Effects of Ageing and Sex on Complexity in the Human Sleep EEG: A Comparison of Three Symbolic Dynamic Analysis Methods

Abstract

Symbolic dynamic analysis (SDA) methods have been applied to biomedical signals and have been proven efficient in characterising differences in the electroencephalogram (EEG) in various conditions (e.g., epilepsy, Alzheimer’s, and Parkinson’s diseases). In this study, we investigated the use of SDA on EEGs recorded during sleep. Lempel-Ziv complexity (LZC), permutation entropy (PE), and permutation Lempel-Ziv complexity (PLZC), as well as power spectral analysis based on the fast Fourier transform (FFT), were applied to 8-h sleep EEG recordings in healthy men (n=31) and women (n=29), aged 20-74 years. The results of the SDA methods and FFT analysis were compared and the effects of age and sex were investigated. Surrogate data were used to determine whether the findings with SDA methods truly reflected changes in nonlinear dynamics of the EEG and not merely changes in the power spectrum. The surrogate data analysis showed that LZC merely reflected spectral changes in EEG activity, whereas PE and PLZC reflected genuine changes in the nonlinear dynamics of the EEG. All three SDA techniques distinguished the vigilance states (i.e., wakefulness, REM sleep, NREM sleep, and its sub-stages: stage 1, stage 2, and slow wave sleep). Complexity of the sleep EEG increased with ageing. Sex on the other hand did not affect the complexity values assessed with any of these three SDA methods, even though FFT detected sex differences. This study shows that SDA provides additional insights into the dynamics of sleep EEG and how it is affected by ageing.

Changes in EEG permutation entropy in the evening and in the transition from wake to sleep

“…when they went to bed relative to light-off or the type of activities they were engaged in prior to going to bed. It is therefore unclear whether participants' approach more sensitive than others that previously failed to identify significant changes during in-lab sleep deprivation protocols 34,74 .…”

“…Permutation Entropy (PE) has received substantial attention 18 : its low computational cost and robustness to observational noise 19 , trends 20 and even common blink and eye-movement artifact in EEG 21 , makes it an interesting approach for large datasets that could, otherwise, require long processing, as well as for, potential noisier, ambulatory recordings. PE was found to be useful in detecting epileptic seizure [22][23][24][25] , assessing the effects of anesthesia [26][27][28] , understanding cognitive brain activity 29,30 and assessing disorders of consciousness 31,32 Moreover, PE was found to progressively decrease during slow wave sleep 33,34 . How PE changes during wakefulness over the few hours preceding sleep and in the transition from wake to sleep is not established.…”

Overall Population Generalities, Sex Differences, and Individual Differences in Sleep Electroencephalography Functional Connectivity

“…Several methods have been reported for detecting sex differences using sleep EEG signals, with most utilizing the calculation of energy [32], [33] or entropy [48]. Few studies have focused on sleep EEG FC.…”

Time course of cortical response complexity during extended wakefulness and its differential association with vigilance in young and older individuals

“…LZC reflects the underlying activeness and information processing capacity of the underlying neurons [9] and is, therefore, brain state-dependent. LZC has been proven to successfully differentiate between different consciousness and vigilance states (alert wakefulness, light and deep slow wave sleep, rapid eye movement (REM) sleep, disorders of consciousness, anaesthesia) [10][11][12]. Furthermore, LZC-based measures have been shown to be high during normal wakefulness and REM sleep and low during non-REM (NREM) sleep, with a progressive decrease from light to deeper sleep stages [10].…”

“…Furthermore, LZC-based measures have been shown to be high during normal wakefulness and REM sleep and low during non-REM (NREM) sleep, with a progressive decrease from light to deeper sleep stages [10]. This progressive increase in the regularity of the signal depends at least in part on changes in the balance between high-and low-frequency EEG powers, ranging from hyper desynchronized high frequency activity during well-rested wakefulness to hypersynchronous low frequency EEG signal during deep sleep [11]. Two animal studies investigated whether cortical complexity changes during partial sleep deprivation in rats [13,14] and found no significant change in complexity.…”