一种新的定量自动方法,用于测量非快速眼动睡眠脑电图幅度可变性。

A new quantitative automatic method for the measurement of non-rapid eye movement sleep electroencephalographic amplitude variability.

机构信息

Sleep Research Centre, Department of Neurology IC, Oasi Institute for Research on Mental Retardation and Brain Aging, Troina, Italy.

出版信息

J Sleep Res. 2012 Apr;21(2):212-20. doi: 10.1111/j.1365-2869.2011.00981.x. Epub 2011 Nov 16.

Abstract

The aim of this study was to arrange an automatic quantitative measure of the electroencephalographic (EEG) signal amplitude variability during non-rapid eye movement (NREM) sleep, correlated with the visually extracted cyclic alternating pattern (CAP) parameters. Ninety-eight polysomnographic EEG recordings of normal controls were used. A new algorithm based on the analysis of the EEG amplitude variability during NREM sleep was designed and applied to all recordings, which were also scored visually for CAP. All measurements obtained with the new algorithm correlated positively with corresponding CAP parameters. In particular, total CAP time correlated with total NREM variability time (r = 0.596; P < 1E-07), light sleep CAP time with light sleep variability time (r = 0.597; P < 1E-07) and slow wave sleep CAP time with slow wave sleep variability time (r = 0.809; P < 1E-07). Only the duration of CAP A phases showed a low correlation with the duration of variability events. Finally, the age-related modifications of CAP time and of NREM variability time were found to be very similar. The new method for the automatic analysis of NREM sleep amplitude variability presented here correlates significantly with visual CAP parameters; its application requires a minimum work time, compared to CAP analysis, and might be used in large studies involving numerous recordings in which NREM sleep EEG amplitude variability needs to be assessed.

摘要

本研究旨在对非快速眼动(NREM)睡眠期间的脑电图(EEG)信号幅度变异性进行自动定量测量,并与视觉提取的周期性交替模式(CAP)参数相关联。使用了 98 例正常对照者的多导睡眠图 EEG 记录。设计了一种基于 NREM 睡眠期间 EEG 幅度变异性分析的新算法,并将其应用于所有记录,这些记录也进行了 CAP 的视觉评分。新算法获得的所有测量值与相应的 CAP 参数呈正相关。特别是,总 CAP 时间与总 NREM 变异性时间相关(r=0.596;P<1E-07),浅睡眠 CAP 时间与浅睡眠变异性时间相关(r=0.597;P<1E-07),慢波睡眠 CAP 时间与慢波睡眠变异性时间相关(r=0.809;P<1E-07)。只有 CAP A 相的持续时间与变异性事件的持续时间显示出低相关性。最后,发现 CAP 时间和 NREM 变异性时间的年龄相关性变化非常相似。这里提出的用于自动分析 NREM 睡眠幅度变异性的新方法与视觉 CAP 参数显著相关;与 CAP 分析相比,其应用需要的工作时间更少,并且可以用于涉及需要评估 NREM 睡眠 EEG 幅度变异性的大量记录的大型研究中。

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索