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

TL;DR

This paper introduces ROSA, a novel method combining millimeter-wave radar and pulse oximetry to detect sleep apnea events, offering a low-cost, non-contact alternative with high accuracy for diagnosing OSAHS.

Contribution

The study presents a new sensor fusion approach that directly predicts sleep apnea event timing, improving over existing segment-based methods and addressing interference issues.

Findings

High correlation (ICC=0.9864) between ROSA and PSG-derived AHI.

Effective detection of sleep apnea events using low-load, non-contact sensors.

Demonstrated robustness against environmental and body movement interference.

Abstract

Obstructive Sleep Apnea-Hypopnea Syndrome (OSAHS) is a sleep-related breathing disorder associated with significant morbidity and mortality worldwide. The gold standard for OSAHS diagnosis, polysomnography (PSG), faces challenges in popularization due to its high cost and complexity. Recently, radar has shown potential in detecting sleep apnea-hypopnea events (SAE) with the advantages of low cost and non-contact monitoring. However, existing studies, especially those using deep learning, employ segment-based classification approach for SAE detection, making the task of event quantity estimation difficult. Additionally, radar-based SAE detection is susceptible to interference from body movements and the environment. Oxygen saturation (SpO2) can offer valuable information about OSAHS, but it also has certain limitations and cannot be used alone for diagnosis. In this study, we propose a method using millimeterwave radar and pulse oximeter to detect SAE, called ROSA. It fuses information from both sensors, and directly predicts the temporal localization of SAE. Experimental results demonstrate a high degree of consistency (ICC=0.9864) between AHI from ROSA and PSG. This study presents an effective method with lowload device for the diagnosis of OSAHS.