![]() 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. Methods to describe temporal histories as depicted in the hypnogram are common in epidemiologic studies but have had limited application in sleep medicine. Tabulating the number of sleep-stage shifts can be helpful 10, 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). 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. 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. Visual scoring of arousals is labor intensive, time consuming, and fraught with low to modest interscorer and intrascorer reliability. 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. The graphic representation of sleep-stage sequence across the night provides a visual depiction of the normal ultradian cycling of sleep. 9 A relatively underutilized, but universally available, method for assessing sleep continuity is the hypnogram. With improvements in digital technology, many of aforementioned techniques are automated and being increasingly incorporated in commercially available software. Although these techniques provide unique insight into sleep continuity, their use requires specialized expertise along with an appreciation of the associated limitations. Power spectral analysis of the sleep electroencephalogram (EEG), 6 sleep spectrograms based on cardiopulmonary coupling, 7 and visual identification of cyclical alternating patterns 8 in sleep EEG have revealed clinically meaningful changes in the sleep structure in health and disease. Several techniques have been used to derive measures of sleep quality that complement the repertoire of traditional metrics. 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. Given the remarkable progress in our understanding of the neurobiology of the sleep-wake switch 4 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. 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. 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. Traditionally, measures such as the arousal frequency and sleep-stage percentages have been used to appraise sleep quality in research and clinical practice. Quantifying sleep fragmentation is central in assessment of sleep quality.
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