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

REM Sleep Behavior Disorder01:15

REM Sleep Behavior Disorder

387
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...
387
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
Narcolepsy01:07

Narcolepsy

197
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.
197
Stages of Sleep01:22

Stages of Sleep

455
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...
455

You might also read

Related Articles

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

Sort by
Same author

A Deep Learning Approach for Classifying Rapid Eye Movement Sleep Behavior Disorder Using EEGNet.

Journal of clinical neurology (Seoul, Korea)·2026
Same author

Neural Basis of Action Simulation in Architectural Perception: A Multi-voxel Pattern Analysis Study.

Journal of cognitive neuroscience·2026
Same author

Re: Comments on "Potentially Inappropriate Medication Use in Patients With Parkinson's Disease: Analysis of Korean National Health Insurance Claims Data".

Journal of clinical neurology (Seoul, Korea)·2026
Same author

Deep-learning analysis of speech using mel-spectrograms for the assessment of mild cognitive impairment and Alzheimer's disease.

Journal of Alzheimer's disease : JAD·2025
Same author

Potentially Inappropriate Medication Use in Patients With Parkinson's Disease: Analysis of Korean National Health Insurance Claims Data.

Journal of clinical neurology (Seoul, Korea)·2025
Same author

Impact of Age at Narcolepsy Onset on Sleep-Onset REM Periods in the Multiple Sleep Latency Test.

Journal of clinical medicine·2025
Same journal

Misleading Anti-Yo Positivity on Immunoblot Alone: Importance of Rat-Brain Immunohistochemistry.

Journal of clinical neurology (Seoul, Korea)·2026
Same journal

Positional Elliptical Pendular Nystagmus in Inferior Olivary Pseudohypertrophy.

Journal of clinical neurology (Seoul, Korea)·2026
Same journal

Acute Small Fiber Neuropathy With Severe Autonomic Dysfunction at the Interface of Guillain-Barré Syndrome.

Journal of clinical neurology (Seoul, Korea)·2026
Same journal

Early Suspected Progressive Supranuclear Palsy-Cerebellar Type From MRI and Dopaminergic Imaging in a Patient Initially Presenting With Ataxia.

Journal of clinical neurology (Seoul, Korea)·2026
Same journal

Bilateral "Three-Quarters" Gaze Palsy Syndrome Caused by Unilateral Thalamomesencephalic Infarction.

Journal of clinical neurology (Seoul, Korea)·2026
Same journal

Diagnostic Value of Flow-Void Sign for Localizing Ventral Dural Defects in Spine MRI.

Journal of clinical neurology (Seoul, Korea)·2026
See all related articles

Related Experiment Video

Updated: Sep 9, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K

Deep-Learning-Based Automated REM Sleep Detection in Patients With REM Sleep Behavior Disorder: Is It Reliable?

Yu Jin Jung1, Sunil Kim2, Yun Ho Choi3

  • 1Department of Neurology, Kyung Hee University Hospital at Gangdong, College of Medicine, Kyung Hee University, Seoul, Korea.

Journal of Clinical Neurology (Seoul, Korea)
|August 29, 2025
PubMed
Summary
This summary is machine-generated.

An automated system using electroencephalography (EEG) and electrooculography (EOG) effectively detects rapid eye movement (REM) sleep. Performance was lower in REM sleep behavior disorder (RBD) patients, particularly those with Parkinson's disease (PD).

Keywords:
REM sleep behavior disorderREM sleep detectorREM sleep without atoniaautomated algorithm

More Related Videos

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

647
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

7.9K

Related Experiment Videos

Last Updated: Sep 9, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
10:28

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

15.4K
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

647
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

7.9K

Area of Science:

  • Neuroscience
  • Sleep Medicine
  • Artificial Intelligence in Healthcare

Background:

  • Detecting REM sleep in REM sleep behavior disorder (RBD) is challenging due to absent muscle atonia.
  • Current methods often rely on electromyography (EMG), which can be unreliable in RBD.

Purpose of the Study:

  • To develop an automated REM sleep detector using only EEG and EOG data.
  • To evaluate the detector's performance in patients with RBD using polysomnography (PSG) data.

Main Methods:

  • Utilized 310 PSG datasets from 5 hospitals, including RBD (n=200) and non-RBD (n=110) groups.
  • Employed an automated REM detection algorithm based on U-Sleep's pretrained network.
  • Subdivided data into Parkinson's disease (PD) with RBD, PD without RBD, idiopathic RBD (iRBD), and healthy controls.

Main Results:

  • The U-Sleep algorithm achieved an overall area under the receiver operating characteristic curve (AUC) of 0.90±0.14 for REM sleep detection.
  • Performance varied significantly between RBD (AUC=0.88±0.13) and non-RBD (AUC=0.93±0.14) groups (p=0.007).
  • Detection accuracy followed the order: healthy controls (0.94±0.02), PD without RBD (0.92±0.03), iRBD (0.90±0.02), and PD with RBD (0.86±0.02).

Conclusions:

  • The EEG/EOG-based automated REM sleep detector demonstrates good performance.
  • The system's accuracy is reduced in RBD patients, especially those with PD.
  • Future improvements using transfer learning and expert fine-tuning are proposed to enhance system performance.