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

225
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...
225

You might also read

Related Articles

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

Sort by
Same author

Deep Learning-Based Alzheimer's Disease Detection from Multi-Channel EEG Using Fused Time-Frequency Image Grids.

Diagnostics (Basel, Switzerland)·2026
Same author

Enhancing Schizophrenia Diagnosis Through Multi-View EEG Analysis: Integrating Raw Signals and Spectrograms in a Deep Learning Framework.

Clinical EEG and neuroscience·2025
Same author

Multi-task learning for arousal and sleep stage detection using fully convolutional networks.

Journal of neural engineering·2023
See all related articles

Related Experiment Video

Updated: Sep 17, 2025

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

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.8K

ModelS4Apnea: leveraging structured state space models for efficient sleep apnea detection from ECG signals.

Hasan Zan1

  • 1Department of Computer Engineering, Mardin Artuklu University, Mardin, Turkey.

Physiological Measurement
|July 3, 2025
PubMed
Summary

ModelS4Apnea, a deep learning framework, accurately detects sleep apnea from ECG data using structured state space models. This efficient method offers high performance for wearable devices and clinical use.

Keywords:
Apnea-ECG datasetS4Structured State Space Modelapnea detectiondeep learning

More Related Videos

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits
10:25

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

6.1K
A Model to Simulate Clinically Relevant Hypoxia in Humans
09:54

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

9.0K

Related Experiment Videos

Last Updated: Sep 17, 2025

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

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

7.8K
Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits
10:25

Multi-system Monitoring for Identification of Seizures, Arrhythmias and Apnea in Conscious Restrained Rabbits

Published on: March 27, 2021

6.1K
A Model to Simulate Clinically Relevant Hypoxia in Humans
09:54

A Model to Simulate Clinically Relevant Hypoxia in Humans

Published on: December 22, 2016

9.0K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence
  • Cardiology

Background:

  • Sleep apnea is a prevalent disorder with serious health implications.
  • Accurate and efficient detection methods are crucial for managing sleep apnea.
  • Electrocardiogram (ECG) signals offer a non-invasive source for sleep apnea detection.

Purpose of the Study:

  • To introduce ModelS4Apnea, a novel deep learning framework for automated sleep apnea detection.
  • To leverage structured state space models (S4) for enhanced temporal modeling in ECG analysis.
  • To achieve high accuracy and efficiency in sleep apnea detection from ECG spectrograms.

Main Methods:

  • Developed a deep learning framework (ModelS4Apnea) integrating CNNs and S4 models.
  • Utilized ECG spectrograms as input for the deep learning model.
  • Trained and evaluated the model on the Apnea-ECG dataset.

Main Results:

  • Achieved high performance metrics: 0.933 accuracy, 0.912 F1-score, 0.916 sensitivity, 0.944 specificity.
  • Demonstrated superior performance compared to existing methods on the Apnea-ECG dataset.
  • Showcased computational efficiency with fewer parameters and reduced training time.

Conclusions:

  • ModelS4Apnea provides a robust and efficient solution for sleep apnea detection.
  • The framework's low memory footprint and fast inference are suitable for real-time applications and wearables.
  • The model's ability to perform per-recording classification highlights its diagnostic potential.