IEEE J Biomed Health Inform. 2018 Jul;22(4):1036-1045. doi: 10.1109/JBHI.2017.2740120. Epub 2017 Aug 14.
Obstructive sleep apnea (OSA) is a chronic sleep disorder affecting millions of people worldwide. Individuals with OSA are rarely aware of the condition and are often left untreated, which can lead to some serious health problems. Nowadays, several low-cost wearable health sensors are available that can be used to conveniently and noninvasively collect a wide range of physiological signals. In this paper, we propose a new framework for OSA detection in which we combine the wearable sensor measurement signals with the mathematical models of the cardiorespiratory system. Vector-valued Gaussian processes (GPs) are adopted to model the physiological variations among different individuals. The GP covariance is constructed using the sum of separable kernel functions, and the GP hyperparameters are estimated by maximizing the marginal likelihood function. A likelihood ratio test is proposed to detect OSA using the widely available heart rate and peripheral oxygen saturation (SpO ) measurement signals. We conduct experiments on both synthetic and real data to show the effectiveness of the proposed OSA detection framework compared to purely data-driven approaches.
阻塞性睡眠呼吸暂停（OSA）是一种影响全球数百万人的慢性睡眠障碍。患有 OSA 的人很少意识到这种情况，而且往往得不到治疗，这可能导致一些严重的健康问题。如今，有几种低成本的可穿戴健康传感器可供使用，可以方便、无创地采集各种生理信号。在本文中，我们提出了一种新的 OSA 检测框架，其中我们将可穿戴传感器测量信号与心肺系统的数学模型相结合。采用向量值高斯过程（GP）对不同个体的生理变化进行建模。GP 协方差由可分离核函数的和构建，GP 超参数通过最大化边际似然函数来估计。提出了一种使用广泛可用的心率和外周血氧饱和度（SpO ）测量信号检测 OSA 的似然比检验。我们在合成数据和真实数据上进行实验，以显示与纯数据驱动方法相比，所提出的 OSA 检测框架的有效性。
