The heartbeat interval (HBI) signal (RR-time series), and electrocardiogram (ECG) derived respiration (EDR) signal quantify the information about the cardiopulmonary activity, and monitoring these two signals simultaneously will provide more information for the sleep apnea detection. This paper proposes a novel approach to detect sleep apnea using both HBI and EDR signals. The approach consists of the decomposition of both HBI and EDR signals into reconstructed components (RCs) or modes using a data-driven signal processing approach namely, the sliding mode singular spectrum analysis (SM-SSA), extraction of features from each RC, and the use of classifier for the detection of sleep apnea. The features such as the mean and the standard deviation values are extracted from the instantaneous amplitude (IA) and instantaneous frequency (IF) of each RC of both HBI and EDR signals. The classifiers, such as the stacked autoencoder based deep neural network (SAE-DNN), and support vector machine (SVM) are considered to classify normal and apnea episodes using the statistical features obtained from the RCs of HBI and EDR signals. The proposed approach is evaluated using different public databases such as apnea-ECG database, University College Dublin (UCD) database, and Physionet challenge database, respectively. The results demonstrate that the combination of the statistical features and SVM classifier has the sensitivity and specificity values of 82.45% and 79.72%, respectively using the 10-fold cross-validation based selection of training and test instances from the apnea-ECG database. Moreover, for subject-specific cross-validation, the proposed method has an average sensitivity and specificity values of 62.87%, and 81.53%, respectively. The proposed method has achieved the accuracy values of 94.3%, and 72% for per-recording based classification of sleep apnea and normal classes using signals from apnea-ECG and UCD databases.

心跳间隔（HBI）信号（RR时间序列）和心电图（ECG）得出的呼吸（EDR）信号可量化有关心肺活动的信息，同时监视这两个信号将为睡眠呼吸暂停检测提供更多信息。本文提出了一种使用HBI和EDR信号检测睡眠呼吸暂停的新方法。该方法包括使用数据驱动的信号处理方法将HBI和EDR信号分解为重构分量（RC）或模式，即滑模奇异频谱分析（SM-SSA），从每个RC提取特征，以及使用分类器检测睡眠呼吸暂停。从HBI和EDR信号的每个RC的瞬时幅度（IA）和瞬时频率（IF）中提取诸如平均值和标准偏差值之类的特征。分类器（例如，基于堆叠式自动编码器的深层神经网络（SAE-DNN）和支持向量机（SVM））被认为可以使用从HBI和EDR信号的RC获得的统计特征对正常和呼吸暂停发作进行分类。分别使用不同的公共数据库（如呼吸暂停ECG数据库，都柏林大学（UCD）数据库和Physionet挑战数据库）对提出的方法进行了评估。结果表明，统计特征和支持向量机分类器的组合具有82.45％和79.72％的敏感性和特异性值，分别使用基于呼吸暂停ECG数据库的10倍交叉验证选择的训练和测试实例。此外，对于特定对象的交叉验证，该方法的平均灵敏度和特异性值分别为62.87％和81.53％。对于来自呼吸暂停-ECG和UCD数据库的信号，基于记录的睡眠呼吸暂停和正常类别的分类，所提出的方法已达到94.3％和72％的准确度值。