Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 20081Quantifying sleep fragmentation is central in assessment of sleep quality. Traditionally, measures such as the arousal frequency and sleep-stage percentages have been used to ap-praise 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 as-sociated with conditions that fragment sleep such as sleep-dis-ordered breathing (SDB).1-3 Furthermore, many of the conven-tional measures provide only 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 ad-dition, a careful portrayal of sleep-stage transitions is essential in clarifying the putative mechanisms through which conditions such as 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 electroencepha-logram (EEG),6 sleep spectrograms based on cardiopulmonary coupling,7 and visual identification of cyclical alternating pat-terns8 in the 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 apprecia-tion of the associated limitations. With improvements in digital technology, many of aforementioned techniques are automated and being increasingly incorporated in commercially avail-able sleep-scoring software.9 A relatively underutilized, but universally available, method for assessing sleep continuity is the hypnogram. The graphic representation of sleep-stage se-quence across the night provides a visual depiction of the nor-mal ultradian cycling of sleep. While the hypnogram provides a Characterizing Sleep Structure Using the HypnogramBruce J. Swihart1; Brian Caffo, Ph.D.1; Karen Bandeen-Roche, Ph.D.1; Naresh M. Punjabi, M.D., Ph.D.21Department of Biostatistics and 2Medicine, Johns Hopkins University, Baltimore, MDDisclosure StatementThis was not an industry supported study. The authors have indicated no financial conflicts of interest. Submitted for publication July, 2006Accepted for publication March, 2008Address correspondence to: Naresh M. Punjabi, MD, PhD, Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, 5501 Hopkins Bayview Circle, Baltimore, MD 21224; Tel: (410) 550-2612 ; Fax: (410) 550-5405 ; E-mail: [email protected] inveStigAtionSobjectives: Research on the effects of sleep-disordered breathing (SDB) on sleep structure has traditionally been based on composite sleep-stage summaries. The primary objective of this investigation was to demonstrate the utility of log-linear and multistate analysis of the sleep hypnogram in evaluating differences in nocturnal sleep structure in subjects with and without SDB.Methods: A community-based sample of middle-aged and older adults with and without SDB matched on age, sex, race, and body mass index was identified from the Sleep Heart Health Study. Sleep was assessed with home polysomnography and categorized into rapid eye movement (REM) and non-REM (NREM) sleep. Log-linear and mul-tistate survival analysis models were used to quantify the frequency and hazard rates of transitioning, respectively, between wakefulness, NREM, and REM sleep.Results: Whereas composite sleep-stage summaries were similar be-tween the two groups, subjects with SDB had higher frequencies and hazard rates for transitioning between the three states. Specifically, log-linear models showed that subjects with SDB had more wake-to-NREM sleep and NREM sleep-to-wake transitions, compared with subjects without SDB. Multistate survival models revealed that sub-jects with SDB transitioned more quickly from wake-to-NREM sleep and NREM sleep-to-wake than did subjects without SDB.conclusions: The description of sleep continuity with log-linear and multistate analysis of the sleep hypnogram suggests that such meth-ods can identify differences in sleep structure that are not evident with conventional sleep-stage summaries. Detailed characterization of nocturnal sleep evolution with event history methods provides addi-tional means for testing hypotheses on how specific conditions impact sleep continuity and whether sleep disruption is associated with ad-verse health outcomes.Keywords: Sleep disruption, sleep-disordered breathing, sleep struc-ture and event history modelingcitation: Swihart BJ; Caffo B; Bandeen-Roche K; Punjabi NM. Char-acterizing sleep structure using the hypnogram. J Clin Sleep Med 2008;4(4):XXX-XXX.Journal of Clinical Sleep Medicine, Vol. 4, No. 4, 20082BJ Swihart, B Caffo, K Bandeen-Roche, et al Sleep Structure in Sleep-disordered Breathingqualitatively description of sleep structure, quantitative metrics 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 in-trascorer 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 in-creases the frequency of sleep-stage transitions but has no ma-terial 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