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Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep-relatedbreathing disorder associated with significant morbidity and mortalityworldwide. The gold standard for OSAHS diagnosis, polysomnography (PSG), faceschallenges in popularization due to its high cost and complexity. Recently,radar has shown potential in detecting sleep apnea-hypopnea events (SAE) withthe advantages of low cost and non-contact monitoring. However, existingstudies, especially those using deep learning, employ segment-basedclassification approach for SAE detection, making the task of event quantityestimation difficult. Additionally, radar-based SAE detection is susceptible tointerference from body movements and the environment. Oxygen saturation (SpO2)can offer valuable information about OSAHS, but it also has certain limitationsand cannot be used alone for diagnosis. In this study, we propose a methodusing millimeterwave radar and pulse oximeter to detect SAE, called ROSA. Itfuses information from both sensors, and directly predicts the temporallocalization of SAE. Experimental results demonstrate a high degree ofconsistency (ICC=0.9864) between AHI from ROSA and PSG. This study presents aneffective method with lowload device for the diagnosis of OSAHS.

Detection of Sleep Apnea-Hypopnea Events Using Millimeter-wave Radar and Pulse Oximeter