Automatic Detection of Sleep Apnea from Single-lead ECG Signal Using Machine Learning

Sleep apnea is a common disorder that reduces sleep quality which can lead to serious health consequences. It is identified by a pause in breathing during sleep. At present, polysomnography (PSG) is used to diagnose sleep disorders at sleep centers. PSG is non-invasive but expensive and inconvenient as a clinician needs to observe the subjects overnight. Therefore, development of low-cost and most comfortable techniques is being developed by the researchers. This research presents an implementation of a classification algorithm for identifying sleep apnea episodes using an electrocardiogram (ECG) signal. It also provides a comparison of the accuracies achieved by different classifiers and a comparison between the performance of individual features. A Sgolay filter was utilized for baseline correction and denoising the ECG signal and used to calculate the Heart Rate Variability (HRV) sequence using RR intervals. Ten features were extracted from the ECG and HRV signals, and the performance of the proposed algorithm has been estimated for five classifiers, support vector machine (SVM), linear discriminant analysis (LDA), K-nearest neighbor (KNN), random forest (RF) and decision tree (DT). For the ECG signal, the average accuracies depicted by SVM, LDA, KNN, RF, and DT are 86.01±1.16,68.16±0.76,84.99±1.24,87.00±1.40, and S2.56±1.05, respectively. Further, for the HRV signal, the average accuracies depicted by SVM, LDA, KNN, RF, and DT are 83.53±1.36, 72.69±2.06, 82.60±1.07, 85.10±1.44, and 78.60±1.15, respectively. This research highlights the significance of selecting the appropriate classifier for a given issue and establishing and using the most relevant characteristics to achieve a higher level of accuracy. These encouraging early-stage findings might pave the way for additional research to refine the classifiers in preparation for potential use in the real world. The effectiveness of the suggested model was evaluated alongside the performance of other methods already in use for the detection of sleep apnea.