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

192
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...
192
Sleep-Wake Cycles01:24

Sleep-Wake Cycles

1.3K
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:
1.3K
Stages of General Anesthesia01:22

Stages of General Anesthesia

444
Various sedation levels offer significant advantages in facilitating procedural interventions for patients undergoing medical or invasive surgical procedures. These levels span from anxiolysis to general anesthesia, providing a spectrum of sedative effects to cater to specific patient needs. Anxiolysis reduces anxiety and is achieved through minimal sedation, enabling patients to remain awake and responsive while feeling more at ease during the procedure. This level can benefit minor...
444

You might also read

Related Articles

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

Sort by
Same author

Noradrenergic infraslow rhythm during sleep is the critical link between heart-rate dynamics and memory consolidation.

bioRxiv : the preprint server for biology·2026
Same author

Effects of lemborexant and zolpidem on sleep electroencephalography in older adults with insomnia: a randomized trial.

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

Classifying Sleep Slow Oscillations in Low Density EEG.

Neuroinformatics·2026
Same author

A user's introduction to an algorithmic method to identify space-time profiles of sleep slow oscillations: dataset constraints, case-use examples, and open-source code.

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

Validation of spectral sleep scoring with polysomnography using forehead EEG device.

Frontiers in sleep·2025
Same author

Individual differences in medial temporal lobe functional network architecture predict the capacity for sleep-related consolidation of emotional memories in older adults.

Sleep·2025
Same journal

Benchmarking the Robustness of Autonomous Driving to Environmental Illusions: A Lane Perception Perspective.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Learning Topology-Aware Representations via Test-Time Adaptation for Anomaly Segmentation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

TraGraph-GS: Trajectory Graph-based Gaussian Splatting for Arbitrary Large-Scale Scene Rendering.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

SWIFT: A Small-World Interaction Framework for Flow-Aware Trajectory Prediction in Autonomous Driving.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

HardFlow: Hard-Constrained Sampling for Flow-Matching Models Via Trajectory Optimization.

IEEE transactions on pattern analysis and machine intelligence·2026
Same journal

Industrial Brain: Self-Evolving Neuro-Symbolic Autonomy with Causal Resilience for Cyber-Physical Systems.

IEEE transactions on pattern analysis and machine intelligence·2026
See all related articles

Related Experiment Video

Updated: Jul 3, 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

514

A Multi-Level Interpretable Sleep Stage Scoring System by Infusing Experts' Knowledge Into a Deep Network

Hamid Niknazar, Sara C Mednick

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |February 15, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an interpretable deep learning system for electroencephalogram (EEG) sleep stage scoring. The transparent model enhances trust in AI for medical decision-making.

    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

    7.7K
    Polygraphic Recording Procedure for Measuring Sleep in Mice
    08:45

    Polygraphic Recording Procedure for Measuring Sleep in Mice

    Published on: January 25, 2016

    23.7K

    Related Experiment Videos

    Last Updated: Jul 3, 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

    514
    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
    Polygraphic Recording Procedure for Measuring Sleep in Mice
    08:45

    Polygraphic Recording Procedure for Measuring Sleep in Mice

    Published on: January 25, 2016

    23.7K

    Area of Science:

    • Artificial Intelligence
    • Neuroscience
    • Biomedical Engineering

    Background:

    • Deep learning models offer efficiency in signal processing but suffer from a lack of interpretability, hindering high-risk applications like medical decision-making.
    • The 'black box' nature of current algorithms is a significant barrier to the clinical adoption of AI in healthcare.
    • Interpretable AI is crucial for building trust and facilitating the translation of AI tools into real-world medical applications.

    Purpose of the Study:

    • To design and develop an interpretable deep learning system for classifying electroencephalogram (EEG) signals for sleep stage scoring.
    • To address the 'black box' problem in AI by creating a transparent system for time series analysis.
    • To create a foundation for transparent AI systems in medical decision-making.

    Main Methods:

    • Developed a novel interpretable deep neural network incorporating a kernel-based convolutional layer.
    • The kernel-based layer was designed using principles from human expert visual analysis of polysomnographic records for sleep scoring.
    • The system's interpretability was assessed across four levels, from signal microstructure (kernels) to macrostructure (stage transitions).

    Main Results:

    • The proposed interpretable deep learning system achieved superior performance compared to previous studies in EEG sleep stage scoring.
    • The system's learned features, visualized through trained kernels, align with established knowledge used by human experts.
    • Analysis revealed consistency between the AI's decision-making process and expert-derived sleep scoring principles.

    Conclusions:

    • The developed interpretable deep learning system successfully performs sleep stage scoring with enhanced transparency.
    • The system's interpretability facilitates understanding of its decision-making process, aligning with expert knowledge.
    • This work represents a significant step towards developing trustworthy and transparent AI for medical applications.