A wearable device for at-home obstructive sleep apnea assessment: State-of-the-art and research challenges

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.

Automated summary

In the past 3 years, the focus of medical resources has been on COVID-19, making it challenging to diagnose sleep disorders like OSA due to a shortage of medical staff and equipment. As a result, alternative at-home OSA detection solutions have gained more attention. This study reviews the latest assessment techniques for out-of-center detection of OSA characteristics and the progress in implementing data acquisition, processing, and machine learning for early detection of severe OSA levels.