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

Sleep Apnea01:21

Sleep Apnea

871
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...
871
Pulse rhythm01:30

Pulse rhythm

1.6K
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
1.6K
Holter Monitor: 24-Hour Monitoring01:23

Holter Monitor: 24-Hour Monitoring

5.4K
Holter monitoring is a continuous electrocardiography (ECG) recording that tracks the heart's electrical activity over an extended period, generally 24 to 48 hours. This noninvasive diagnostic tool detects irregular heart rhythms that may not be captured during a standard ECG performed in a clinical setting.DeviceThe Holter monitor is a portable, small device connected to several electrodes on the patient's chest. These electrodes detect the heart's electrical signals and transmit them to the...
5.4K
Pulse Oximetry01:24

Pulse Oximetry

1.5K
Pulse oximetry, or SpO2, is a non-invasive method for continuously monitoring arterial oxygen saturation (SaO2). This procedure involves attaching a probe or sensor to the patient's fingertip, forehead, earlobe, or nose bridge. The sensor works by detecting changes in oxygen saturation levels through light signals generated by the oximeter and reflected by the pulsing blood under the probe.
Purpose
Average SpO2 values are greater than 95%. If the readings fall below 90%, it indicates that...
1.5K

You might also read

Related Articles

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

Sort by
Same author

Ear-Worn Inertial Sensors Can Predict Gait Metrics and Reconstruct Vertical Ground Reaction Force Curves During Running.

Journal of applied biomechanics·2026
Same author

Randomised controlled trial of a very brief nurse-delivered intervention followed by a digital intervention to support medication adherence and reduce blood pressure in people prescribed treatment for hypertension in primary care: protocol for the Programme on Adherence to Medication (PAM) trial.

NIHR open research·2026
Same author

SALTS: Streamlined Adaptive Learning for Sensors Time Series.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Deep-Learning Based Segmentation of In-Ear Cardiac Sounds.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Emotion Recognition from Speech Signals by Mel-Spectrogram and a CNN-RNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

A smoking cessation smartphone app that delivers real-time 'context aware' behavioural support: the Quit Sense feasibility RCT.

Public health research (Southampton, England)·2024
Same journal

Multimodal Contrastive Spatiotemporal Self-Organizing Neural Networks for In-Home Activity Learning of Mild Cognitive Impairment.

IEEE journal of biomedical and health informatics·2026
Same journal

Integrating Multi-View Residue Graph and Protein Language Model for Cell-Penetrating Peptide Prediction via Global-Local Graph Aggregation and Cross-Attentive Fusion.

IEEE journal of biomedical and health informatics·2026
Same journal

An Ultra-Lightweight Cross-scale Attention Mamba Network for Accurate Skin Lesion Segmentation.

IEEE journal of biomedical and health informatics·2026
Same journal

Explanation-Guided Reconstruction of Missing Clinical Features for Survival Prediction in Pancreatic Cancer.

IEEE journal of biomedical and health informatics·2026
Same journal

stDGCN: A dual-augmentation graph convolutional network for identifying spatial domains with attention mechanism.

IEEE journal of biomedical and health informatics·2026
Same journal

Patient-specific Biomechanical Investigation of Percutaneous Pulmonary Valves: Towards the Integration of Routinely Acquired Clinical Data and Fluid-structure Interaction Simulations.

IEEE journal of biomedical and health informatics·2026
See all related articles

Related Experiment Video

Updated: Apr 22, 2026

Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.3K

Continuous Mobile Audio Monitoring for Sleep Apnea Detection.

Jing Han, Tong Xia, Cecilia Mascolo

    IEEE Journal of Biomedical and Health Informatics
    |April 20, 2026
    PubMed
    Summary
    This summary is machine-generated.

    Home sleep apnea screening using audio recordings from mobile sensors shows high accuracy. This method offers a promising way to improve diagnosis access and remote monitoring for sleep apnea.

    More Related Videos

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
    08:36

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

    Published on: August 8, 2019

    13.3K
    Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
    07:54

    Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

    Published on: December 6, 2016

    19.7K

    Related Experiment Videos

    Last Updated: Apr 22, 2026

    Multi-Modal Home Sleep Monitoring in Older Adults
    07:40

    Multi-Modal Home Sleep Monitoring in Older Adults

    Published on: January 26, 2019

    7.3K
    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
    08:36

    Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

    Published on: August 8, 2019

    13.3K
    Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea
    07:54

    Drug-Induced Sleep Endoscopy DISE with Target Controlled Infusion TCI and Bispectral Analysis in Obstructive Sleep Apnea

    Published on: December 6, 2016

    19.7K

    Area of Science:

    • Biomedical Engineering
    • Medical Acoustics
    • Artificial Intelligence in Medicine

    Background:

    • Audio-based sleep apnea detection offers accessible, at-home screening.
    • Tracheal and ambient microphone recordings are viable sources for respiratory sound analysis.
    • Deep learning models can effectively analyze complex acoustic patterns for medical diagnosis.

    Purpose of the Study:

    • To compare and evaluate tracheal and ambient microphone recordings for sleep apnea detection.
    • To develop and assess deep learning models for classifying sleep apnea events using audio data.
    • To investigate the performance of audio-based detection for different severities of sleep apnea.

    Main Methods:

    • Utilized the open PSG-Audio dataset (over 850 hours, 194 subjects).
    • Employed various acoustic representations and deep learning architectures.
    • Compared tracheal and ambient microphone recordings for sleep apnea classification.

    Main Results:

    • The most effective model achieved 90.8% accuracy for general sleep apnea detection.
    • Accuracies were 83.3% for hypopneic/apneic events and 75.7% for sub-categorized apneic events.
    • High sensitivity and specificity were observed for moderate (0.93/1.0) and severe (0.84/0.97) sleep apnea screening.

    Conclusions:

    • Audio-based sleep apnea detection using tracheal and ambient sounds is highly effective.
    • The developed deep learning model shows significant potential for remote sleep apnea diagnosis and monitoring.
    • This research validates the use of combined respiratory sounds from different sensors for improved accuracy.