AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients
Obstructive sleep apnea (OSA) is characterized by frequent episodes of sleep-disordered breathing (SDB), which are often accompanied by leg movement (LM) events, especially periodic limb movements during sleep (PLMS). Traditional single-event detection methods often overlook the dynamic interactions...
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MDPI AG
2025-05-01
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| author | Qiuyue Li Kewei Li Cong Fu Yiyuan Zhang Huan Yu Chen Chen Wei Chen |
| author_facet | Qiuyue Li Kewei Li Cong Fu Yiyuan Zhang Huan Yu Chen Chen Wei Chen |
| author_sort | Qiuyue Li |
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| description | Obstructive sleep apnea (OSA) is characterized by frequent episodes of sleep-disordered breathing (SDB), which are often accompanied by leg movement (LM) events, especially periodic limb movements during sleep (PLMS). Traditional single-event detection methods often overlook the dynamic interactions between SDB and LM, failing to capture their temporal overlap and differences in duration. To address this, we propose Attention-enhanced CRF with U-Net (AttenCRF-U), a novel joint detection framework that integrates multi-head self-attention (MHSA) within an encoder–decoder architecture to model long-range dependencies between overlapping events and employs multi-scale convolutional encoding to extract discriminative features across different temporal scales. The model further incorporates a conditional random field (CRF) to refine event boundaries and enhance temporal continuity. Evaluated on clinical PSG recordings from 125 OSA patients, the model with CRF improved the average F1 score from 0.782 to 0.788 and reduced temporal alignment errors compared with CRF-free baselines. The joint detection strategy distinguished respiratory-related leg movements (RRLMs) from PLMS, boosting the PLMS detection F1 score from 0.756 to 0.778 and the SDB detection F1 score from 0.709 to 0.728. By integrating MHSA into a CRF-augmented U-Net framework and enabling joint detection of multiple event types, this study presents a novel approach to modeling temporal dependencies and event co-occurrence patterns in sleep disorder diagnosis. |
| format | Article |
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| institution | OA Journals |
| issn | 2306-5354 |
| language | English |
| publishDate | 2025-05-01 |
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| spelling | doaj-art-97ee409576eb4e6aa49f4ce71eaeaecd2025-08-20T02:24:39ZengMDPI AGBioengineering2306-53542025-05-0112657110.3390/bioengineering12060571AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA PatientsQiuyue Li0Kewei Li1Cong Fu2Yiyuan Zhang3Huan Yu4Chen Chen5Wei Chen6School of Information Science and Technology, Fudan University, Shanghai 200433, ChinaSchool of Stomatology, Fudan University, Shanghai 200032, ChinaHuashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, ChinaSchool of Information Science and Technology, Fudan University, Shanghai 200433, ChinaHuashan Hospital, Shanghai Medical College, Fudan University, Shanghai 200040, ChinaCenter for Medical Research and Innovation, Shanghai Pudong Hospital, Human Phenome Institute, Fudan University, Shanghai 201203, ChinaSchool of Biomedical Engineering, The University of Sydney, Camperdown, NSW 2006, AustraliaObstructive sleep apnea (OSA) is characterized by frequent episodes of sleep-disordered breathing (SDB), which are often accompanied by leg movement (LM) events, especially periodic limb movements during sleep (PLMS). Traditional single-event detection methods often overlook the dynamic interactions between SDB and LM, failing to capture their temporal overlap and differences in duration. To address this, we propose Attention-enhanced CRF with U-Net (AttenCRF-U), a novel joint detection framework that integrates multi-head self-attention (MHSA) within an encoder–decoder architecture to model long-range dependencies between overlapping events and employs multi-scale convolutional encoding to extract discriminative features across different temporal scales. The model further incorporates a conditional random field (CRF) to refine event boundaries and enhance temporal continuity. Evaluated on clinical PSG recordings from 125 OSA patients, the model with CRF improved the average F1 score from 0.782 to 0.788 and reduced temporal alignment errors compared with CRF-free baselines. The joint detection strategy distinguished respiratory-related leg movements (RRLMs) from PLMS, boosting the PLMS detection F1 score from 0.756 to 0.778 and the SDB detection F1 score from 0.709 to 0.728. By integrating MHSA into a CRF-augmented U-Net framework and enabling joint detection of multiple event types, this study presents a novel approach to modeling temporal dependencies and event co-occurrence patterns in sleep disorder diagnosis.https://www.mdpi.com/2306-5354/12/6/571obstructive sleep apneasleep-disordered breathingperiodic limb movements during sleepjoint sleep event detection |
| spellingShingle | Qiuyue Li Kewei Li Cong Fu Yiyuan Zhang Huan Yu Chen Chen Wei Chen AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients Bioengineering obstructive sleep apnea sleep-disordered breathing periodic limb movements during sleep joint sleep event detection |
| title | AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients |
| title_full | AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients |
| title_fullStr | AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients |
| title_full_unstemmed | AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients |
| title_short | AttenCRF-U: Joint Detection of Sleep-Disordered Breathing and Leg Movements in OSA Patients |
| title_sort | attencrf u joint detection of sleep disordered breathing and leg movements in osa patients |
| topic | obstructive sleep apnea sleep-disordered breathing periodic limb movements during sleep joint sleep event detection |
| url | https://www.mdpi.com/2306-5354/12/6/571 |
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