Characterizing sleep structure using the hypnogram

Quantifying sleep fragmentation is central in assessment of sleep quality. Traditionally, measures such as the arousal frequency and sleep-stage percentages have been used to appraise sleep quality in research and clinical practice. Although conventional metrics of sleep structure have provided useful insight into the biology of sleep, these parameters explain only part of the variance in outcomes such as daytime sleepiness associated with conditions that fragment sleep.1–3 Furthermore, many of the conventional measures provide an overall summary of the entire night and unable to capture the temporal evolution of overnight events, the frequency of sleep-stage transitions, and the time between these transitions. Given the remarkable progress in our understanding of the neurobiology of the sleep-wake switch4 and the underlying neural circuitry responsible for transitioning between rapid eye movement (REM) and non-REM (NREM) sleep,5 adequately characterizing sleep-stage transitions is a priority to better define the influence of specific factors (e.g., age and sex) on normal sleep structure and organization. In addition, a careful portrayal of sleep-stage transitions is essential in clarifying the putative mechanisms through which conditions such as sleep-disordered breathing (SDB) mediate adverse health outcomes. Several techniques have been used to derive measures of sleep quality that complement the repertoire of traditional metrics. Power spectral analysis of the sleep electroencephalogram (EEG),6 sleep spectrograms based on cardiopulmonary coupling,7 and visual identification of cyclical alternating patterns8 in sleep EEG have revealed clinically meaningful changes in the sleep structure in health and disease. Although these techniques provide unique insight into sleep continuity, their use requires specialized expertise along with an appreciation of the associated limitations. With improvements in digital technology, many of aforementioned techniques are automated and being increasingly incorporated in commercially available software.9 A relatively underutilized, but universally available, method for assessing sleep continuity is the hypnogram. The graphic representation of sleep-stage sequence across the night provides a visual depiction of the normal ultradian cycling of sleep. While the hypnogram provides a qualitative description of sleep structure, quantitative measures based on the hypnogram are not as commonly used in research or clinical practice as are other measures such as the frequency of arousals. Visual scoring of arousals is labor intensive, time consuming, and fraught with low to modest interscorer and intrascorer reliability. Even when coupled with the distribution of sleep-stage amounts, the frequency of arousals is unable to characterize the full extent of information embedded within the hypnogram. It is certainly plausible that a clinical disorder increases the frequency of sleep-stage transitions but has no material impact on the total amount of time spent in each stage or perhaps even the number of arousals. Tabulating the number of sleep-stage shifts can be helpful10,11 but is insufficient because it describes only one dimension of the hypnogram (i.e., number of shifts) while neglecting another (i.e., the time spent in a sleep stage before transitioning). Methods to describe temporal histories as depicted in the hypnogram are common in epidemiologic studies but have had limited application in sleep medicine. Although event-history models have been previously used in the context of examining determinants of sleep latency, such methods have not been employed in assessing the sleep-stage transitions and quantifying the impact of SDB on sleep structure.12–16 Thus, the primary objective of the current investigation was to determine whether event-history models are able to quantify sleep fragmentation using the overnight hypnogram. Specifically, log-linear models and multistate survival analysis methods were used to model the number and rate of sleep-stage transitions, respectively, in a community sample of middle-aged and older adults with and without SDB.