睡眠AC:不太依赖手动注释,更可靠的采样自动睡眠分期.
IEEE journal of biomedical and health informatics
|July 9, 2025
概括
通过自适应地选择各种样本并使用对比学习,SleepAC显著减少了手动睡眠评分注释的需求. 这种方法以最小的标记数据实现了高精度,改善了睡眠障碍诊断.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
背景情况:
- 准确的睡眠分期对于诊断睡眠障碍至关重要,但需要广泛,昂贵的手册注释.
- 当前减少注释依赖的方法往往缺乏数据多样性,限制了模型概括.
- 有限的标记数据阻碍了强大的睡眠分析模型的开发.
研究的目的:
- 开发一种新型模型,SleepAC,最大限度地减少手动睡眠评分注释的需要.
- 提高睡眠阶段模型在低资源环境中的概括性和准确性.
- 为了减少与创建大型,专业标记的睡眠数据集相关的重大成本和时间.
主要方法:
- SleepAC采用适应性样本选择策略,优先考虑信息和多样化的数据点.
- 该模型结合了特定于睡眠的因素,以减少标签,提高分类准确性.
- 一个对比的学习框架产生了硬负样本,以改善具有挑战性的过渡睡眠阶段的分类.
主要成果:
- 在四个公共数据集中,SleepAC 实现了具有竞争力的准确性和 F1 评分.
- 该模型使用仅20%的标记数据达到大约95%的完全监督性能.
- 在低资源场景中表现出有效性,在复杂的睡眠动态中显示出有希望的概括性.
结论:
- 睡眠AC显著降低了注释成本和睡眠分期的时间.
- 该模型提供了一个可行的解决方案,用于开发精确的睡眠分析工具,使用有限的标记数据.
- 睡眠AC显示出在睡眠医学和研究中广泛应用的潜力,提高了诊断能力.
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