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

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.

Piezoelectric rubber sheet sensor: a promising tool for home sleep apnea testing

“…In this study, we developed an unobtrusive method for HSAT utilizing a piezoelectric rubber sheet sensor. We designated algorithms to extract respiration and ballistocardiogram signals, allowing for the scoring of the respiratory event index (REI) [16,17], as well as measuring the frequency (Fcv) of cyclic variation of heart rate (CVHR) [7,8,18]. The performance of REI and Fcv in classifying sleep apnea severity was assessed using the apnea-hypopnea index (AHI) obtained from the simultaneous polysomnogram as a reference standard.…”

“…Generally, it is believed that the accuracy of sleep apnea detection improves with an increased number of signals measured [ 15 ]. However, considering the convenience of HSAT, it is desirable to minimize the number of sensors used and the effort required to wear them [ 16 ]. Thus, the optimal signal and measurement method should be selected by considering the tradeoff between accuracy and convenience.…”