English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

A level-IV home-based sleep apnea monitoring system that utilizes alternative sensors, such as dual respiratory inductance plethysmography (RIP) belts, is proposed to promote routine apnea monitoring. Notably, continued excursion may occur in RIP belt signals, owing to the imperfect relationship between thoracic and abdominal movements during obstructive events. Therefore, we propose a novel algorithm to detect obstructive apnea based on an obstructive reciprocal divergence (ORD) continued excursion model and to explore the possibility of a multistage breathing-effort evaluation model using only RIP signals. Using the developed approach, we detected obstructive sleep apnea with a high accuracy of 99.83 ± 0.71% and a slight reduction of 73.34 ± 28.35% in hypopnea performance with overall combined objective metrics of 89.38 ± 10.53%. We found that introducing many stages improves specificity (p <; 0.001). Furthermore, apart from apneic detection, we detected subtle changes in RIP signals qualitatively, which can help represent the inspiratory flow limitation (IFL) of the RIP. This study was validated by predicting an apnea hypopnea index (AHI) based on paradoxical breathing during sleep. A strong exponential relationship was observed between the proposed parameter based on the number of transitions with AHI ( R<sup>2</sup>=0.98; p <; 0.001). The proposed approach can assist sleep technologists in characterizing obstructive and nonobstructive apneic events. Moreover, ORD is competent for further quantitative and qualitative IFL analysis and will significantly benefit the automated IFL detection system studies.

Three-Stage Breathing Effort Quantification for Obstructive Sleep Apnea Detection Based on Thoracic and Abdominal Movement Signals