Citation

Tran NT, Tran HN, Mai AT. Front. Neurol. 2023; 14: e1123227.

Copyright

(Copyright © 2023, Frontiers Research Foundation)

DOI

10.3389/fneur.2023.1123227

PMID

36824418

PMCID

PMC9941521

Abstract

In the last 3 years, almost all medical resources have been reserved for the screening and treatment of patients with coronavirus disease (COVID-19). Due to a shortage of medical staff and equipment, diagnosing sleep disorders, such as obstructive sleep apnea (OSA), has become more difficult than ever. In addition to being diagnosed using polysomnography at a hospital, people seem to pay more attention to alternative at-home OSA detection solutions. This study aims to review state-of-the-art assessment techniques for out-of-center detection of the main characteristics of OSA, such as sleep, cardiovascular function, oxygen balance and consumption, sleep position, breathing effort, respiratory function, and audio, as well as recent progress in the implementation of data acquisition and processing and machine learning techniques that support early detection of severe OSA levels.

Language: en

Keywords

COVID-19; machine learning; OSA; SCOPER; wearable device