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

We conducted a pilot study to evaluate the accuracy of a custom built non-contactpressure-sensitive device in diagnosing obstructive sleep apnea (OSA) severity as an alternative toin-laboratory polysomnography (PSG) and a Type 3 in-home sleep apnea test (HSAT). Fourteenpatients completed PSG sleep studies for one night with simultaneous recording from ourload-cell-based sensing device in the bed. Subjects subsequently installed pressure sensors in theirbed at home and recorded signals for up to four nights. Machine learning models were optimized toclassify sleep apnea severity using a standardized American Academy of Sleep Medicine (AASM)scoring of the gold standard studies as reference. On a per-night basis, our model reached a correctOSA detection rate of 82.9% (sensitivity = 88.9%, specificity = 76.5%), and OSA severity classificationaccuracy of 74.3% (61.5% and 81.8% correctly classified in-clinic and in-home tests, respectively).There was no difference in Apnea Hypopnea Index (AHI) estimation when subjects wore HSATsensors versus load cells (LCs) only (p-value = 0.62). Our in-home diagnostic system providesan unobtrusive method for detecting OSA with high sensitivity and may potentially be used forlong-term monitoring of breathing during sleep. Further research is needed to address the lowerspecificity resulting from using the highest AHI from repeated samples.

Design and Evaluation of a Non-Contact Bed-Mounted Sensing Device for Automated In-Home Detection of Obstructive Sleep Apnea: A Pilot Study