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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...

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Related Experiment Video

Updated: Oct 2, 2025

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks.

Hector E Romero, Ning Ma, Guy J Brown

IEEE Journal of Biomedical and Health Informatics

|February 25, 2022

Summary

This summary is machine-generated.

A new smartphone-based method accurately screens for obstructive sleep apnea (OSA) using breathing sounds. This accessible approach can identify individuals needing further polysomnography (PSG) testing for diagnosis.

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Area of Science:

Medical Devices
Biomedical Engineering
Sleep Medicine

Background:

Obstructive sleep apnea (OSA) is a common condition with serious health implications.
Diagnosis relies on polysomnography (PSG), which has limited accessibility.
There is a need for cost-effective, home-based screening tools for OSA.

Purpose of the Study:

To develop and validate a novel method for screening obstructive sleep apnea (OSA) using smartphone-recorded breathing sounds.
To assess the feasibility of using deep neural networks for OSA screening in a home environment.
To provide an accessible alternative to polysomnography (PSG) for initial OSA detection.

Main Methods:

A deep neural network was trained to analyze overnight audio recordings of breathing sounds captured by smartphones.
Recordings were segmented, and each segment was classified for the presence or absence of OSA.
The apnea-hypopnea index (AHI) was estimated from OSA-positive segments to screen for the condition.

Main Results:

The smartphone-based system demonstrated good performance in screening for moderate OSA (sensitivity 0.79, specificity 0.80).
High accuracy was achieved when screening for severe OSA (sensitivity 0.78, specificity 0.93).
The system is suitable for implementation on consumer smartphones, enabling widespread home use.

Conclusions:

Smartphone-based audio analysis offers a promising, low-cost method for home screening of obstructive sleep apnea (OSA).
This technology can improve access to OSA diagnosis by identifying individuals who require formal polysomnography (PSG).
Further development could lead to widespread adoption of this accessible screening tool.