Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data
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-moti...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
China Science Publishing & Media Ltd. (CSPM)
2025-02-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24107 |
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| Summary: | 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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| ISSN: | 2095-283X |