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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2509.15008 |
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| _version_ | 1866915757202341888 |
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| author | Niu, Chaoyue Rowe, Veronica Brown, Guy J. Elphick, Heather Kenyon, Heather Thomas, Lowri Johnson, Sam Ma, Ning |
| author_facet | Niu, Chaoyue Rowe, Veronica Brown, Guy J. Elphick, Heather Kenyon, Heather Thomas, Lowri Johnson, Sam Ma, Ning |
| contents | Paediatric obstructive sleep apnoea (OSA) is clinically significant yet difficult to diagnose, as children poorly tolerate sensor-based polysomnography. Acoustic monitoring provides a non-invasive alternative for home-based OSA screening, but limited paediatric data hinders the development of robust deep learning approaches. This paper proposes a transfer learning framework that adapts acoustic models pretrained on adult sleep data to paediatric OSA detection, incorporating SpO2-based desaturation patterns to enhance model training. Using a large adult sleep dataset (157 nights) and a smaller paediatric dataset (15 nights), we systematically evaluate (i) single- versus multi-task learning, (ii) encoder freezing versus full fine-tuning, and (iii) the impact of delaying SpO2 labels to better align them with the acoustics and capture physiologically meaningful features. Results show that fine-tuning with SpO2 integration consistently improves paediatric OSA detection compared with baseline models without adaptation. These findings demonstrate the feasibility of transfer learning for home-based OSA screening in children and offer its potential clinical value for early diagnosis. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15008 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Transfer Learning for Paediatric Sleep Apnoea Detection Using Physiology-Guided Acoustic Models Niu, Chaoyue Rowe, Veronica Brown, Guy J. Elphick, Heather Kenyon, Heather Thomas, Lowri Johnson, Sam Ma, Ning Audio and Speech Processing Paediatric obstructive sleep apnoea (OSA) is clinically significant yet difficult to diagnose, as children poorly tolerate sensor-based polysomnography. Acoustic monitoring provides a non-invasive alternative for home-based OSA screening, but limited paediatric data hinders the development of robust deep learning approaches. This paper proposes a transfer learning framework that adapts acoustic models pretrained on adult sleep data to paediatric OSA detection, incorporating SpO2-based desaturation patterns to enhance model training. Using a large adult sleep dataset (157 nights) and a smaller paediatric dataset (15 nights), we systematically evaluate (i) single- versus multi-task learning, (ii) encoder freezing versus full fine-tuning, and (iii) the impact of delaying SpO2 labels to better align them with the acoustics and capture physiologically meaningful features. Results show that fine-tuning with SpO2 integration consistently improves paediatric OSA detection compared with baseline models without adaptation. These findings demonstrate the feasibility of transfer learning for home-based OSA screening in children and offer its potential clinical value for early diagnosis. |
| title | Transfer Learning for Paediatric Sleep Apnoea Detection Using Physiology-Guided Acoustic Models |
| topic | Audio and Speech Processing |
| url | https://arxiv.org/abs/2509.15008 |