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Multi-Modal Home Sleep Monitoring in Older Adults
Published on: January 26, 2019
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在使用β阻塞剂的患者中,基于心肺呼吸系统的睡眠阶段表现
Lieke Hermans1,2, Fokke van Meulen2,3, Peter Anderer4,5
1Philips Research, Eindhoven, The Netherlands.
概括
β抑制剂不会影响CReSS算法的准确性,用于使用心肺呼吸信号自动测定睡眠阶段. 这种机器学习方法对于睡眠障碍患者来说仍然可靠,即使使用这种常见药物.
科学领域:
- 心血管呼吸系统信号分析
- 睡眠的药物 睡眠的药物
- 自主神经系统功能自主神经系统功能
背景情况:
- 家庭睡眠监测提供了自动睡眠分阶段的潜力.
- 机器学习算法显示出对睡眠障碍诊断的前景.
- 像β阻塞剂这样的药物对自主生理学和睡眠阶段的影响尚不清楚.
研究的目的:
- 评估现有的机器学习睡眠分期算法 (CReSS) 在使用β阻塞剂的睡眠障碍患者与不使用β阻塞剂的患者的性能.
- 为了确定β阻塞剂的使用是否会影响基于心肺呼吸系统的睡眠阶段的准确性.
主要方法:
- 追溯分析114名睡眠障碍患者的睡眠记录 (57名使用β阻塞剂,57名对照).
- 睡眠阶段使用基于心电图和呼吸力度的CReSS算法自动得分.
- 算法性能与使用时代协议和衍生睡眠参数的手动多睡眠法进行了比较.
主要成果:
- 在两组中,CReSS算法在四种睡眠阶段分类方面取得了实质性的一致性 (β阻断剂:卡帕=0.635,精度=78.1%;对照组:卡帕=0.660,精度=78.8%).
- 在使用β抑制剂和对照组的患者之间没有观察到统计学上显著的性能差异.
- 对于衍生睡眠参数的共识也没有在两组之间有所不同.
结论:
- 在使用β阻塞剂的患者中,CReSS算法的性能不会受到损害.
- 使用机器学习进行基于心肺呼吸系统的睡眠分期是这种患者群体的可行方法.
- 自主特征可可靠地用于代孕睡眠测量,即使使用β阻塞药物.
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