在多个公共睡眠研究数据集中改进睡眠障碍呼吸注释
Hyun Keun Ahn1, Younghoon Na1,2, Hyun-Woo Shin1,2,3,4,5,6
1Obstructive Upper Airway Research Laboratory (OUaR LaB), Department of Pharmacology, Seoul National University College of Medicine, Seoul, South Korea.
Journal of sleep research
|December 23, 2025
概括
一个新的标准化管道改善了睡眠呼吸暂停研究的多睡眠学注释. 这提高了呼吸暂停-呼吸暂停指数计算和阻塞性睡眠暂停严重程度分类的准确性.
科学领域:
- 睡眠医学 睡眠医学
- 生物医学数据科学 生物医学数据科学
- 医疗保健中的人工智能
背景情况:
- 主要睡眠研究 (SHHS,MrOS,MESA) 中现有的多睡眠学注释使用了对呼吸暂停/呼吸暂停的简化标准,偏离了美国睡眠医学学会 (AASM) 的指导方针.
- 这种不一致导致不准确的呼吸暂停-呼吸暂停指数 (AHIs) 和潜在的错误的研究结论.
研究的目的:
- 根据AASM标准,开发和验证一个标准化的注释管道,用于聚睡眠学数据,整合睡眠阶段,氧气脱和唤醒事件.
- 在大型队列中重新评估AHI和阻塞性睡眠呼吸暂停 (OSA) 严重程度,使用精细的注释.
- 评估改进的注释对OSA分类深度学习模型性能的影响.
主要方法:
- 开发了一个标准化的注释管道,结合了AASM关于睡眠阶段,氧气脱和兴奋事件的指南.
- 追溯分析了SHHS1,SHHS2,MrOS1,MrOS2,MESA和KISS队伍的多睡眠学数据.
- 将原始AHI值与使用精制管道和训练深度学习模型对原始和精制注释进行重新计算的AHI进行了比较.
主要成果:
- 在SHHS,MrOS和MESA中的原始注释大大高估了AHIs (MAE:10.323.6事件/小时).
- 精细的注释大大降低了MAE到0.561.29事件/小时,证明了更好的准确性.
- 对于OSA严重程度分类的深度学习模型性能有所改善,在注释改进后,F1得分从0.5增加到0.69.
结论:
- 标准化的注释管道增强了聚睡眠学分析的跨队列一致性和准确性.
- 这种方法可以更可靠地利用现有的睡眠研究数据集用于临床研究和人工智能驱动的应用.
- 通过标准化的注释遵守AASM指南对于准确的睡眠呼吸暂停评估和研究至关重要.
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