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

641
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...
641
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

1.8K
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:
1.8K
Understanding Sleep01:11

Understanding Sleep

704
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...
704

You might also read

Related Articles

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

Sort by
Same author

A transendocardial delivery and intracardiac ultrasound irradiation treatment catheter.

Drug delivery·2013
Same author

The MDS-UPDRS Part II (motor experiences of daily living) resulted useful for assessment of disability in Parkinson's disease.

Parkinsonism & related disorders·2013
Same author

C3orf58, a novel paracrine protein, stimulates cardiomyocyte cell-cycle progression through the PI3K-AKT-CDK7 pathway.

Circulation research·2013
Same author

Morphological weighted penalized least squares for background correction.

The Analyst·2013
Same author

[Management of Kasabach-Merritt syndrome by drug therapy and surgery].

Zhonghua zheng xing wai ke za zhi = Zhonghua zhengxing waike zazhi = Chinese journal of plastic surgery·2013
Same author

Chemical constituents of fine particulate air pollution and pulmonary function in healthy adults: the Healthy Volunteer Natural Relocation study.

Journal of hazardous materials·2013

Related Experiment Video

Updated: Oct 1, 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

688

An Improved Neural Network Based on SENet for Sleep Stage Classification.

Jing Huang, Lifeng Ren, Xiaokang Zhou

    IEEE Journal of Biomedical and Health Informatics
    |March 8, 2022
    PubMed
    Summary

    This study introduces an automated sleep staging model using electroencephalogram (EEG) data and a hidden Markov model (HMM). The novel attention mechanism improves accuracy for sleep quality analysis.

    More Related Videos

    Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood
    08:20

    Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood

    Published on: October 2, 2019

    12.1K
    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

    Related Experiment Videos

    Last Updated: Oct 1, 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

    688
    Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood
    08:20

    Measuring Neural Mechanisms Underlying Sleep-Dependent Memory Consolidation During Naps in Early Childhood

    Published on: October 2, 2019

    12.1K
    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

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Computer Science

    Background:

    • Manual sleep staging from polysomnography is time-consuming and subjective.
    • Automated sleep staging using electroencephalogram (EEG) data is crucial for efficient sleep quality analysis.

    Purpose of the Study:

    • To develop an improved automatic sleep staging model using single-channel EEG.
    • To enhance feature fusion and classification performance through an improved attention module and Hidden Markov Model (HMM).

    Main Methods:

    • Feature extraction using two convolution kernels of different scales from single-channel EEG.
    • Feature fusion via an improved attention module based on Squeeze-and-Excitation Networks (SENet).
    • Sleep stage classification refined by a Hidden Markov Model (HMM) incorporating sleep transition rules.

    Main Results:

    • The model achieved high accuracy on the sleep-EDFx dataset: 84.6% (Fpz-Cz) and 82.3% (Pz-Oz).
    • Kappa coefficients of 0.79 (Fpz-Cz) and 0.76 (Pz-Oz) indicate excellent agreement.
    • The improved attention module and HMM significantly boosted classification performance, particularly for the challenging N1 stage.

    Conclusions:

    • The proposed automatic sleep staging model demonstrates excellent performance and efficiency.
    • The integration of an improved attention mechanism and HMM effectively refines sleep stage classification.
    • This method offers a promising approach for objective and automated sleep quality assessment.