A deep neural network-based model for OSA severity classification using unsegmented peripheral oxygen saturation signals

Obstructive sleep apnea (OSA) is a common type of sleep-related breathing disorder, and polysomnography (PSG) remains the gold standard for its diagnosis. However, it takes a significant amount of time to perform PSG in a well-equipped laboratory, and patients typically have to wait a long time for a PSG test. In view of this, over recent years portable and even wearable tools for OSA classification have been developed as a low-cost and easy-to-use alternative to PSG. In this paper, a deep neural network (DNN)-based model was developed to classify OSA severity using peripheral oxygen saturation (SpO₂) signals; it showed the following advantages. First, the presented model takes unsegmented SpO₂ signals recorded overnight as its input, and OSA severity is then classified as one of four levels as the output. Consequently, there is obviously no need to label segmented signals, and the tremendous amount of effort spent on signal segmentation and then annotation can be completely saved. Second, a high generalization ability is provided since the largest amount of data were used to test the model. This feature gives the model an improved reliability for clinical use. Notably, the outperformance of this work is highlighted in a two-level classification case (with a cutoff apnea–hypopnea index of 5), where the accuracy and the sensitivity increased to above 91% and 95%, respectively.