Complexity of Wake Electroencephalography Correlates With Slow Wave Activity After Sleep Onset.

Fengzhen Hou,Chunyong Wu,Zhinan Yu,Chung-Kang Peng,Yan Ma,Albert Yang

doi:10.3389/fnins.2018.00809

Fengzhen Hou, Chunyong Wu + Show 4 more

Open Access

https://doi.org/10.3389/fnins.2018.00809

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Abstract

Sleep electroencephalography (EEG) provides an opportunity to study sleep scientifically, whose chaotic, dynamic, complex, and dissipative nature implies that non-linear approaches could uncover some mechanism of sleep. Based on well-established complexity theories, one hypothesis in sleep medicine is that lower complexity of brain waves at pre-sleep state can facilitate sleep initiation and further improve sleep quality. However, this has never been studied with solid data. In this study, EEG collected from healthy subjects was used to investigate the association between pre-sleep EEG complexity and sleep quality. Multiscale entropy analysis (MSE) was applied to pre-sleep EEG signals recorded immediately after light-off (while subjects were awake) for measuring the complexities of brain dynamics by a proposed index, CI1−30. Slow wave activity (SWA) in sleep, which is commonly used as an indicator of sleep depth or sleep intensity, was quantified based on two methods, traditional Fast Fourier transform (FFT) and ensemble empirical mode decomposition (EEMD). The associations between wake EEG complexity, sleep latency, and SWA in sleep were evaluated. Our results demonstrated that lower complexity before sleep onset is associated with decreased sleep latency, indicating a potential facilitating role of reduced pre-sleep complexity in the wake-sleep transition. In addition, the proposed EEMD-based method revealed an association between wake complexity and quantified SWA in the beginning of sleep (90 min after sleep onset). Complexity metric could thus be considered as a potential indicator for sleep interventions, and further studies are encouraged to examine the application of EEG complexity before sleep onset in populations with difficulty in sleep initiation. Further studies may also examine the mechanisms of the causal relationships between pre-sleep brain complexity and SWA, or conduct comparisons between normal and pathological conditions.

Highlights

Sleep medicine has been increasingly recognized as an important discipline in recent decades; the current limitations of electroencephalography (EEG)-based sleep analysis and quantification may have led to ongoing controversy
We examined the associations among wake EEG complexity, sleep latency, and the subsequent Slow wave activity (SWA) quantities during early sleep and over the entire night
Our results revealed a positive correlation between the complexity during the 5 min wakeful EEG and sleep latency

Summary

Introduction

Sleep medicine has been increasingly recognized as an important discipline in recent decades; the current limitations of electroencephalography (EEG)-based sleep analysis and quantification may have led to ongoing controversy. Owing to the non-linear and dynamic features of EEG, non-linear approaches may lead to better understanding of the profound complexity of sleep. Non-linear dynamics theory provides new opportunities for understanding the behavior of EEG (Acharya et al, 2005). EEG has been used to mark features of sleep (He et al, 2005; Janjarasjitt et al, 2008; Yeh et al, 2013; Abeysuriya et al, 2014), and it has been reported that nonlinearity depends on sleep stage (Shen et al, 2003). Studies have increasingly used non-linear methods to investigate the nature of brain activities during sleep (Ma et al, 2018), but there are still limitations in the existing literature. The full advantages of nonlinear approaches have yet to be determined (Ma et al, 2018)

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