Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events
Heterogeneous recurrence analysis of heartbeat dynamics for the identification of sleep apnea events
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DOI：
10.1016/j.compbiomed.2016.05.006
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发表时间：
2016-08-01
影响因子：
7.7
通讯作者：
Yang, Hui
中科院分区：
文献类型：
--
作者：
Cheng, Changqing;Kan, Chen;Yang, Hui
阻塞性睡眠呼吸暂停 (OSA) 是一种常见的睡眠障碍，影响 24% 的成年男性和 9% 的成年女性。它的发生是由于睡眠期间上呼吸道阻塞，从而导致血氧水平降低，从而引发觉醒和睡眠碎片。 OSA 显着影响睡眠质量，并且已知它会导致许多健康并发症，例如高血压和 2 型糖尿病。 OSA 的传统诊断依赖于多导睡眠图，这种方法昂贵、耗时且一般人群难以获得。传感技术的最新进展为使用单通道心电图 (ECG) 筛查 OSA 事件提供了前所未有的机会。然而，现有方法在表征心电图信号非线性动力学特征方面能力有限。因此，OSA 改变的心电活动的隐藏模式无法完全揭示和理解。本文提出了一种新的异质复发模型来表征心率变异性，以识别 OSA。首先根据从单通道 ECG 中提取的 RR 间隔时间序列重建非线性状态空间。此外，状态空间被递归地划分为局部递归区域的层次结构。设计了一种新的分形表示来有效地表征分段子区域之间的状态转换。然后开发统计方法来量化异质复发模式。此外，我们将分类模型与异质复发特征相结合，以区分健康受试者和 OSA 患者。实验结果表明，所提出的方法捕获了变换空间中的异质复发模式，并提供了一种使用单导联心电图信号检测 OSA 的有效工具。 (C) 2016 Elsevier Ltd. 保留所有权利。
Obstructive sleep apnea (OSA) is a common sleep disorder that affects 24% of adult men and 9% of adult women. It occurs due to the occlusion of the upper airway during sleep, thereby leading to a decrease of blood oxygen level that triggers arousals and sleep fragmentation. OSA significantly impacts the quality of sleep and it is known to be responsible for a number of health complications, such as high blood pressure and type 2 diabetes. Traditional diagnosis of OSA relies on polysomnography, which is expensive, time-consuming and inaccessible to the general population. Recent advancement of sensing provides an unprecedented opportunity for the screening of OSA events using single-channel electrocardiogram (ECG). However, existing approaches are limited in their ability to characterize nonlinear dynamics underlying ECG signals. As such, hidden patterns of OSA-altered cardiac electrical activity cannot be fully revealed and understood. This paper presents a new heterogeneous recurrence model to characterize the heart rate variability for the identification of OSA. A nonlinear state space is firstly reconstructed from a time series of RR intervals that are extracted from single-channel ECGs. Further, the state space is recursively partitioned into a hierarchical structure of local recurrence regions. A new fractal representation is designed to efficiently characterize state transitions among segmented sub-regions. Statistical measures are then developed to quantify heterogeneous recurrence patterns. In addition, we integrate classification models with heterogeneous recurrence features to differentiate healthy subjects from OSA patients. Experimental results show that the proposed approach captures heterogeneous recurrence patterns in the transformed space and provides an effective tool to detect OSA using one-lead ECG signals. (C) 2016 Elsevier Ltd. All rights reserved.