Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders
Abstract Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocognitive risk factors. Current multi-sensor diagnostic approaches are intrusive and prone to misdiagnosis when simplified. This study introduces an enhanced single-sensor-based OSA screening meth...
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Nature Portfolio
2025-05-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-01430-3 |
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| author | Xiaoman Xing Sizhi Ai Jihui Zhang Rui Huang Yaping Liu Dongming Quan Jiacheng Ma Guoli Wu Jiangen Xu Yuan Zhang Hongliang Feng Wen-fei Dong |
| author_facet | Xiaoman Xing Sizhi Ai Jihui Zhang Rui Huang Yaping Liu Dongming Quan Jiacheng Ma Guoli Wu Jiangen Xu Yuan Zhang Hongliang Feng Wen-fei Dong |
| author_sort | Xiaoman Xing |
| collection | DOAJ |
| description | Abstract Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocognitive risk factors. Current multi-sensor diagnostic approaches are intrusive and prone to misdiagnosis when simplified. This study introduces an enhanced single-sensor-based OSA screening method, leveraging novel signal processing and machine learning to ensure accurate detection across diverse populations. Wrist actigraphy is a widely-used and energy-efficient tool for respiratory rate estimation. The main challenge in OSA pattern recognition is handling various disturbances in real-world applications. We developed a novel approach combining apex-centric tokenization with a Multi-Head Causal Attention (MHCA) mechanism. Apex-centric tokenization enhances sensitivity to OSA events, while MHCA refines predictions and increases specificity in detecting oxygen desaturation. Our study involved 58 participants, with overnight bilateral wrist actigraphy and concurrent polysomnography used as a reference for thorough analysis. By focusing on the physiological causal relationship of the events, the algorithm excelled in detecting moderate to severe oxygen desaturation, achieving a sensitivity of 85.7% and a specificity of 98.1%, even in the presence of disturbances such as restless leg movements and snoring. The estimated oxygen desaturation index correlated strongly with clinical standards (r = 0.89), and the correlation with the apnea-hypopnea index was 0.87. Both apex-centric tokenization and MHCA were crucial for this performance. Our approach shows potential for analyzing apnea patterns and related oxygen desaturation in a broader population using only wrist actigraphy, reducing measurement burdens and improving understanding of complex sleep disorders. |
| format | Article |
| id | doaj-art-dd5d0402f10e48b49c38d99a7f8ebf6e |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-dd5d0402f10e48b49c38d99a7f8ebf6e2025-08-20T03:08:25ZengNature PortfolioScientific Reports2045-23222025-05-0115111310.1038/s41598-025-01430-3Apnea detection using wrist actigraphy in patients with heterogeneous sleep disordersXiaoman Xing0Sizhi Ai1Jihui Zhang2Rui Huang3Yaping Liu4Dongming Quan5Jiacheng Ma6Guoli Wu7Jiangen Xu8Yuan Zhang9Hongliang Feng10Wen-fei Dong11Division of Life Sciences and Medicine, School of Biomedical Engineering (Suzhou), University of Science and Technology of ChinaCenter for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical UniversityCenter for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical UniversitySuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesCenter for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical UniversityGuangdong Mental Health Center, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical UniversityCenter for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical UniversityCenter for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical UniversitySuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesDepartment of Respiratory Medicine, Xiangya Hospital, Central South UniversityCenter for Sleep and Circadian Medicine, The Affiliated Brain Hospital, Guangzhou Medical UniversitySuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesAbstract Obstructive sleep apnea (OSA) and related hypoxia are well-established cardiovascular and neurocognitive risk factors. Current multi-sensor diagnostic approaches are intrusive and prone to misdiagnosis when simplified. This study introduces an enhanced single-sensor-based OSA screening method, leveraging novel signal processing and machine learning to ensure accurate detection across diverse populations. Wrist actigraphy is a widely-used and energy-efficient tool for respiratory rate estimation. The main challenge in OSA pattern recognition is handling various disturbances in real-world applications. We developed a novel approach combining apex-centric tokenization with a Multi-Head Causal Attention (MHCA) mechanism. Apex-centric tokenization enhances sensitivity to OSA events, while MHCA refines predictions and increases specificity in detecting oxygen desaturation. Our study involved 58 participants, with overnight bilateral wrist actigraphy and concurrent polysomnography used as a reference for thorough analysis. By focusing on the physiological causal relationship of the events, the algorithm excelled in detecting moderate to severe oxygen desaturation, achieving a sensitivity of 85.7% and a specificity of 98.1%, even in the presence of disturbances such as restless leg movements and snoring. The estimated oxygen desaturation index correlated strongly with clinical standards (r = 0.89), and the correlation with the apnea-hypopnea index was 0.87. Both apex-centric tokenization and MHCA were crucial for this performance. Our approach shows potential for analyzing apnea patterns and related oxygen desaturation in a broader population using only wrist actigraphy, reducing measurement burdens and improving understanding of complex sleep disorders.https://doi.org/10.1038/s41598-025-01430-3Wrist actigraphyApneaOxygen desaturationTokenizationMulti-head causal attention |
| spellingShingle | Xiaoman Xing Sizhi Ai Jihui Zhang Rui Huang Yaping Liu Dongming Quan Jiacheng Ma Guoli Wu Jiangen Xu Yuan Zhang Hongliang Feng Wen-fei Dong Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders Scientific Reports Wrist actigraphy Apnea Oxygen desaturation Tokenization Multi-head causal attention |
| title | Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders |
| title_full | Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders |
| title_fullStr | Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders |
| title_full_unstemmed | Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders |
| title_short | Apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders |
| title_sort | apnea detection using wrist actigraphy in patients with heterogeneous sleep disorders |
| topic | Wrist actigraphy Apnea Oxygen desaturation Tokenization Multi-head causal attention |
| url | https://doi.org/10.1038/s41598-025-01430-3 |
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