Sleep Neurophysiological Dynamics Through the Lens of Multitaper Spectral Analysis

多窗口 脑电图 神经生理学 光谱图 睡眠(系统调用) 睡眠纺锤 光谱分析 大脑活动与冥想 睡眠阶段 计算机科学 心理学 慢波睡眠 神经科学 人工智能 多导睡眠图 语音识别 物理 量子力学 光谱学 操作系统
作者
Michael J. Prerau,Ritchie E. Brown,Matt T. Bianchi,Jeffrey M. Ellenbogen,Patrick L. Purdon
出处
期刊:Physiology [American Physiological Society]
卷期号:32 (1): 60-92 被引量:231
标识
DOI:10.1152/physiol.00062.2015
摘要

During sleep, cortical and subcortical structures within the brain engage in highly structured oscillatory dynamics that can be observed in the electroencephalogram (EEG). The ability to accurately describe changes in sleep state from these oscillations has thus been a major goal of sleep medicine. While numerous studies over the past 50 years have shown sleep to be a continuous, multifocal, dynamic process, long-standing clinical practice categorizes sleep EEG into discrete stages through visual inspection of 30-s epochs. By representing sleep as a coarsely discretized progression of stages, vital neurophysiological information on the dynamic interplay between sleep and arousal is lost. However, by using principled time-frequency spectral analysis methods, the rich dynamics of the sleep EEG are immediately visible—elegantly depicted and quantified at time scales ranging from a full night down to individual microevents. In this paper, we review the neurophysiology of sleep through this lens of dynamic spectral analysis. We begin by reviewing spectral estimation techniques traditionally used in sleep EEG analysis and introduce multitaper spectral analysis, a method that makes EEG spectral estimates clearer and more accurate than traditional approaches. Through the lens of the multitaper spectrogram, we review the oscillations and mechanisms underlying the traditional sleep stages. In doing so, we will demonstrate how multitaper spectral analysis makes the oscillatory structure of traditional sleep states instantaneously visible, closely paralleling the traditional hypnogram, but with a richness of information that suggests novel insights into the neural mechanisms of sleep, as well as novel clinical and research applications.

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