Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Understanding Sleep01:11

Understanding Sleep

487
Sleep, an essential biological state, involves significant reductions in physical activity, sensory awareness, and interaction with the environment. This complex physiological process is primarily regulated by specific brain regions, notably the hypothalamus and pons, which govern the sleep-wake cycle or circadian rhythm.
The circadian rhythm, a nearly 24-hour cycle, is deeply influenced by environmental light cues. Light exposure directly affects the hypothalamus, which in turn regulates...
487
Stages of Sleep01:22

Stages of Sleep

450
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...
450
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

1.6K
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
NREM sleep comprises four progressive stages that seamlessly merge:
1.6K
Management of Insomnia01:19

Management of Insomnia

318
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...
318
Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

216
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,...
216
Nightmares and Night Terrors01:18

Nightmares and Night Terrors

156
Nightmares and night terrors represent two distinct types of sleep disturbances that differ in timing, characteristics, and the sleeper's recall of the event. Nightmares are vivid, disturbing dreams that usually awaken the sleeper from REM sleep, a stage of sleep where brain activity is high, and dreams are most frequent. Upon awakening, individuals often have detailed recollections of their nightmares, which can include themes of threats to survival, security, or self-esteem.
Nightmares...
156

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sleep-Wake Disturbances in Patients With Chronic Pain-Associations With Physical Activity Levels.

European journal of pain (London, England)·2026
Same author

Mining Association Rules From a Multimodal Dataset of a Digital Therapeutics Application for Sleep Improvement Through a Healthy Lifestyle: Quantitative Study.

JMIR formative research·2026
Same author

Association between surgically treated knee injury and knee arthroplasty: an explorative study based on Finnish nationwide register-based data.

Acta orthopaedica·2026
Same author

Respiratory effort during sleep predicts mortality in patients with suspected obstructive sleep apnea.

American journal of respiratory and critical care medicine·2026
Same author

Risk factors for manipulation under anesthesia after total knee arthroplasty and subsequent revision arthroplasty: a Finnish register-based study of 154,883 patients.

Acta orthopaedica·2026
Same author

Respiratory event type and duration modulate PPG waveforms in OSA.

Physiological measurement·2026

Related Experiment Video

Updated: Sep 6, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.1K

The Sleep Revolution project: the concept and objectives.

Erna S Arnardottir1,2, Anna Sigridur Islind1,3, María Óskarsdóttir1,3

  • 1Reykjavik University Sleep Institute, Reykjavik University, Reykjavik, Iceland.

Journal of Sleep Research
|June 30, 2022
PubMed
Summary

Obstructive sleep apnea (OSA) affects nearly a billion people, but current diagnostics are inadequate. The Sleep Revolution project uses machine learning to improve OSA severity estimation and personalize treatments, making diagnosis more accessible and cost-effective.

Keywords:
P4 medicineapnea-hypopnea indexcostsdigital management platforme-healthexerciselifestylesmachine learningmobile applicationneurocognitive testsparticipatorypatient-reported outcome measurespolysomnographyself-applied home testingsleep diarysleep revolutiontelemedicine

More Related Videos

The Sleep Nullifying Apparatus: A Highly Efficient Method of Sleep Depriving Drosophila
06:06

The Sleep Nullifying Apparatus: A Highly Efficient Method of Sleep Depriving Drosophila

Published on: December 14, 2020

3.6K
Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.7K

Related Experiment Videos

Last Updated: Sep 6, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.1K
The Sleep Nullifying Apparatus: A Highly Efficient Method of Sleep Depriving Drosophila
06:06

The Sleep Nullifying Apparatus: A Highly Efficient Method of Sleep Depriving Drosophila

Published on: December 14, 2020

3.6K
Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.7K

Area of Science:

  • Sleep Medicine
  • Artificial Intelligence
  • Digital Health

Background:

  • Obstructive sleep apnea (OSA) is a prevalent condition with significant health and economic impacts.
  • Current diagnostic methods, like the apnea-hypopnea index, poorly correlate with OSA comorbidities and symptoms.
  • Existing polysomnography analysis is labor-intensive, costly, and contributes to widespread underdiagnosis.

Purpose of the Study:

  • To develop advanced machine learning tools for more accurate estimation of obstructive sleep apnea severity and phenotypes.
  • To enable personalized treatment strategies for OSA patients, enhancing patient engagement.
  • To reduce the cost and increase the accessibility of sleep studies by automating signal analysis.

Main Methods:

  • Utilizing machine learning algorithms to analyze large datasets of sleep recordings.
  • Developing a digital platform with a mobile application for patient engagement, including electronic sleep diaries, cognitive tests, and questionnaires.
  • Leveraging extensive collaboration across 39 centers with expertise in sleep medicine, computer science, and industry.

Main Results:

  • The project aims to improve the estimation of obstructive sleep apnea severity and identify distinct patient phenotypes.
  • Expected outcomes include more personalized and effective treatment options for individuals with OSA.
  • Anticipated reduction in manual labor for sleep study analysis, leading to decreased costs and increased availability.

Conclusions:

  • The Sleep Revolution project seeks to overcome the limitations of current OSA diagnostics through innovative technology.
  • The initiative has the potential to create new standardized guidelines for sleep medicine, improving patient care globally.
  • By integrating machine learning and digital health tools, the project aims to revolutionize OSA diagnosis and management.