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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...

Assessment of Ventilation II: Respiratory Depth and Rhythm

Assessment of Ventilation II: Respiratory Depth and Rhythm

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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Asthma Detection Research Based on Voice Signal Processing and Machine Learning

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Real-Time Detection of Sleep Apnea Based on Breathing Sounds and Prediction Reinforcement Using Home Noises:

Vu Linh Le¹, Daewoo Kim¹, Eunsung Cho¹

¹ASLEEP Inc, Seoul, Republic of Korea.

Journal of Medical Internet Research

|February 22, 2023

Summary

This summary is machine-generated.

This study developed a real-time sound-based model to detect obstructive sleep apnea (OSA) events using smartphone audio, achieving 86% accuracy in noisy home environments. This non-contact monitoring aids in OSA diagnosis and management.

Keywords:

OSA detection artificial intelligence audio deep learning diagnostic home care home technology prediction model sleep apnea sound

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Area of Science:

Biomedical Engineering
Sleep Medicine
Artificial Intelligence in Healthcare

Background:

Multinight monitoring is crucial for diagnosing and managing obstructive sleep apnea (OSA).
Real-time detection of OSA in noisy home environments is essential for effective management.
Sound-based OSA assessment using smartphones offers a promising non-contact home monitoring solution.

Purpose of the Study:

To develop a predictive model for real-time obstructive sleep apnea (OSA) detection.
To ensure the model's effectiveness in diverse home environments with ambient noise.
To enable non-contact, smartphone-integrated OSA monitoring at home.

Main Methods:

Utilized 1018 polysomnography (PSG) audio datasets and 297 synced smartphone audio datasets.
Incorporated a dataset of 22,500 home noises to enhance model robustness.
Trained a model to predict sleep breathing events (apneas, hypopneas) from breathing sounds, validated by epoch-by-epoch accuracy and AHI classification.

Main Results:

Achieved 86% epoch-by-epoch accuracy and a 0.75 macro F1-score for 3-class OSA event detection.
Model demonstrated high accuracy for 'no-event' (92%) and 'apnea' (84%), with 51% for 'hypopnea'.
OSA severity classification (AHI≥15) showed 0.85 sensitivity and 0.84 specificity.

Conclusions:

A real-time, epoch-by-epoch OSA detection model effective in noisy home settings was developed.
Further research is required to validate the utility of multinight monitoring and real-time diagnostic technologies in home-based OSA care.