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

Stages of Sleep01:22

Stages of Sleep

673
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...
673
Understanding Sleep01:11

Understanding Sleep

788
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...
788
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

1.9K
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:
1.9K
Management of Insomnia01:19

Management of Insomnia

341
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...
341
Sleep Apnea01:21

Sleep Apnea

241
Sleep apnea is a condition where breathing stops intermittently during sleep, often leading to significant health issues. Each episode can last from 10 to 20 seconds or more and is frequently accompanied by a brief arousal from sleep. This disturbance, largely unnoticed by the individual, can lead to severe daytime fatigue. Commonly, individuals seek help after being informed by their partners about loud snoring and noticeable breathing pauses during sleep.
The condition is more prevalent among...
241

You might also read

Related Articles

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

Sort by
Same author

Spectral mapping reveals a resemblance of the anesthetic brain state to both sleep and coma.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

The new science of sleep: From cells to large-scale societies.

PLoS biology·2024
Same author

Call to action: an open-source pipeline for standardized performance evaluation of sleep-tracking technology.

Sleep·2023
Same author

Sleep Loss Influences the Interconnected Brain-Body Regulation of Cardiovascular Function in Humans.

Psychosomatic medicine·2022
Same author

How people wake up is associated with previous night's sleep together with physical activity and food intake.

Nature communications·2022
Same author

A Nationally Representative Survey Assessing Restorative Sleep in US Adults.

Frontiers in sleep·2022
Same journal

Non-canonical amino acid incorporation enables minimally disruptive labeling of stress granule and TDP-43 proteinopathy.

eLife·2026
Same journal

Analysis of dendritic input currents during place field dynamics.

eLife·2026
Same journal

TopoMetry systematically learns and evaluates the latent geometry of single-cell data.

eLife·2026
Same journal

Navigating the path: Advice to physician-scientists on choosing a clinical specialty.

eLife·2026
Same journal

Neural activity profiles reveal overlapping, intermingled subpopulations spanning area borders in mouse sensorimotor cortex.

eLife·2026
Same journal

The exquisite mechanics of a tsetse bite.

eLife·2026
See all related articles

Related Experiment Video

Updated: Oct 16, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

724

An open-source, high-performance tool for automated sleep staging.

Raphael Vallat1, Matthew P Walker1

  • 1Center for Human Sleep Science, Department of Psychology, University of California, Berkeley, Berkeley, United States.

Elife
|October 14, 2021
PubMed
Summary
This summary is machine-generated.

A new automated algorithm provides accurate human sleep staging from polysomnography recordings. This free, open-source tool aims to standardize sleep analysis, overcoming the limitations of manual scoring.

Keywords:
NREM sleepREM sleepYASAalgorithmautomated sleep staginghumanmachine-learningneurosciencesleep apneasleep scoringsleep spindleslow wave

More Related Videos

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

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.8K
Polygraphic Recording Procedure for Measuring Sleep in Mice
08:45

Polygraphic Recording Procedure for Measuring Sleep in Mice

Published on: January 25, 2016

24.5K

Related Experiment Videos

Last Updated: Oct 16, 2025

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
04:54

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

Published on: November 8, 2024

724
Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.8K
Polygraphic Recording Procedure for Measuring Sleep in Mice
08:45

Polygraphic Recording Procedure for Measuring Sleep in Mice

Published on: January 25, 2016

24.5K

Area of Science:

  • Sleep Medicine
  • Biomedical Engineering
  • Computational Neuroscience

Background:

  • Human sleep analysis relies heavily on manual visual scoring of polysomnography (PSG) data.
  • Manual scoring is time-consuming, labor-intensive, and susceptible to inter-scorer variability and subjective bias.
  • The increasing volume of sleep data necessitates automated solutions for efficient and objective analysis.

Purpose of the Study:

  • To develop and validate a novel, automated algorithm for accurate human sleep staging.
  • To provide an accessible, user-friendly, and computationally efficient tool for sleep research.
  • To establish an industry standard for automated sleep staging software.

Main Methods:

  • Algorithm training and validation using over 30,000 hours of diverse, global polysomnographic sleep recordings.
  • Performance evaluation based on sleep-staging accuracy and inter-scorer agreement compared to human scorers.
  • Development of an open-source, free software package prioritizing ease of use and low computational demand.

Main Results:

  • The automated algorithm achieved high sleep-staging accuracy, comparable to human expert scoring.
  • The tool demonstrated robust performance across heterogeneous populations worldwide.
  • Achieved inter-scorer agreement levels consistent with human scoring standards.

Conclusions:

  • The developed algorithm offers a reliable and objective method for automated sleep staging.
  • This open-source software has the potential to significantly advance sleep research by enabling widespread adoption of automated analysis.
  • Facilitates standardized, efficient, and unbiased sleep analysis in clinical and research settings.