Sleep apnea is a sleep related pathology in which breathing or respiratory activity of an individual is obstructed, resulting in variations in the cardio-pulmonary (CP) activity. The monitoring of both cardiac (heart rate (HR)) and pulmonary (respiration rate (RR)) activities are important for the automated detection of this ailment. In this paper, we propose a novel automated approach for sleep apnea detection using the bivariate CP signal. The bivariate CP signal is formulated using both HR and RR signals extracted from the electrocardiogram (ECG) signal. The approach consists of three stages. First, the bivariate CP signal is decomposed into intrinsic mode functions (IMFs) and residuals for both HR and RR channels using bivariate fast and adaptive empirical mode decomposition (FAEMD). Second, the features are extracted using time-domain analysis, spectral analysis, and time-frequency domain analysis of IMFs from CP signal. The time-frequency domain features are computed from the cross time-frequency matrices of IMFs of CP signal. The cross time-frequency matrix of each IMF is evaluated using the Stockwell (S)-transform. Third, the support vector machine (SVM) and the random forest (RF) classifiers are used for automated detection of sleep apnea with the features from the bivariate CP signal. Our proposed approach has demonstrated an average sensitivity and specificity of 82.27% and 78.67%, respectively for sleep apnea detection using the 10-fold cross-validation method. The approach has yielded an average sensitivity and specificity of 73.19% and 73.13%, respectively for the subject-specific cross-validation. The performance of the approach was compared with other CPC features used for the detection of sleep apnea.

中文翻译：

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使用双变量快速自适应EMD和交叉时频分析，从心肺信号自动检测睡眠呼吸暂停。

睡眠呼吸暂停是一种与睡眠有关的病理，其中个体的呼吸或呼吸活动受阻，导致心肺（CP）活动发生变化。心脏（心率（HR））和肺（呼吸率（RR））活动的监测对于自动检测这种疾病很重要。在本文中，我们提出了一种使用双变量CP信号进行睡眠呼吸暂停检测的新型自动化方法。使用从心电图（ECG）信号提取的HR和RR信号来制定双变量CP信号。该方法包括三个阶段。首先，使用双变量快速和自适应经验模式分解（FAEMD）将双变量CP信号分解为固有模式函数（IMF）和HR和RR通道的残差。第二，使用时域分析，频谱分析和IMF从CP信号的时频域分析来提取特征。时频域特征是根据CP信号的IMF的交叉时频矩阵计算得出的。使用斯托克韦尔（S）变换评估每个IMF的交叉时频矩阵。第三，支持向量机（SVM）和随机森林（RF）分类器用于自动检测睡眠呼吸暂停，并具有双变量CP信号的特征。我们提出的方法已证明使用10倍交叉验证方法检测睡眠呼吸暂停的平均敏感性和特异性分别为82.27％和78.67％。该方法对受试者特异性交叉验证的平均敏感性和特异性分别为73.19％和73.13％。