Hypnogram and Hypnodensity Analysis of REM Sleep Behaviour Disorder Using Both EEG and HRV-Based Sleep Staging Models.

Abstract

Rapid-eye-movement (REM) sleep behaviour disorder (RBD) is a primary sleep disorder strongly associated with Parkinson's disease. Assessing sleep structure in RBD is important for understanding the underlying pathophysiology and developing diagnostic methods. However, the performance of automated sleep stage classification (ASSC) models is considered suboptimal in RBD, for both models utilising neurological signals ("ExG": EEG, EOG, and chin EMG) and heart rate variability combined with body movements (HRVm). Here, we explore this underperformance through the categorical representation of sleep macrostructure (i.e., hypnogram) and a representation that leverages the underlying probability distribution of ASSCs (i.e., hypnodensity). By comparing the RBD population (n = 36) to a sex- and age-matched group of OSA patients chosen for their anticipated similarly decreased sleep stability, we confirm lower 4-stage classification performance in both ExG-based ASSC (RBD: κ = 0.74, OSA: κ = 0.80) and HRVm-based ASSC (RBD: κ = 0.50, OSA: κ = 0.63). Stages showing lower agreement in RBD, namely, N1 + N2 and REM sleep, exhibited elevated ambiguity in the hypnodensity, indicating more ambiguous classification distributions. Limited differences in bout durations between RBD and OSA suggested sleep instability is not necessarily driving lower agreement in RBD. However, stage transitions in OSA showed more abrupt changes in the underlying probability distribution, while RBD transitions had a more continuous profile, possibly complicating classification. Although both ExG-based and HRVm-based automated sleep staging in RBD remain challenging, hypnodensity analysis is informative for the characterisation of (RBD) sleep and can capture potential drivers of classification disagreement.