Meta-learning Based Obstructive Sleep Apnea Detection Using Single-Lead ECG Signals

Abstract

As a respiratory syndrome correlated with some cardiovascular diseases, obstructive sleep apnea (OSA) not only destroys the quality of our sleep, but also induces a variety of major chronic diseases such as heart disease, and diabetes, and even causes sudden death during sleep. Many studies have been conducted on the classification of OSA from normal events by machine learning. However, we found that differences in patients caused by the individuality of the ECG patterns and variability in the ECG do not create optimal rules for OSA classification by ECG signals. It is necessary to reduce the impact of individual differences in classification. In this study, we propose a method based on meta-learning to detect OSA using a 2D time-frequency scalogram that is converted from a single-lead ECG signal. According to the experiment results, we achieved 68.10% accuracy, 69.19% sensitivity, 67.71% specificity, and a 0.694 F1 score. The results showed this method based on meta-learning using the Siamese network is feasible without being pre-trained by massive 2D scalogram representations. © 2023 IEEE.