Rhythms and Background (RnB): The Spectroscopy of Sleep Recordings

Non-rapid eye movement (NREM) sleep is characterized by the interaction of multiple coupled oscillations essential for various functions such as memory consolidation, alongside a pervasive and dynamic arrhythmic 1/f scale-free background that may also contribute to these functions. Although recent spectral parametrization methods such as FOOOF (Fitting-One-and-Over-f) allowed to dissociate rhythmic and arrhythmic components in the spectral domain, they fail to resolve these processes in the time domain, where instantaneous measures of frequency, amplitude, and phase-amplitude coupling are still confounded by arrhythmic activity. This limitation represents a significant pitfall for studies of NREM sleep, which often rely on phase-based analyses of specific oscillations. To address this limitation, we introduce Rhythms & Background (RnB), a novel wavelet-based methodology designed to dynamically denoise time-series data by correcting for arrhythmic interference. This enables the extraction of a purely rhythmic time-series suitable for enhanced time-domain analyses of sleep rhythms. We first validate RnB through simulations, demonstrating its robust performance in accurately estimating spectral profiles of individual and multiple oscillations across a range of arrhythmic conditions. We then apply RnB to publicly available intracranial EEG sleep recordings, showing that it provides an improved spectral and time-domain representation of hallmark NREM rhythms. Finally, we demonstrate that RnB significantly enhances the assessment of phase-amplitude coupling between cardinal NREM oscillations, outperforming traditional methods that conflate rhythmic and arrhythmic components. This methodological advance offers a substantial improvement in the analysis of sleep oscillations, providing greater precision in the study of rhythmic activity critical to NREM sleep functions. SIGNIFICANCE STATEMENTThe Rhythms and Background (RnB) algorithm introduces a novel approach to signal processing in electrophysiology by isolating rhythmic activity from the arrhythmic background at the time-series level. Unlike existing spectral decomposition methods, RnB enables more precise analysis of brain rhythms in both the time and spectral domains, providing clearer insights into cerebral oscillatory processes. This breakthrough has direct applications in studying brain connectivity and oscillation dynamics during sleep. Additionally, its application in clinical populations where pathological changes in arrhythmic activity are common, such as neurodevelopmental and neurodegenerative disorders, will help to better understand abnormal oscillatory processes. By improving the accuracy of rhythmic signal analysis, RnB opens new avenues for understanding brain function and dysfunction in research and clinical settings.