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

Sleep Disordered Breathing and Decreased Bone Mineral Density: Masking Effects of Body Mass Index.

Obesity facts·2026

Same author

The Prodromal Synucleinopathy Rating Scale: An Assessment in Patients With REM Sleep Behavior Disorder.

Neurology·2026

Same author

Overnight sleep features and next-morning brain metabolism in older adults.

Sleep medicine·2026

Same author

Heart rate and vascular responses to spontaneous arousals following experimental sleep restriction.

American journal of physiology. Heart and circulatory physiology·2026

Same author

Ru-SATED scale and Sleep Health Index: A systematic review of two leading multidimensional sleep health measures and frameworks across the globe.

Sleep medicine reviews·2026

Same author

Proceedings From the Minnesota Learning Health Systems Symposium: The Role of Academic Partnership and (De)Implementation Science.

Mayo Clinic proceedings·2026

Search research articles

Related Experiment Video

Updated: Jan 17, 2026

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

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

Published on: December 6, 2016

Deep Neural Network Algorithm Using the Electrocardiogram for Detection of Obstructive Sleep Apnea.

Naima Covassin¹, Kan Liu¹, Jan Bukartyk¹

¹Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

|September 17, 2025

Summary

A new artificial intelligence (AI)-ECG algorithm effectively screens for obstructive sleep apnea (OSA) using routine electrocardiograms. This AI-ECG tool shows promise for widespread, low-cost OSA screening, particularly in females.

Keywords:

artificial intelligence convolutional neural network electrocardiogram machine learning obstructive 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

A Model to Simulate Clinically Relevant Hypoxia in Humans

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

Related Experiment Videos

Last Updated: Jan 17, 2026

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

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

Published on: December 6, 2016

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

A Model to Simulate Clinically Relevant Hypoxia in Humans

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

Area of Science:

Cardiology
Sleep Medicine
Artificial Intelligence

Background:

Obstructive sleep apnea (OSA) is highly prevalent but often underdiagnosed.
Effective screening tools are crucial for early detection and management of OSA.

Purpose of the Study:

To develop and evaluate a machine learning-powered algorithm for identifying OSA from 12-lead electrocardiograms (ECG).
To assess the diagnostic performance of the AI-ECG model in a large clinical population.

Main Methods:

A deep convolutional neural network model was trained to detect OSA from 12-lead ECG data of 11,299 patients.
The model's predictive performance was evaluated using the area under the receiver-operating characteristic curve (AUC).
Performance was assessed in the overall population and stratified by sex.

Main Results:

The AI-ECG model achieved an AUC of 0.80 for OSA detection in the test sample.
The model demonstrated higher discriminatory performance in females (AUC: 0.82) compared to males (AUC: 0.73).
The algorithm's predictive ability remained robust across various conditions and ECG abnormalities.

Conclusions:

The developed AI-ECG model shows significant potential as a non-invasive screening tool for OSA.
This technology could facilitate widespread, low-cost OSA screening, improving early diagnosis and treatment initiation.
The model's enhanced performance in females warrants further investigation and highlights its potential clinical utility.