2021Unpublished venueRequires access

Unobtrusively Detecting Apnea and Hypopnea Events via a Hydraulic Bed Sensor

David Heise, Ruhan Yi, Laurel Despins

Open publisher page 4 citations

Abstract

Disordered breathing during sleep impacts sleep quality and the perceived amount of rest obtained while also serving as a potential indicator of other health conditions or risks. Apneas and hypopneas are leading indicators of disordered breathing, often quantified by an apnea-hypopnea index (AHI). Polysomnography is the gold standard for detecting apnea and hypopnea events (and thus calculating a subject’s AHI), but despite the inconvenience of sleeping in a strange place with numerous instruments attached, polysomnography delivers only a snapshot in time and is not practical for long-term monitoring. In this work, we describe a method of detecting apnea and hypopnea events during sleep using a hydraulic bed sensor, which has proven valuable for other dimensions of long-term monitoring and early detection of illness. We compare our results to those produced by a polysomnography lab, including calculation of respiratory disturbance indices. We successfully detect 73.6% of apneas with 77.2% precision, and our calculations for apnea index (AI) and respiratory disturbance index (RDI) are precise enough to indicate the appropriate severity of sleep apnea-hypopnea syndrome (SAHS) for each of our subjects.

About this research paper

What this paper is about

Disordered breathing during sleep impacts sleep quality and the perceived amount of rest obtained while also serving as a potential indicator of other health conditions or risks. Apneas and hypopneas are leading indicators of disordered breathing, often quantified by an apnea-hypopnea index (AHI). Polysomnography is the gold standard for detecting apnea and hypopnea events (and thus calculating a subject’s AHI), but despite the inconvenience of sleeping in a strange place with numerous instruments attached, polysomnography delivers only a snapshot in time and is not practical for long-term monitoring. In this work, we describe a method of detecting apnea and hypopnea events during sleep using a hydraulic bed sensor, which has proven valuable for other dimensions of long-term monitoring and early detection of illness. We compare our results to those produced by a polysomnography lab, including calculation of respiratory disturbance indices. We successfully detect 73.6% of apneas with 77.2% precision, and our calculations for apnea index (AI) and respiratory disturbance index (RDI) are precise enough to indicate the appropriate severity of sleep apnea-hypopnea syndrome (SAHS) for each of our subjects.

Why it matters

OpenAlex reports 4 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

Disordered breathing during sleep impacts sleep quality and the perceived amount of rest obtained while also serving as a potential indicator of other health conditions or risks. Apneas and hypopneas are leading indicators of disordered breathing, often quantified by an apnea-hypopnea index (AHI). Polysomnography is the gold standard for detecting apnea and hypopnea events (and thus calculating a subject’s AHI), but despite the inconvenience of sleeping in a strange place with numerous instruments attached, polysomnography delivers only a snapshot in time and is not practical for long-term monitoring. In this work, we describe a method of detecting apnea and hypopnea events during sleep using a hydraulic bed sensor, which has proven valuable for other dimensions of long-term monitoring and early detection of illness. We compare our results to those produced by a polysomnography lab, including calculation of respiratory disturbance indices. We successfully detect 73.6% of apneas with 77.2% precision, and our calculations for apnea index (AI) and respiratory disturbance index (RDI) are precise enough to indicate the appropriate severity of sleep apnea-hypopnea syndrome (SAHS) for each of our subjects.

Key concepts: Polysomnography, Hypopnea, Apnea, Respiratory disturbance index, Medicine, Sleep apnea, Apnea–hypopnea index, Sleep disorder

Related papers

Back to paper searchBrowse research topicsOriginal source
Unobtrusively Detecting Apnea and Hypopnea Events via a Hydraulic Bed Sensor — Research Paper | ScholarLens