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

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

Lipids and glucose fluctuations are associated with clinically diagnosed depression in older adults with type 2 diabetes.

Frontiers in endocrinology·2026

Same author

Isolation enhancement in a compact 2.4 GHz antenna pair for radar systems.

Scientific reports·2026

Same author

Solvent- and Hydrogen-Free Upcycling of Polystyrene by Layered Double Oxides.

Small (Weinheim an der Bergstrasse, Germany)·2026

Same author

Mechanical Ball Milling as a Green Strategy for Lignocellulose Valorization: A Review.

Chemical record (New York, N.Y.)·2026

Same author

Effect of insoles with different cushioning properties on knee impact after radial tear of posterior horn of medial meniscus: a finite element analysis.

BMC musculoskeletal disorders·2026

Same author

m<sup>6</sup>A-mediated CABLES1 translation potentiates tumor radioresistance via facilitating nonhomologous end-joining repair.

Cell death and differentiation·2026

Same journal

Quantitative single-cell analysis of PML-RARα oncogene-induced DNA damage along cell cycle progression.

Methods (San Diego, Calif.)·2026

Same journal

Cilia SubQ: a modular suite of semi- and fully automated pipelines for analysis of primary cilia and ciliary subdomains.

Methods (San Diego, Calif.)·2026

Same journal

Projective invariant of surface ratio: application to pupil measurement through simulations and proof-of-concept recordings.

Methods (San Diego, Calif.)·2026

Same journal

A quantitative radiographic framework for longitudinal monitoring of additively manufactured biodegradable scaffolds with graded tantalum reinforcement.

Methods (San Diego, Calif.)·2026

Same journal

An accessible, absorbance-based plate reader assay to assess cumulative exposure of blood plasma & serum to thawed conditions.

Methods (San Diego, Calif.)·2026

Same journal

EC-isHCR: A rapid method for in situ hybridization chain reaction in diverse animal samples.

Methods (San Diego, Calif.)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Sep 6, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

Accurate contactless sleep apnea detection framework with signal processing and machine learning methods.

Zhongxu Zhuang¹, Fengxia Wang¹, Xuan Yang¹

¹Nanjing University of Science and Technology, Nanjing, China.

Methods (San Diego, Calif.)

|July 5, 2022

Summary

This summary is machine-generated.

This study introduces a radar-based system for detecting sleep apnea, improving sleep quality assessment and disease diagnosis. The framework achieves high accuracy using advanced signal processing and machine learning techniques.

Keywords:

FMCW Radar Machine learning Sleep apnea

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: Sep 6, 2025

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

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:

Biomedical Engineering
Sleep Medicine
Signal Processing

Background:

Sleep apnea detection is crucial for sleep quality and diagnosing cardiovascular diseases.
Radar-based non-contact vital sign monitoring shows promise for sleep apnea detection.
Current radar-based methods require improved detection accuracy.

Purpose of the Study:

To propose a novel sleep apnea detection framework utilizing Frequency-Modulated Continuous Wave (FMCW) radar.
To enhance the accuracy of sleep apnea detection through advanced signal processing and machine learning.

Main Methods:

An FMCW radar system recorded overnight sleep data, validated against polysomnography (PSG).
Investigated signal processing techniques to extract accurate respiratory signals from raw radar data, addressing signal discontinuity.
Employed machine learning algorithms for classifying apneic and non-apneic events based on extracted respiratory signal features.

Main Results:

The proposed framework achieved a classification accuracy of 95.53%.
Key performance metrics include sensitivity of 72.60%, specificity of 97.32%, Kappa of 0.68, and F-score of 0.84.
Demonstrated effective classification of sleep apnea events using radar-derived respiratory signals.

Conclusions:

The developed FMCW radar-based framework offers a promising non-contact solution for sleep apnea detection.
The integration of signal processing and machine learning significantly improves detection performance.
This system has the potential to enhance sleep quality assessment and diagnostic capabilities for related health conditions.