使用粗粒度标签监测睡眠呼吸暂停严重性的弱监督深度学习
Xin Zan1, Di Wang2, Changyue Song3
1Department of Industrial and Systems Engineering, The University of Iowa, IA, USA.
IEEE transactions on automation science and engineering : a publication of the IEEE Robotics and Automation Society
|August 29, 2025
概括
这项研究引入了一个弱监督的深度学习模型来估计睡眠呼吸暂停的严重程度. 这种方法降低了标签成本,为睡眠呼吸暂停患者提供了更广泛的诊断.
科学领域:
- 生物医学工程
- 医学的人工智能
- 睡眠医学
背景情况:
- 睡眠呼吸暂停的诊断依赖于手动注释生理信号,这是耗时和昂贵的.
- 目前用于无呼吸检测的机器学习方法需要大量精细标记的数据,从而限制了其临床应用.
- 不被诊断的睡眠呼吸暂停影响了大量患者,强调需要更容易获得的诊断工具.
研究的目的:
- 开发一个弱监督的深度学习框架,以仅使用粗粒度标签来估计细粒度睡眠呼吸暂停的严重程度.
- 将临床知识纳入深度学习模型,以提高呼吸暂停严重程度的准确性.
- 减少对睡眠呼吸暂停诊断的昂贵手动标签的依赖.
主要方法:
- 一个新的知识增强的双颗粒度一致性损失旨在改善细粒度呼吸暂停的严重性学习.
- 临床知识是使用顺序对齐函数进行数学编码以校准估计的准确性.
- 该框架使用粗粒度标签 (呼吸暂停存在) 来估计细粒度的严重程度.
主要成果:
- 拟议的方法可以准确地估计细粒度睡眠呼吸暂停的严重程度.
- 与传统的监督方法相比,标签成本大大降低.
- 该模型在高时间分辨率下监测呼吸暂停的严重程度方面表现出卓越的性能.
结论:
- 低监督的深度学习,加上临床知识,为睡眠呼吸暂停的严重程度估计提供了有效的解决方案.
- 这种方法可以显著降低诊断成本,并扩大睡眠呼吸暂停监测的机会.
- 开发的框架对实验室和家庭的睡眠呼吸暂停诊断具有前景.
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