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

Abstract

Quantifying the complexity of the EEG signal during prolonged wakefulness and during sleep is gaining interest as an additional mean to characterize the mechanisms associated with sleep and wakefulness regulation. Here, we characterized how EEG complexity, as indexed by Multiscale Permutation Entropy (MSPE), changed progressively in the evening prior to light off and during the transition from wakefulness to sleep. We further explored whether MSPE was able to discriminate between wakefulness and sleep around sleep onset and whether MSPE changes were correlated with spectral measures of the EEG related to sleep need during concomitant wakefulness (theta power – Ptheta: 4-8 Hz). To address these questions, we took advantage of large datasets of several hundred of ambulatory EEG recordings of individual of both sexes aged 25 to 101y. Results show that MSPE significantly decreases before light off (i.e. before sleep time) and in the transition from wakefulness to sleep onset. Furthermore, MSPE allows for an excellent discrimination between pre-sleep wakefulness and early sleep. Finally, we show that MSPE is correlated with concomitant Ptheta. Yet, the direction of the latter correlation changed from before light-off to the transition to sleep. Given the association between EEG complexity and consciousness, MSPE may track efficiently putative changes in consciousness preceding sleep onset. MSPE stands as a comprehensive measure that is not limited to a given frequency band and reflects a progressive change brain state associated with sleep and wakefulness regulation. It may be an effective mean to detect when the brain is in a state close to sleep onset.

OFF-periods reduce the complexity of neocortical activity during sleep

“…Methods) for 15 rats in freely moving conditions, cycling through the states of sleep and wakefulness. In what follows, we present results from applying Recurrence Quantification Analysis (RQA) [32] to the population activity during the states of wakefulness (Wake), slow-wave sleep (SWS), and rapid-eye movement (REM) sleep, which explain the complexity changes observed from field recordings in various works [2,3,[8][9][10][11][12]14].…”

“…S3). To improve our understanding of previously reported results from field recordings, such as EEG, ECoG, or LFP [2,3,5,[8][9][10][11][12]14], we create synthetic local field-potentials, sLFP, and compare them with LFP recordings (Fig. 5).…”

“…The loss of temporal complexity is characteristic of unconsciousness -including deep sleep states -and can be widely observed in field recordings, such as electroencephalograms (EEG) [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Complexity can be defined as the diversity of patterns in a given signal, which approximately quantifies its information content.…”

“…High complexity is observed from field recordings during Wakefulness, decreasing as awareness is lost [1,2,[4][5][6]8,13,14], which suggests that consciousness needs a complex substrate [15,16]. Moreover, these complexity changes are conserved across species (such as mice [17], rats [10,11], monkeys [13], and humans [3,6,8,12]) and are influenced by circadian rhythms [18], age [19], and pathology [9,20], pointing to the existence of a fundamental state of cortical circuits during the sleep-wake cycle.…”

Decoding of the coupling between brain and skin activities in olfactory stimulation by analysis of EEG and GSR signals

“…On the other hand, many researchers utilized various types of entropy to analyze EEG signals. The works that evaluated the alterations of EEG signals using permutation entropy [17], approximate entropy [18], multiscale entropy [19], sample entropy [20], fuzzy entropy [21], and Shannon entropy [22] are worthy of being mentioned.…”