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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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2025-02-01
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| Series: | Leida xuebao |
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| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR24107 |
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| author | Xiang ZHAO Wei WANG Chenyang LI Jian GUAN Gang LI |
| author_facet | Xiang ZHAO Wei WANG Chenyang LI Jian GUAN Gang LI |
| author_sort | Xiang ZHAO |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-2a49d67b81f849fa92768593827caa46 |
| institution | Kabale University |
| issn | 2095-283X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj-art-2a49d67b81f849fa92768593827caa462025-01-22T06:12:25ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2025-02-0114110211610.12000/JR24107R24107Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave DataXiang ZHAO0Wei WANG1Chenyang LI2Jian GUAN3Gang LI4Department of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Otorhinolaryngology Head and Neck Surgery, Shanghai JiaoTong University School of Medicine Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, ChinaDepartment of Otorhinolaryngology Head and Neck Surgery, Shanghai JiaoTong University School of Medicine Affiliated Sixth People’s Hospital, Shanghai Key Laboratory of Sleep Disordered Breathing, Shanghai 200233, ChinaDepartment of Electronic Engineering, Tsinghua University, Beijing 100084, ChinaSleep 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.https://radars.ac.cn/cn/article/doi/10.12000/JR24107millimeter-wave radarphotoplethysmography (ppg)multimodal signal fusiondeep neural networksleep apnea hypopnea syndrome (sahs)apnea-hypopnea index (ahi) |
| spellingShingle | Xiang ZHAO Wei WANG Chenyang LI Jian GUAN Gang LI Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data Leida xuebao millimeter-wave radar photoplethysmography (ppg) multimodal signal fusion deep neural network sleep apnea hypopnea syndrome (sahs) apnea-hypopnea index (ahi) |
| title | Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data |
| title_full | Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data |
| title_fullStr | Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data |
| title_full_unstemmed | Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data |
| title_short | Diagnosis of Sleep Apnea Hypopnea Syndrome Using Fusion of Micro-motion Signals from Millimeter-wave Radar and Pulse Wave Data |
| title_sort | diagnosis of sleep apnea hypopnea syndrome using fusion of micro motion signals from millimeter wave radar and pulse wave data |
| topic | millimeter-wave radar photoplethysmography (ppg) multimodal signal fusion deep neural network sleep apnea hypopnea syndrome (sahs) apnea-hypopnea index (ahi) |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR24107 |
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