Unobtrusively Detecting Apnea and Hypopnea Events via a Hydraulic Bed Sensor
David Heise, Ruhan Yi, Laurel Despins
Abstract
David Heise, Ruhan Yi, Laurel Despins
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.
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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