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

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.

Beyond Frequency Band Constraints in EEG Analysis: The Role of the Mode Decomposition in Pushing the Boundaries

“…An innovative data-driven technique called empirical mode decomposition (EMD) has been introduced to analyze non-Gaussian, nonlinear, and non-stationary signals [19]. When combined with the Hilbert transform of the EMD-extracted modal components, this method is also known as the Hilbert-Huang transform (HHT) method and has been applied in the analysis of EEG signals [5,15,[20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. As feature extraction of the EEG is improved through HHT, applications for other fields, such as brain-computer interface and machine learning, have arisen as a natural consequence [36][37][38][39]; however, less attention has been paid to the biological meaning of the IMFs themselves or how the mode-mixing problem affects their interpretation in terms of physiology.…”

“…Most existing studies have utilized clinical databases or have involved only a limited number of participants and EEG leads. Furthermore, the choice between the FFT and Hilbert Transform methods for IMF-based spectral analysis of EEG signals lacks clear evidence and consensus [30,40].…”

Comparison between traditional fast Fourier transform and marginal spectra using the Hilbert–Huang transform method for the broadband spectral analysis of the electroencephalogram in healthy humans

“…The original algorithm proposed by Norden Huang 5 has been improved several times across the years making the EMD applicable for the quantitative analysis of a multivariate signal like the EEG 10‐12 . In the last few years other procedures have been proposed for the analysis of the nonlinear and non‐stationary signals 13‐15 but the EMD‐based techniques have demonstrated itself to be a reliable and effective method in the processing of different biomedical signals such as the EEG 16‐30 …”

Analysis in the frequency domain of multicomponent oscillatory modes of the human electroencephalogram extracted with multivariate empirical mode decomposition

“…A novel fully data-driven method (Huang et al 1998) for the analysis of non-Gaussian, nonlinear and non-stationary signals, the empirical mode decomposition (EMD), followed by the Hilbert transform of the extracted modal components with the EMD, known also as the Hilbert-Huang transform method, has been introduced for the study of EEG signals (Al-Subari et al 2015;Al-Subari et al 2016;Carella et al 2018;Chatterjee 2019;Chuang et al 2019;Dinares-Ferran et al 2018;Estevez-Baez et al 2017a, b, c;Hansen et al 2019;Hassan, Bhuiyan 2017;Hou et al 2018;Javed et al 2019;Rahman, Fattah 2017). The modal components extracted using the EMD can be considered as an adaptive spectral band (Noshadi et al 2014).…”