机器学习诊断阻塞性睡眠呼吸暂停的心电图心率变化:贝叶斯的元分析
Yunrui Hao1, Nicole Kye Wen Tan1, Esther Yanxin Gao1,2,3
1Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
Sleep & breathing = Schlaf & Atmung
|September 30, 2025
概括
使用心率变化 (HRV) 的机器学习模型显示了查阻塞性睡眠呼吸暂停综合征 (OSA) 的前景. 这些人工智能工具为OSA检测提供了传统诊断方法的可扩展和可访问的替代方案.
科学领域:
- 心脏病学 心脏病学
- 睡眠医学 睡眠医学
- 人工智能的人工智能
背景情况:
- 阻塞性睡眠呼吸暂停综合征 (OSA) 是普遍存在且未被诊断出来的,对健康构成重大风险.
- 多睡眠学,OSA诊断的黄金标准,是资源密集的,不适合广泛的查.
- 通过心电图进行心率变化 (HRV) 分析,提供了一个潜在的可访问的查替代方案.
研究的目的:
- 评估在HRV上训练的机器学习 (ML) 模型的诊断准确性,以检测成年人OSA.
- 评估自动化HRV分析作为OSA可扩展查工具的潜力.
- 为了比较基于ML的HRV分析与已建立的OSA诊断方法的性能.
主要方法:
- 在主要数据库 (PubMed,Embase,Scopus,Web of Science,IEEE Xplore) 进行了系统的文献搜索,直到2024年10月.
- 包括符合条件的研究,比较HRV训练的AI模型与OSA诊断的呼吸暂停-呼吸暂停指数 (AHI).
- 贝叶斯双变随机效应元分析被用于聚合灵敏度和特异性,QUADAS-2和GRADE用于偏差和确定性评估.
主要成果:
- 包括2019名参与者的9项研究符合纳入标准.
- 对OSA检测的综合灵敏度为79.0% (95% CrI: 74.9%-82.7%) 和综合特异性为75.0% (95% CrI: 67.9%-82.3%).
- 超回归表明,特异性因人口因素而异,但模型架构和验证方法没有显著影响性能. 没有发现出版偏差.
结论:
- 使用HRV的机器学习模型证明了阻塞性睡眠呼吸暂停综合征的良好诊断准确性.
- 这些模型的特异性高于STOP-BANG问卷,性能与家庭睡眠测试相美.
- 基于HRV的ML模型的可扩展性和潜在集成到可穿戴设备中,为OSA提供了一种实用且具有成本效益的查策略,需要进一步的现实验证.
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