Respiratory scoring is an important step in the diagnosis of Obstructive Sleep Apnea (OSA). Airflow, abdolmel-thorax and pulse oximetry signals are obtained with the help of Polysomnography (PSG) device for the respiration scoring stage. These signals are visually scored by a specialist physician. The PSG has several disadvantages: one of them is that a technician is required to use the device. In addition, the records must be taken in the hospital environment. The aim of this study is to develop a new machine learning based hybrid sleep/awake detection method with single channel ECG alternative to respiratory scoring. For this purpose, electrocardiography (ECG) signal of 10 patients with OSA was used. The Heart Rate Variable signal was derived from the ECG signal. Then, QRS components in different frequency bands were obtained from ECG signal by digital filtering. In this way, a total of nine more signals were obtained. Each of the nine signals consists of 25 features, which amounts to a total of 225 features. Fisher feature selection algorithm and Principal Component Analysis (PCA) were used to reduce the number of features. Ultimately the features extracted from the first received signals were classified with Decision Tree, Support Vector Machines, k-Nearest Neighborhood Algorithm and Ensemble classifiers. In addition, the proposed model was checked with the Leave One Out method. At the end of the study, for the detection of apnea, 82.11% accuracy with only 3 features and 85.12% accuracy with 13 features were obtained. The sensitivity and specificity values for the 3 properties are 0.82 and 0.82, respectively. For the 13 properties, 0.85 and 0.86, respectively. These results show that the proposed model can be used for the detection of Respiratory Scoring in the OSA diagnostic process.

呼吸评分是诊断阻塞性睡眠呼吸暂停（OSA）的重要步骤。借助多导睡眠图（PSG）装置在呼吸评分阶段获得气流，abdolmel-胸腔和脉搏血氧饱和度信号。这些信号由专科医生视觉评分。PSG有几个缺点：其中之一是要求技术人员使用该设备。此外，这些记录必须在医院环境中进行。这项研究的目的是开发一种新的基于机器学习的混合睡眠/清醒检测方法，该方法具有替代呼吸计分的单通道ECG。为此，使用了10例OSA患者的心电图（ECG）信号。心率可变信号来自ECG信号。然后，通过数字滤波从心电信号中获得不同频段的QRS分量。这样，总共获得了九个信号。九个信号中的每个信号都包含25个特征，总计225个特征。Fisher特征选择算法和主成分分析（PCA）用于减少特征数量。最终，使用决策树，支持向量机，k最近邻算法和Ensemble分类器对从第一个接收到的信号中提取的特征进行分类。此外，建议的模型还通过“留一法”进行了检查。在研究结束时，对于呼吸暂停的检测，仅3个特征的准确度为82.11％，对13个特征的准确度为85.12％。这三个特性的灵敏度和特异性值分别为0.82和0.82。对于13个属性，分别为0.85和0.86。这些结果表明，提出的模型可用于OSA诊断过程中的呼吸计分检测。