IEEE J Biomed Health Inform. 2014 Jul;18(4):1285-93. doi: 10.1109/JBHI.2013.2292928.
This paper introduces an online sleep apnea detection method based on heart rate complexity as measured by recurrence quantification analysis (RQA) statistics of heart rate variability (HRV) data. RQA statistics can capture nonlinear dynamics of a complex cardiorespiratory system during obstructive sleep apnea. In order to obtain a more robust measurement of the nonstationarity of the cardiorespiratory system, we use different fixed amount of neighbor thresholdings for recurrence plot calculation. We integrate a feature selection algorithm based on conditional mutual information to select the most informative RQA features for classification, and hence, to speed up the real-time classification process without degrading the performance of the system. Two types of binary classifiers, i.e., support vector machine and neural network, are used to differentiate apnea from normal sleep. A soft decision fusion rule is developed to combine the results of these classifiers in order to improve the classification performance of the whole system. Experimental results show that our proposed method achieves better classification results compared with the previous recurrence analysis-based approach. We also show that our method is flexible and a strong candidate for a real efficient sleep apnea detection system.
本文提出了一种基于心率变异性（HRV）数据的递归定量分析（RQA）统计的在线睡眠呼吸暂停检测方法。RQA 统计可以捕捉阻塞性睡眠呼吸暂停期间复杂心肺系统的非线性动力学。为了更稳健地测量心肺系统的非平稳性，我们使用不同的固定邻域阈值进行递归图计算。我们整合了一种基于条件互信息的特征选择算法，用于选择最具信息量的 RQA 特征进行分类，从而在不降低系统性能的情况下加快实时分类过程。我们使用两种类型的二进制分类器，即支持向量机和神经网络，来区分呼吸暂停和正常睡眠。开发了一种软决策融合规则来结合这些分类器的结果，以提高整个系统的分类性能。实验结果表明，与基于先前的递归分析的方法相比，我们提出的方法取得了更好的分类结果。我们还表明，我们的方法具有灵活性，是一种有效的睡眠呼吸暂停检测系统的有力候选者。
