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ZHAO Xiang, WANG Wei, LI Chenyang, et al. Diagnosis of sleep apnea hypopnea syndrome using fusion of micro-motion signals from millimeter-wave radar and pulse wave data[J]. Journal of Radars, in press. doi: 10.12000/JR24107
Citation: ZHAO Xiang, WANG Wei, LI Chenyang, et al. Diagnosis of sleep apnea hypopnea syndrome using fusion of micro-motion signals from millimeter-wave radar and pulse wave data[J]. Journal of Radars, in press. doi: 10.12000/JR24107

Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data

DOI: 10.12000/JR24107
Funds:  The National Science Fund for Distinguished Young Scholars (61925106)
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  • Corresponding author: LI Gang, gangli@mail.tsinghua.edu.cn
  • Received Date: 2024-05-30
  • Rev Recd Date: 2024-09-05
  • Available Online: 2024-09-10
  • Sleep Apnea Hypopnea Syndrome (SAHS) is a common chronic sleep-related breathing disorder that affects individuals’ sleep quality and physical health. This article presents a sleep apnea and hypopnea detection framework based on multisource signal fusion. Integrating millimeter-wave radar micro-motion signals and pulse wave signals of PhotoPlethysmoGraphy (PPG) achieves a highly reliable and light-contact diagnosis of SAHS, addressing the drawbacks of traditional medical methods that rely on PolySomnoGraphy (PSG) for sleep monitoring, such as poor comfort and high costs. This study used a radar and pulse wave data preprocessing algorithm to extract time-frequency information and artificial features from the signals, balancing the accuracy and robustness of sleep-breathing abnormality event detection Additionally, a deep neural network was designed to fuse the two types of signals for precise identification of sleep apnea and hypopnea events, and to estimate the Apnea-Hypopnea Index (AHI) for quantitative assessment of sleep-breathing abnormality severity. Experimental results of a clinical trial dataset at Shanghai Jiaotong University School of Medicine Affiliated Sixth People’s Hospital demonstrated that the AHI estimated by the proposed approach correlates with the gold standard PSG with a coefficient of 0.93, indicating good consistency. This approach is a promiseing tool for home sleep-breathing monitoring and preliminary diagnosis of SAHS.

     

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