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Related Concept Videos

Stages of Sleep01:22

Stages of Sleep

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Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
Before sleep begins, in wakefulness, the brain exhibits primarily beta waves, which are high in frequency and low in amplitude, indicating alertness...
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Sleep-Wake Cycles01:24

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Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and  rapid eye movement (REM).
NREM Sleep
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REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

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REM Sleep Behavior Disorder (RBD) is a sleep disorder characterized by the absence of muscle paralysis that normally occurs during the REM phase of sleep. This absence allows individuals to physically act out their dreams, which are often vivid and disturbing. Common behaviors exhibited during episodes include kicking, punching, and yelling. These actions can be dangerous, potentially leading to injuries for the person with RBD or their bed partner.
RBD is significantly associated with...
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Management of Insomnia01:19

Management of Insomnia

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The sleep cycle, an integral part of human health, consists of several stages with distinct characteristics and functions. It begins with a transition from wakefulness to sleep, known as the light sleep phase, followed by the restorative deep sleep phase, essential for physical recovery and growth. The cycle concludes with the Rapid Eye Movement (REM) phase, characterized by high brain activity and vivid dreaming. Insomnia, a prevalent sleep disorder, involves difficulty falling asleep, staying...
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Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

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Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
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Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood
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Radar-based sleep stage classification in children undergoing polysomnography: a pilot-study.

R de Goederen1, S Pu2, M Silos Viu3

  • 1Pediatric Intensive Care Unit, Erasmus MC, Sophia Children's Hospital, Rotterdam, the Netherlands; Department of Neonatology, Wilhelmina Children's Hospital, University Medical Center Utrecht Utrecht, the Netherlands.

Sleep Medicine
|April 18, 2021
PubMed
Summary
This summary is machine-generated.

Ultra-Wide band (UWB) radar shows promise for unobtrusive sleep monitoring in children, achieving up to 89.8% accuracy in classifying wake and sleep states. Further development is needed for clinical application.

Keywords:
Breathing rateCentral sleep apneaObstructive sleep apneaRadarRespirationSleep stages

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Area of Science:

  • Biomedical Engineering
  • Sleep Medicine
  • Pediatric Health

Background:

  • Monitoring sleep in children non-invasively is challenging.
  • Polysomnography is the gold standard but can be intrusive for pediatric subjects.

Purpose of the Study:

  • To evaluate the feasibility of using Ultra-Wide band (UWB) radar for unobtrusive sleep monitoring in children.
  • To assess UWB radar's ability to measure sleep stages and breathing patterns.

Main Methods:

  • Thirty-two children (2 months to 14 years) underwent polysomnography with simultaneous UWB radar monitoring.
  • UWB radar captured body movements and breathing rate, from which 38 features were extracted.
  • An adaptive boosting machine learning classifier was employed to estimate sleep stages.

Main Results:

  • The study achieved 89.8% accuracy and a Cohen's Kappa of 0.67 for wake vs. sleep classification.
  • Classification accuracy for wake, REM, and non-REM sleep was 72.9% (Kappa=0.47).
  • Accuracy for differentiating wake, REM, light, and deep sleep was 58.0% (Kappa=0.43).

Conclusions:

  • UWB radar demonstrates potential as a non-contact method for pediatric sleep analysis.
  • Current performance levels require further improvement for widespread clinical adoption.
  • This technology offers a promising avenue for less intrusive sleep disorder assessment in children.