors) simply by analyzing the radio waves bouncing off people’s bodies while they sleep is quite powerful. Such a capability would allow for longitudinal data collection in patients’ …

    mit Repository record for Automatic Sleep Assessment from Nocturnal Breathing and Its Applications for Contactless Monitoring (opens in a new tab)

  3. Sleep and Cardiac Tachyarrhythmia: Results from the Cross-Sectional Sleep Heart Health Study

    … Despite the well-known relationship between sleep disorders and general cardiovascular risk, relatively few studies have examined sleep quality and quality at pre-clinical levels in patients with cardiac arrhythmias (CA). Patients with CA have at a greatly elevated risk of stroke, sudden …

    york Repository record for Sleep and Cardiac Tachyarrhythmia: Results from the Cross-Sectional Sleep Heart Health Study (opens in a new tab)

  4. Topic Modeling for Inferring Brain States from Electroencephalography (EEG) Signals

    … from EEG signals allows for the management of sleep disorders and brain diseases by providing an insight into the electrophysiological state of the brain. We explore the use of topic modeling – which are popular text processing algorithms – to infer brain states from EEG signals. Latent …

    duke Repository record for Topic Modeling for Inferring Brain States from Electroencephalography (EEG) Signals (opens in a new tab)

  5. "Towards Closed-Loop Sleep Monitoring in Parkinson’s Disease: Self-Supervised Learning Strategies for Sleep Stage Classification"

    Parkinson’s disease (PD) involves severe sleep disturbances that may accelerate neurodegeneration. Closed-loop deep brain stimulation (DBS) is a promising therapeutic solution but requires accurate, real-time sleep-stage classification from subthalamic nucleus signals, a task where conventional …

    york Repository record for "Towards Closed-Loop Sleep Monitoring in Parkinson’s Disease: Self-Supervised Learning Strategies for Sleep Stage Classification" (opens in a new tab)

  6. Quantifying Nocturnal Itch And Its Impact On Sleep Using Machine Learning And Radio Signals

    … nocturnal scratching and its impact on patients’ sleep quality in an objective, sensitive and privacy preserving way. In this work we collect large nocturnal scratching dataset, consisting of 370 nights of infrared footage, radio-frequency (RF) data, and human annotations of scratching. Using this …

    mit Repository record for Quantifying Nocturnal Itch And Its Impact On Sleep Using Machine Learning And Radio Signals (opens in a new tab)

  7. An electrophysiological examination of intentional and inadvertent sleep onset : the effect of intention on the sleep onset process

    … was to examine the effect ofintention on the sleep onset process from an electrophysiological point ofview. To test this, two nap conditions, the Multiple Sleep Latency Test (MSLT) and the Repeated Test of Sustained Wakefulness (RTSW) were used to compare intentional and inadvertent sleep

    brock Repository record for An electrophysiological examination of intentional and inadvertent sleep onset : the effect of intention on the sleep onset process (opens in a new tab)

  8. Wireless Sensing with Machine Learning: Through-Wall Vision & Contactless Health Monitoring

    … assess their vital signs, learn their sleep and sleep stages, and recognize their emotions. Since wireless sensors traverse walls, our sensors can deliver all of these functions through walls and occlusions. The key challenge in delivering the above contributions is that radio signals …

    mit Repository record for Wireless Sensing with Machine Learning: Through-Wall Vision & Contactless Health Monitoring (opens in a new tab)

  9. Enabling Contactless Sleep Studies at Home using Wireless Signals

    Sleep studies help doctors diagnose a variety of sleep-related disorders, such as insomnia and sleep apnea. Most disorders can be managed once they are correctly diagnosed. However, sleep studies usually introduce discomfort and high cost, as patients need to go to hospitals, sleep in unfamiliar …

    mit Repository record for Enabling Contactless Sleep Studies at Home using Wireless Signals (opens in a new tab)

  10. A model for cerebral cortical neuron group electric activity and its implications for cerebral function

    … of the brain, such as arousal, drowsiness, and sleep stages. Moreover, it is used to detect pathological conditions such as seizures, to calibrate drug action during anesthesia, and to understand cognitive task signatures in healthy and abnormal subjects. Being an aggregate measure of neural …

    mit Repository record for A model for cerebral cortical neuron group electric activity and its implications for cerebral function (opens in a new tab)

