Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
Department of Electrical and Electronics Engineering, BITS-Pilani, Hyderabad Campus, Hyderabad, 500078, India.
Comput Biol Med. 2019 May;108:20-30. doi: 10.1016/j.compbiomed.2019.03.016. Epub 2019 Mar 26.
Sleep is a prominent physiological activity in our daily life. Sleep apnea is the category of sleep disorder during which the breathing of the person diminishes causing the alternation in the upper airway resistance. The electrocardiogram derived respiration (EDR) and heart rate (RR-time-series) signals are normally used for the detection of sleep apnea as these two signals capture cardio-pulmonary activity information. Hence, the analysis of these two signals provides vital information about sleep apnea. In this paper, we propose the novel sparse residual entropy (SRE) features for the automated detection of sleep apnea using EDR and heart rate signals. The features required for the automated detection of sleep apnea are extracted in three steps: (i) atomic decomposition based residual estimation from both EDR and heart rate signals using orthogonal matching pursuit (OMP) with different dictionaries, (ii) estimation of probabilities from each sparse residual, and (iii) calculation of the entropy features. The proposed SRE features are fed to the combination of fuzzy K-means clustering and support vector machine (SVM) to pick the best performing classifier. The experimental results demonstrate that the proposed SRE features with radial basis function (RBF) kernel-based SVM classifier yielded higher performance with accuracy, sensitivity and specificity values of 78.07%, 78.01%, and 78.13%, respectively with Fourier dictionary and 10-fold cross-validation. For subject-specific or leave-one-out validation case, the SVM classifier has sensitivity and specificity of 85.43% and 92.60%, respectively using SRE features with Fourier dictionary (FD).
睡眠是我们日常生活中一项重要的生理活动。睡眠呼吸暂停是睡眠障碍的一种，在这种障碍中，人的呼吸会减弱，导致上呼吸道阻力的交替变化。心电图衍生呼吸（EDR）和心率（RR 时间序列）信号通常用于睡眠呼吸暂停的检测，因为这两个信号捕捉心肺活动信息。因此，对这两个信号的分析提供了有关睡眠呼吸暂停的重要信息。在本文中，我们提出了一种新颖的稀疏残差熵（SRE）特征，用于使用 EDR 和心率信号自动检测睡眠呼吸暂停。自动检测睡眠呼吸暂停所需的特征通过以下三个步骤提取：（i）使用正交匹配追踪（OMP）和不同字典对 EDR 和心率信号进行基于原子分解的残差估计，（ii）从每个稀疏残差中估计概率，以及（iii）计算熵特征。将提出的 SRE 特征输入到模糊 K-均值聚类和支持向量机（SVM）的组合中，以选择性能最佳的分类器。实验结果表明，使用基于径向基函数（RBF）核的 SVM 分类器的 SRE 特征具有更高的性能，其准确率、灵敏度和特异性值分别为 78.07%、78.01%和 78.13%，Fourier 字典和 10 折交叉验证。对于特定于主题或留一法验证的情况，SVM 分类器使用 Fourier 字典（FD）的 SRE 特征具有 85.43%和 92.60%的灵敏度和特异性。
