In this paper, we utilized obstructive sleep apnea and cardiovascular disease-related photoplethysmography (PPG) features in constructing the input to deep learning (DL). The features are pulse wave amplitude (PWA), beat-to-beat or RR interval, a derivative of PWA, a derivative of RR interval, systolic phase duration, diastolic phase duration, and pulse area. Then, we develop DL architectures to evaluate the proposed features' usefulness. Eventually, we demonstrate that in human-machine settings where the medical staff only needs to label 20% of the PPG recording length, our proposed features with the developed DL architectures achieve 79.95% and 73.81% recognition accuracy in MESA and HeartBEAT datasets. This simplifies the labelling task of the medical staff during the sleep test yet provides accurate apnea event recognition.

ApSense: Data-driven Algorithm in PPG-based Sleep Apnea Sensing