  11. Examining the effects of wearable technology and biometric coaching on heart rate variability, health related quality of life, and predictors of heart rate variability in collegiate athletes

    … off-season.</p> <p>Objective: To determine if sleep metrics and group assignment between wearable plus coaching (WC) and wearable without coaching (WO) are predictive of HRV in NCAA DI football players. Participants: Collegiate student athletes on NCAA Division I football team. Methods: …

    eastern-wash Repository record for Examining the effects of wearable technology and biometric coaching on heart rate variability, health related quality of life, and predictors of heart rate variability in collegiate athletes (opens in a new tab)

  12. Learning new words: effects of meaning, memory consolidation, and sleep

    … The third question focused on the role of sleep in the consolidation of novel words: which aspects of sleep architecture are associated with lexical integration? Experiment 8 looked at sleep during the night after word learning and sought to clarify the roles sleep spindles and different …

    whiterose Repository record for Learning new words: effects of meaning, memory consolidation, and sleep (opens in a new tab)

  13. ON-FARM APPLICATION OF PLF TECHNOLOGIES TO DETECT EARLY BEHAVIOURAL AND PHYSIOLOGICAL INDICATORS OF DISEASE AND WELFARE IN PAIR - HOUSED DAIRY CALVES BEFORE WEANING.

    … calves, Study 3 further investigated rest and sleep quality, reported as positive welfare indicators, and to sensor-based approaches for their assessment. A systematic review was conducted on the application of PLF technologies to monitor lying, rest, and sleep in calves. The review highlighted …

    milano Repository record for ON-FARM APPLICATION OF PLF TECHNOLOGIES TO DETECT EARLY BEHAVIOURAL AND PHYSIOLOGICAL INDICATORS OF DISEASE AND WELFARE IN PAIR - HOUSED DAIRY CALVES BEFORE WEANING. (opens in a new tab)

  14. Phenotyping Behavioral Disorders in REM Sleep: Study of Digital Biomarkers of Neurodegeneration

    … advancing the understanding and detection of REM Sleep Behavior Disorder (RBD), a recognized prodromal symptom of alpha-synucleinopathies, among which Parkinson's Disease (PD). By integrating computational approaches with physiological insights, this research presents novel tools and methods for …

    cagliari Repository record for Phenotyping Behavioral Disorders in REM Sleep: Study of Digital Biomarkers of Neurodegeneration (opens in a new tab)

  15. An Integrated Software System for EEG/EMG-Based Forward Genetic Screen of Sleep/Wake Abnormalities in ENU-Mutagenized Mice

    The executive neural circuitry and chemistry for sleep/wake switching mechanisms have been increasingly revealed in recent years. However, the very fundamental mechanism of sleep regulation remains a mystery, for example, with the question of what is the neural substrate for “sleepiness” still …

    utswmed Repository record for An Integrated Software System for EEG/EMG-Based Forward Genetic Screen of Sleep/Wake Abnormalities in ENU-Mutagenized Mice (opens in a new tab)

  16. Interfacing with Dreams : Novel Technologies and Protocols for Targeted Dream Incubation

    Scientific research into relationships between sleep physiology and waking cognition has progressed dramatically in the past few decades, but research on the basic science, function, and health consequences of dream phenomenology has not. This dissertation describes research into novel devices and …

    mit Repository record for Interfacing with Dreams : Novel Technologies and Protocols for Targeted Dream Incubation (opens in a new tab)

  17. Klasifikace spánkových stádii

    Cílem této bakalářské práce bylo zpracovat literární rešerši na téma automatické klasifikace spánkových stádií z polysomnografického měření a následně zvolit způsob extrakce příznakových vektorů a kvantitativně ho zhodnotit. V první části se práce zabývá převážně teorií ohledně klasifikace …

    brno-tech Repository record for Klasifikace spánkových stádii (opens in a new tab)

  18. A microanalysis of EMG and EEG changes during the sleep onset period (SOP) : a theoretical investigation with practical applications /

    … and EEG state during the process of falling asleep. Sleep stages during sleep onset (SO) have been generally defined with regards to brain wave activity (Recht schaff en & Kales (1968); and more precisely by Hori, Hayashi, & Morikawa (1994)). However, no previous study has attempted to …

    brock Repository record for A microanalysis of EMG and EEG changes during the sleep onset period (SOP) : a theoretical investigation with practical applications / (opens in a new tab)