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首页> 外文期刊>Journal of sleep research >Analysis of oscillatory patterns in the human sleep EEG using a novel detection algorithm.
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Analysis of oscillatory patterns in the human sleep EEG using a novel detection algorithm.

机译：使用新型检测算法分析人类睡眠脑电图中的振荡模式。

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摘要

The different brain states during sleep are characterized by the occurrence of distinct oscillatory patterns such as spindles or delta waves. Using a new algorithm to detect oscillatory events in the electroencephalogram (EEG), we studied their properties and changes throughout the night. The present approach was based on the idea that the EEG may be described as a superposition of stochastically driven harmonic oscillators with damping and frequency varying in time. This idea was implemented by fitting autoregressive models to the EEG data. Oscillatory events were detected, whenever the damping of one or more frequencies was below a predefined threshold. Sleep EEG data of eight healthy young males were analyzed (four nights per subject). Oscillatory events occurred mainly in three frequency ranges, which correspond roughly to the classically defined delta (0-4.5 Hz), alpha (8-11.5 Hz) and sigma (11.5-16 Hz) bands. Their incidence showed small intra- but large inter-individual differences, in particular with respect to alpha events. The incidence and frequency of the events was characteristic for sleep stages and non-rapid eye movement (REM)-REM sleep cycles. The mean event frequency of delta and sigma (spindle) events decreased with the deepening of sleep. It was higher in the second half of the night compared with the first one for delta, alpha and sigma oscillations. The algorithm provides a general framework to detect and characterize oscillatory patterns in the EEG and similar signals.
机译：睡眠期间不同的大脑状态的特征是出现了不同的振荡模式，例如纺锤体或三角波。使用一种新的算法来检测脑电图（EEG）中的振荡事件，我们研究了它们在整个晚上的特性和变化。本方法基于这样的思想，即脑电图可以描述为具有随时间变化的阻尼和频率的随机驱动谐波振荡器的叠加。这个想法是通过将自回归模型拟合到EEG数据来实现的。每当一个或多个频率的阻尼低于预定阈值时，都会检测到振荡事件。分析了八名健康的年轻男性的睡眠脑电数据（每个受试者四晚）。振荡事件主要发生在三个频率范围内，大致对应于经典定义的增量（0-4.5 Hz），阿尔法（8-11.5 Hz）和西格玛（11.5-16 Hz）频段。它们的发生率显示出个体间较小但个体间较大的差异，特别是在α事件方面。事件的发生率和频率是睡眠阶段和非快速眼动（REM）-REM睡眠周期的特征。随着睡眠的加深，三角洲事件和西格玛（主轴）事件的平均事件频率降低。在夜晚的后半部分，它的增量，阿尔法和西格玛振荡的频率要高于前者。该算法提供了检测和表征EEG和类似信号中的振荡模式的通用框架。

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