通过对大规模夜间呼吸信号的深度学习来推进睡眠健康平等
Zhongxu Zhuang1, Biao Xue1, Qiang An2
1School of Electronic and Optical Engineering, Nanjing University of Science and Technology, Nanjing, China.
Nature communications
|October 22, 2025
概括
一个新的深度学习框架分析呼吸信号,用于远程睡眠监测,改善睡眠障碍诊断的可访问性. 这项技术能够准确地分类睡眠阶段和检测呼吸暂停,从而实现可扩展的睡眠医疗保健.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 睡眠医学 睡眠医学
背景情况:
- 睡眠障碍影响全球健康,但医疗保健资源限制了对诊断的准入.
- 需要远程监控解决方案来克服诊断可访问性的障碍.
研究的目的:
- 开发和验证一个深度学习框架,用于使用呼吸信号进行远程睡眠健康监测.
- 为了实现准确的睡眠阶段分类和呼吸暂停-呼吸暂停指数估计.
主要方法:
- 在15785个夜晚的呼吸信号数据上训练了一个深度学习框架.
- 采用转移学习来适应无接触雷达衍生呼吸信号的模型.
- 用自主监督学习技术进行实时处理.
主要成果:
- 该框架在四阶段睡眠分类 (内部验证) 中达到82.13%的准确性和79.62% (外部验证).
- 在呼吸暂停-呼吸暂停指数估计上发现了很高的一致性 (ICC=0.90内部,0.94外部).
- 在人口学子组中观察到一致的表现.
结论:
- 开发的深度学习框架为远程睡眠健康监测提供了强大的和可访问的解决方案.
- 这项技术可以显著改善睡眠障碍查,诊断和管理,提高医疗保健公平性.
- 与远程睡眠健康平台的集成有助于临床部署和可扩展的睡眠健康解决方案.
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