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Unobtrusively Detecting Apnea and Hypopnea Events via a Hydraulic Bed Sensor

机译:通过液压床传感器不引人注发地检测呼吸暂停和缺氧事件

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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.
机译:在睡眠期间呼吸混乱影响睡眠质量和所获得的休息量,同时也作为其他健康状况或风险的潜在指标。呼吸暂停和低钠是呼吸紊乱的主要指标,通常通过呼吸暂停症状指数(AHI)量化。多元纲术是用于检测呼吸暂停和低次胃肠事件的黄金标准(从而计算受试者的AHI),但尽管睡觉在奇怪的地方睡觉带来的奇怪的地方,但多重创新只能及时发出快照,并且长期不实用。监测。在这项工作中,我们描述了一种使用液压床传感器在睡眠期间检测呼吸暂停和次酮事件的方法,这已经证明了对长期监测和早期检测疾病的其他维度。我们将我们的结果与多酷热创新实验室生产的结果进行比较,包括呼吸扰动指数的计算。我们成功检测73.6%的呼吸暂停,精度77.2%,我们对呼吸暂停(AI)和呼吸障碍指数(RDI)的计算足够精确,以表明我们每个受试者的睡眠呼吸暂停症综合征(SAH)的适当严重程度。

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