Objective: Ultra-wideband radar technology offers a promising solution for unobtrusive and cost-effective in-home sleep monitoring. However, the limited availability of radar sleep data poses challenges in building robust models that generalize across diverse cohorts and environments. This study proposes a novel deep transfer learning framework to enhance sleep stage classification using radar data. Methods: An end-to-end neural network was developed to classify sleep stages based on nocturnal respiratory and motion signals. The network was trained using a combination of large-scale polysomnography (PSG) datasets and radar data. A domain adaptation approach employing adversarial learning was utilized to bridge the knowledge gap between PSG and radar signals. Validation was performed on a radar dataset of 47 older adults (mean age: 71.2), including 18 participants with prodromal or mild Alzheimer disease. Results: The proposed network structure achieves an accuracy of 79.5% with a Kappa value of 0.65 when classifying wakefulness, rapid eye movement, light sleep and deep sleep. Experimental results confirm that our deep transfer learning approach significantly enhances automatic sleep staging performance in the target domain. Conclusion: This method effectively addresses challenges associated with data variability and limited sample size, substantially improving the reliability of automatic sleep staging models, especially in contexts where radar data is limited. Significance: The findings underscore the viability of UWB radar as a nonintrusive, forward-looking sleep assessment tool that could significantly benefit care for older people and people with neurodegenerative disorders.

翻译：目的：超宽带雷达技术为无干扰、经济高效的家庭睡眠监测提供了一种前景广阔的解决方案。然而，雷达睡眠数据的有限可用性对构建能够泛化至不同人群与环境的稳健模型提出了挑战。本研究提出了一种新颖的深度迁移学习框架，旨在利用雷达数据提升睡眠分期分类性能。方法：开发了一种端到端神经网络，基于夜间呼吸与运动信号进行睡眠分期分类。该网络结合大规模多导睡眠图数据集与雷达数据进行训练。采用基于对抗学习的域适应方法，以弥合PSG信号与雷达信号之间的知识差异。验证在包含47名老年人（平均年龄：71.2岁）的雷达数据集上进行，其中18名参与者患有前驱期或轻度阿尔茨海默病。结果：所提出的网络结构在区分清醒期、快速眼动期、浅睡期与深睡期时，实现了79.5%的准确率与0.65的Kappa值。实验结果证实，我们的深度迁移学习方法显著提升了目标领域中自动睡眠分期的性能。结论：该方法有效应对了数据变异性和样本量有限带来的挑战，显著提高了自动睡眠分期模型的可靠性，尤其在雷达数据有限的场景中。意义：研究结果强调了超宽带雷达作为一种非侵入性、前瞻性睡眠评估工具的可行性，该工具可能为老年人及神经退行性疾病患者的照护带来显著益处。