Jove
Visualize
Contact Us

Related Concept Videos

REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

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...
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

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:
Narcolepsy01:07

Narcolepsy

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.

You might also read

Related Articles

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

Sort by
Same author

Enhancing Brain Tumor Classification and Generalization Using DDPM-Generated MRI, Mutual Information and Ensemble Learning.

Technology in cancer research & treatment·2026
Same author

Sleep apnea test prediction based on Electronic Health Records.

Journal of biomedical informatics·2024
Same author

Respiration-triggered olfactory stimulation reduces obstructive sleep apnea severity: A prospective pilot study.

Journal of sleep research·2024
Same author

Handling Missing MRI Data in Brain Tumors Classification Tasks: Usage of Synthetic Images vs. Duplicate Images and Empty Images.

Journal of magnetic resonance imaging : JMRI·2023
Same author

Utilizing the TractSeg Tool for Automatic Corticospinal Tract Segmentation in Patients With Brain Pathology.

Technology in cancer research & treatment·2022
Same author

Comparing in-lab full polysomnography for diagnosing sleep apnea in children to home sleep apnea tests (HSAT) with an online video attending technician.

Sleep and biological rhythms·2022
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 Experiment Video

Updated: Jun 23, 2026

Through-the-Wall Blood Sampling Method to Minimize Sleep Disruption in Clinical Settings
06:39

Through-the-Wall Blood Sampling Method to Minimize Sleep Disruption in Clinical Settings

Published on: June 13, 2025

Data mining techniques for detection of sleep arousals.

Oren Shmiel1, Tomer Shmiel, Yaron Dagan

  • 1Department of Computer Science and Mathematics, Bar-Ilan University, Ramat-Gan 52900, Israel. oren.shmiel@live.biu.ac.il

Journal of Neuroscience Methods
|May 12, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data mining approach for automatically detecting arousals, a key factor in sleep quality. The new method accurately identifies arousals, improving sleep analysis and reducing manual scoring burdens.

More Related Videos

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring
04:54

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

Published on: November 8, 2024

Noninvasive, High-throughput Determination of Sleep Duration in Rodents
07:33

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Published on: April 18, 2018

Related Experiment Videos

Last Updated: Jun 23, 2026

Through-the-Wall Blood Sampling Method to Minimize Sleep Disruption in Clinical Settings
06:39

Through-the-Wall Blood Sampling Method to Minimize Sleep Disruption in Clinical Settings

Published on: June 13, 2025

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring
04:54

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring

Published on: November 8, 2024

Noninvasive, High-throughput Determination of Sleep Duration in Rodents
07:33

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Published on: April 18, 2018

Area of Science:

  • Sleep Science
  • Biomedical Engineering
  • Data Mining

Background:

  • Arousals significantly impact sleep quality and daytime alertness.
  • Manual arousal scoring is subjective, time-consuming, and prone to inter-scorer variability.
  • Existing automatic methods struggle with patient-specific signal variations.

Purpose of the Study:

  • To develop a data mining approach for accurate automatic arousal detection.
  • To overcome patient-specific signal variability in automatic arousal detection.
  • To establish a novel method for estimating sleep quality through arousal analysis.

Main Methods:

  • Developed 'meta-rules' from training data by analyzing correlations between arousals and physiological signals (EEG, EMG, pulse, SaO2).
  • Utilized signal projection, critical point extraction, and pattern discovery via data mining.
  • Generated patient-specific 'actual-rules' online using meta-rules for arousal detection.

Main Results:

  • The algorithm demonstrated significant correlation (R=0.88, p<0.0001) with manual scoring.
  • Achieved high performance metrics: 75.2% sensitivity and 76.5% positive predictive value.
  • Successfully adapted to patient-specific signal characteristics from different sleep labs.

Conclusions:

  • The proposed data mining technique offers a robust and accurate method for automatic arousal detection.
  • This algorithm provides a novel approach for objective sleep quality estimation.
  • The system's ability to generalize across patients and labs highlights its clinical potential.