Detection of Abnormal Respiratory Events with Single Channel ECG and Hybrid Machine Learning Model in Patients with Obstructive Sleep Apnea

Abstract 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.