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Related Concept Videos

Sleep Apnea01:21

Sleep Apnea

145
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...
145
Assessment of Ventilation II: Respiratory Depth and Rhythm01:29

Assessment of Ventilation II: Respiratory Depth and Rhythm

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Respiratory Depth
Respiratory depth measures the volume of air inhaled or exhaled during a breath. It can vary from shallow to deep and typically remains consistent when a person is at rest or asleep. Occasionally, individuals will automatically inhale deeply, known as sighing, which inflates the lungs with more air than normal breathing.
To assess respiratory depth, observe the degree of chest excursion or movement:
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Related Experiment Video

Updated: Jun 24, 2025

Multi-Modal Home Sleep Monitoring in Older Adults
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Towards automatic home-based sleep apnea estimation using deep learning.

Gabriela Retamales1, Marino E Gavidia1, Ben Bausch1

  • 1Luxembourg Centre for Systems Biomedicine, University of Luxembourg, L-4367, Belvaux, Luxembourg.

NPJ Digital Medicine
|June 1, 2024
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Summary
This summary is machine-generated.

A new method estimates sleep apnea severity at home using wearable sensors, improving diagnosis comfort and accessibility. This approach aids physicians in identifying sleep-disordered breathing, enhancing patient care.

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Area of Science:

  • Sleep Medicine
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • Sleep disorders like apnea and hypopnea involve airway obstruction, impacting health.
  • Polysomnography (PSG) is the standard for diagnosing sleep apnea severity via the Apnea-Hypopnea Index (AHI) but is costly and uncomfortable for long-term monitoring.
  • Early detection and treatment of sleep apnea are crucial for reducing morbidity and mortality.

Purpose of the Study:

  • To introduce DRIVEN, a novel method for estimating AHI and detecting sleep events (apnea, hypopnea, wakefulness) at home using wearable sensors.
  • To provide a comfortable and cost-effective alternative to traditional PSG for long-term sleep apnea monitoring.
  • To assist physicians in diagnosing sleep apnea severity through accessible home-based measurements.

Main Methods:

  • Utilized a combination of deep convolutional neural networks and a light-gradient-boost machine for classification.
  • Employed publicly available data from three large sleep studies, encompassing 14,370 recordings.
  • Integrated data from wearable sensors measuring abdominal movement, thoracic movement, or pulse oximetry.

Main Results:

  • DRIVEN achieved 72.4% correct classification of patients into four AHI severity classes using just two sensors.
  • 99.3% of patients were either correctly classified or placed within one class of their true AHI severity.
  • Demonstrated a favorable balance between diagnostic performance and patient comfort.

Conclusions:

  • DRIVEN offers a feasible solution for unsupervised, long-term home monitoring of sleep apnea.
  • The method has the potential to significantly reduce healthcare costs associated with sleep studies.
  • Implementation of DRIVEN can lead to improved patient care and earlier intervention for sleep-disordered breathing.