人工智能驱动的睡眠呼吸暂停查与夜间血氧和:目前的做法和未来的方向
1Ubiquitous and Personal Computing Lab, Faculty of Engineering, Kyoto University of Advanced Science (KUAS), Kyoto, Japan.
Frontiers in digital health
|April 24, 2025
概括
可穿戴的血液氧气传感器可以进行可访问的睡眠呼吸暂停查. 未来的研究需要更多的数据和标准化的方法来进行人工智能驱动的SpO2分析,以改善诊断.
科学领域:
- 生物医学工程 生物医学工程
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 睡眠呼吸暂停是一种普遍的睡眠障碍,具有严重的长期健康风险.
- 由于医疗检测的机会有限,许多患者未被诊断.
- 便携式血液氧和 (SpO2) 传感器为可访问的查提供了一个有希望的途径.
研究的目的:
- 调查基于SpO2的睡眠呼吸暂停查方法的进展.
- 确定当前该领域的研究类别和局限性.
主要方法:
- 在主要的科学数据库中进行了系统的文献审查.
- 关键词包括"睡眠呼吸暂停"",SpO2"/"血液氧和",以及"机器学习"/"深度学习".
- 从835个初步结果中选择了31个相关出版物进行全文分析.
主要成果:
- 基于SpO2的睡眠呼吸暂停查研究分为三个类别:个人呼吸暂停事件检测,呼吸暂停-呼吸暂停指数预测和呼吸暂停严重程度分类.
- 发现了两个重要的研究缺陷:公共数据集不足/不多样化,数据收集,预处理和基准测试缺乏标准化的协议.
结论:
- 使用SpO2信号的AI驱动的睡眠呼吸暂停查具有早期诊断的巨大潜力.
- 解决数据可访问性和标准化问题对于提高这些方法的可靠性和有效性至关重要.
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