使用床下称重传感器对呼吸事件进行分类。
Classification of breathing events using load cells under the bed.
作者信息
Beattie Zachary T, Hagen Chad C, Pavel Misha, Hayes Tamara L
机构信息
Oregon Health & Science University, Portland, 97239, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:3921-4. doi: 10.1109/IEMBS.2009.5333548.
Abstract
Sleep disturbances are prevalent, financially taxing, and have a negative effect on health and quality of life. One of the most common sleep disturbances is obstructive sleep apnea-hypopnea syndrome (OSAHS) which frequently goes undiagnosed. The gold standard for diagnosing OSAHS is polysomnography (PSG)-a procedure that is inconvenient, time-consuming, and interferes with normal sleep patterns. We are investigating an alternative to PSG in which unobtrusive load cells fitted under the bed are used to monitor movement, heart rate, and respiration. In this paper we describe how load cell data can be used to distinguish between clinically relevant disordered breathing (apneas and hypopneas) and normal respiration. The method correctly classified disordered breathing segments with a sensitivity of 0.77 and a specificity of 0.91.
摘要
睡眠障碍很普遍，会带来经济负担，且对健康和生活质量有负面影响。最常见的睡眠障碍之一是阻塞性睡眠呼吸暂停低通气综合征（OSAHS），该病症常常未被诊断出来。诊断OSAHS的金标准是多导睡眠图（PSG）——这是一种不方便、耗时且会干扰正常睡眠模式的检查方法。我们正在研究一种替代PSG的方法，即使用安装在床下的无干扰称重传感器来监测运动、心率和呼吸。在本文中，我们描述了如何利用称重传感器数据来区分临床上相关的呼吸紊乱（呼吸暂停和低通气）与正常呼吸。该方法正确分类呼吸紊乱片段的灵敏度为0.77，特异性为0.91。
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