Severity Classification of Obstructive Sleep Apnea Using Electrocardiogram Signals

In this paper, we propose a method of classifying the severity of obstructive sleep apnea (OSA) using electrocardiogram (ECG) signals and deep learning. In our previous research, we presented an ECG-based signal segmentation-free model for OSA severity classification. Its key feature is using the unsegmented overnight ECG signal as input and directly predicting the four categories of OSA severity as output. The overall performance of our previous work has been demonstrated to significantly exceed those of most existing studies. On the basis of a preliminary study, a method of improving the accuracy of OSA severity classification is proposed in this paper. Modifications to the model architecture for OSA severity classification were made, and a squeeze-and-excitation network (SENet) was integrated into this work. Finally, our experimental results indicated that the accuracy of the four-category classification of OSA severity in this paper is 57.91%, which is slightly higher than 57.55% achieved in our previous research.