Night-to-night sleep EEG variability over one year

Abstract

This study aimed to explore night-to-night variability of multiscale sleep patterns by analyzing subcutaneous electroencephalography (sqEEG) from 20 healthy participants over one year (205-388 nights per participant, 6,429 nights in total). We utilized the time series of aperiodic slopes, sigma and slow-wave power as a new whole-night unit of sleep macrostructure. Using dynamic time warping, we calculated the distances (differences) between those time series to assess night-to-night sleep macrostructure dissimilarity. We found that the overall sleep macrostructural patterns were relatively similar across nights (20% dissimilarity), while their temporal alignment was quite variable (time series warped by [~]60% for the best alignment). Lower variation in macrostructure dissimilarity was associated with better subjective sleep quality (r=-0.25). Then, we qualitatively compared yearlong variation in macroscale, microscale (sleep stage proportions, mean spectral power) and mesoscale (sleep cycle duration) metrics. We found that intra-individual night-to-night variation was "low" (coefficients of variation < 20%) for spectral power, sleep duration, N2 and REM sleep; "medium" (20-40%) - for N3 and macrostructure dissimilarity; and "high" (>40%) - for sleep cycle duration, wake and N1. In summary, different sleep metrics showed differential night-to-night variability, which was more metric-specific than scale-dependent. This might reflect a distinction between more trait-like versus more dynamically varying features of sleep, although this assumption needs further clarification. Significance statementThe degree of the intra- and inter-individual night-to-night variability of sleep metrics at different scales shows more metric-specific than scale-dependent patterns and potentially distinguishes between trait-like and state-like features of sleep. Detailed description of the multiscale sleep structure and its night-to-night variability/stability might eventually lead to identification of electrophysiological fingerprints and bring insights about their physiological significance. The potential implications of this include the development of personalized time-sensitive recommendations for people facing (social) jet lag, shift work and other deviations from normal sleep patterns. In clinical settings, this might advance the tools to diagnose and monitor sleep alterations, predict nocturnal seizures in epilepsy care, instability in breathing control in apnea etc.