用成人数据训练的声学模型，结合血氧信号，可有效检测儿童睡眠呼吸暂停。
Transfer Learning for Paediatric Sleep Apnoea Detection Using Physiology-Guided Acoustic Models
- 用成人声学数据预训练，再通过血氧信号微调适应儿童数据
- 在15个儿童夜的数据上，融合血氧信息后准确率显著提升
- 适合做儿童居家睡眠筛查，临床价值高
儿童阻塞性睡眠呼吸暂停（OSA）临床意义重大但难以诊断，因儿童难以耐受基于传感器的多导睡眠图。声学监测提供了一种无创的居家筛查替代方案，但缺乏儿科数据限制了深度学习方法的发展。本文提出一种迁移学习框架，将基于成人睡眠数据预训练的声学模型适配至儿童OSA检测，并引入基于血氧饱和度（SpO2）的低氧模式以增强训练。利用大规模成人睡眠数据集（157个夜晚）和小规模儿科数据集（15个夜晚），系统评估了（i）单任务与多任务学习，（ii）编码器冻结与全量微调，以及（iii）延迟对齐血氧标签以更好地匹配声学信号、捕捉生理相关特征的影响。结果表明，结合血氧信息的微调策略在儿童OSA检测上持续优于未适配基线模型。这些发现证明了迁移学习在儿童居家OSA筛查中的可行性，并展示了其早期诊断的潜在临床价值。
原文摘要 · Abstract (English)
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.
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