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

Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

202
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,...
202
Understanding Sleep01:11

Understanding Sleep

441
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...
441
REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

299
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...
299
Sleep Apnea01:21

Sleep Apnea

204
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...
204
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

1.5K
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:
1.5K
Narcolepsy01:07

Narcolepsy

164
Narcolepsy is a chronic sleep disorder characterized by pervasive, uncontrolled sleepiness and other sleep disturbances. One of its hallmark symptoms is an abrupt transition to REM sleep upon falling asleep, which causes symptoms typically associated with this phase to occur unexpectedly during wakefulness. These include the following symptoms, which typically last from a minute or two to half an hour.
164

You might also read

Related Articles

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

Sort by
Same author

Large language model derived regular expressions for sleep phenotyping from electronic health record: a feasibility study.

Sleep advances : a journal of the Sleep Research Society·2026
Same author

Hydrodynamic characterization and interception-entrainment rates of the DSM-flux - a new technology to monitor CSOs.

Journal of environmental management·2026
Same author

PANDA pediatric arousal neural detection architecture.

NPJ digital medicine·2026
Same author

Antibodies against influenza A/H1N1pdm2009 and B/Victoria strains but not A/H3N2 are increased in recent onset type 1 narcolepsy versus matched controls.

medRxiv : the preprint server for health sciences·2026
Same author

Redefining hypersomnia disorders in the context of psychiatry.

L'Encephale·2026
Same author

To diagnose Narcolepsy type 1 after a negative Multiple Sleep Latency Test: the contribution of systematic hypocretin measurement.

Sleep·2026

Related Experiment Video

Updated: Aug 3, 2025

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

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis.

Alexander Neergaard Zahid, Poul Jennum, Emmanuel Mignot

    IEEE Transactions on Bio-Medical Engineering
    |April 7, 2023
    PubMed
    Summary

    This study introduces an automated deep neural network model for detecting sleep events like arousals and breathing disturbances. The joint model outperforms single-event models, offering a more accurate and efficient approach to clinical sleep analysis.

    More Related Videos

    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.8K
    Polygraphic Recording Procedure for Measuring Sleep in Mice
    08:45

    Polygraphic Recording Procedure for Measuring Sleep in Mice

    Published on: January 25, 2016

    23.9K

    Related Experiment Videos

    Last Updated: Aug 3, 2025

    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
    Design and Analysis for Fall Detection System Simplification
    08:05

    Design and Analysis for Fall Detection System Simplification

    Published on: April 6, 2020

    10.8K
    Polygraphic Recording Procedure for Measuring Sleep in Mice
    08:45

    Polygraphic Recording Procedure for Measuring Sleep in Mice

    Published on: January 25, 2016

    23.9K

    Area of Science:

    • Sleep Medicine
    • Artificial Intelligence
    • Computational Neuroscience

    Background:

    • Manual scoring of sleep events (arousals, leg movements, sleep disordered breathing) in clinical sleep analysis exhibits significant inter-scorer variability.
    • Accurate detection of these events is crucial for diagnosing sleep disorders.

    Purpose of the Study:

    • To investigate the efficacy of an automated deep neural network for detecting multiple sleep events simultaneously.
    • To compare the performance of a joint detection model (trained on all events) against event-specific models.
    • To assess the correlation of automated event detection with manual annotations and compare with existing state-of-the-art models.

    Main Methods:

    • A deep neural network model was trained on 1653 sleep recordings and tested on 1000 separate recordings.
    • The model was evaluated in both a joint detection configuration and as individual single-event models.
    • Performance was quantified using F1 scores, correlation coefficients (r^2) with manual annotations, and temporal difference metrics.

    Main Results:

    • The joint detection model achieved higher F1 scores (0.70 for arousals, 0.63 for leg movements, 0.62 for sleep disordered breathing) compared to single-event models.
    • Computed event indices showed strong positive correlations with manual annotations (r^2 = 0.73–0.78).
    • The joint model demonstrated improved accuracy based on temporal difference metrics and outperformed previous state-of-the-art models with a 97.5% reduction in size.

    Conclusions:

    • An automated joint detection model effectively identifies arousals, leg movements, and sleep disordered breathing events with high accuracy.
    • This deep learning approach offers a reliable and more consistent alternative to manual scoring in sleep analysis.
    • The proposed model provides a significant advancement in automated sleep event detection, balancing performance with substantial model size reduction.