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

ABSTRACT:

Background

Multinight monitoring can be helpful for the diagnosis and management of obstructive sleep apnea (OSA). For this purpose, it is necessary to be able to detect OSA in real time in a noisy home environment. Sound-based OSA assessment holds great potential since it can be integrated with smartphones to provide full noncontact monitoring of OSA at home.

Objective

The purpose of this study is to develop a predictive model that can detect OSA in real time, even in a home environment where various noises exist.

Methods

This study included 1018 polysomnography (PSG) audio data sets, 297 smartphone audio data sets synced with PSG, and a home noise data set containing 22,500 noises to train the model to predict breathing events, such as apneas and hypopneas, based on

SUBMITTER: Le VL 

PROVIDER: S-EPMC9996414 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature