For automatic detection and monitoring of obstructive sleep apnea

R. Katz, M. Lawee, A. Newman, J. Woodrow Weiss
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引用次数: 1

Abstract

Nonlinear/chaotic algorithms are used for automatic detection and clinical monitoring of obstructive sleep apnea (OSA). We cite an example taken from a group of adults where similar results are obtained. The algorithms are applied to clinical time series of airflow (thermistry), chest effort (impedance) and electrocardiogram (ECG) traces obtained from sleep apnea records (Edentrace Systems). These algorithms may be applied to a variety of other data sets (i.e. oxygen saturation, heart rate). The algorithms can pinpoint the onset of a disabling disorder (i.e apnea) and mark the duration of the event. They are robust when applied to multiple data sets in which obstructive apneas are known to occur.
用于自动检测和监测阻塞性睡眠呼吸暂停
非线性/混沌算法用于阻塞性睡眠呼吸暂停(OSA)的自动检测和临床监测。我们以一组成年人为例,得出了类似的结果。该算法应用于从睡眠呼吸暂停记录(Edentrace Systems)获得的气流(热学)、胸用力(阻抗)和心电图(ECG)痕迹的临床时间序列。这些算法可以应用于各种其他数据集(即氧饱和度,心率)。该算法可以精确定位残疾障碍(即呼吸暂停)的发作,并标记事件的持续时间。当应用于已知发生阻塞性呼吸暂停的多个数据集时,它们具有鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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