Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Understanding Sleep

Understanding Sleep

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...

Sleep Apnea

Sleep Apnea

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...

You might also read

Related Articles

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

Sort by

Same author

Analysis of federated learning on non-independent and identically distributed sleep data.

Physiological measurement·2026

Same author

Quality of Sleep Data Validation From the Xiaomi Mi Band 5 Against Polysomnography: Comparison Study.

Journal of medical Internet research·2023

Same author

Computer-assisted analysis of polysomnographic recordings improves inter-scorer associated agreement and scoring times.

PloS one·2022

Same author

Pharmacologic and Nonpharmacologic Treatment of Restless Legs Syndrome.

Sleep medicine clinics·2022

Same author

Sleep-related leg movements in obstructive sleep apnea: definitions, determinants, and clinical consequences.

Sleep medicine·2020

Same author

Pharmacologic and Nonpharmacologic Treatment of Restless Legs Syndrome.

Sleep medicine clinics·2020

Same journal

Analysis of strength degradation of coal and rock masses and stability of mined areas under long term immersion environment.

PloS one·2026

Same journal

Biogenic Silver-Selenium nanocomposite with anticancer activity and potent efficacy against vancomycin-resistant Staphylococcus aureus.

PloS one·2026

Same journal

Preparation and physicochemical characterization of a biodegradable chitosan/carboxymethyl cellulose hydrogel synthesized in NaOH/urea medium.

PloS one·2026

Same journal

Action-guilt, survivor-guilt, and depression in combat-related PTSD.

PloS one·2026

Same journal

Explainable machine learning for predicting activities of daily living at discharge in stroke patients: A retrospective study using SHAP interpretability.

PloS one·2026

Same journal

Deep learning based two-way feature depiction model for brain tumor detection.

PloS one·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 29, 2026

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Published on: April 18, 2018

Inter-database validation of a deep learning approach for automatic sleep scoring.

Diego Alvarez-Estevez^1,2, Roselyne M Rijsman¹

¹Sleep Center, Haaglanden Medisch Centrum, The Hague, South-Holland, The Netherlands.

|August 16, 2021

Summary

This summary is machine-generated.

Developing generalizable automatic sleep staging algorithms is challenging. An ensemble deep learning approach improved model performance across diverse public sleep staging databases, enhancing generalization capabilities.

More Related Videos

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

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

Related Experiment Videos

Last Updated: Jun 29, 2026

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Noninvasive, High-throughput Determination of Sleep Duration in Rodents

Published on: April 18, 2018

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

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

Area of Science:

Artificial Intelligence
Biomedical Engineering
Computational Neuroscience

Background:

Automatic sleep staging is crucial for diagnosing sleep disorders.
Inter-database generalization of sleep staging algorithms is hindered by data variability and privacy concerns.
Existing deep learning models often struggle to generalize across different sleep datasets.

Purpose of the Study:

To develop a generalizable deep learning approach for automatic sleep staging.
To evaluate the generalization performance of the proposed method on diverse public sleep staging databases.
To investigate the effectiveness of an ensemble-based approach for improving inter-database generalization.

Main Methods:

A general deep learning network architecture for automatic sleep staging was developed and tested with various preprocessing and architectural variants.
The prediction capabilities were evaluated on six public sleep staging datasets, assessing both local and external dataset generalization.
A novel ensemble-based approach combining individual local models was proposed and evaluated.

Main Results:

The CNN_LSTM_5 neural network variant achieved an average kappa score of 0.80 on independent local testing sets.
Individual local models showed a decrease in performance (average kappa 0.54) when predicting external datasets.
The proposed ensemble-based approach improved the average kappa score to 0.62 on external dataset prediction scenarios.
This study represents the largest validation of automatic sleep staging generalization across external databases to date.

Conclusions:

The developed deep learning method demonstrates good general performance, comparable to human agreement levels and state-of-the-art methods.
The ensemble-based approach offers a flexible and scalable solution for enhancing sleep staging algorithm generalization.
This method preserves data locality while improving the system's ability to generalize across different datasets.