1/20/2024 0 Comments Hypnogram examples![]() ![]() (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) This figure appears in color in the electronic version of this article. Green points represent a full and flat paradigm, where all S( S − 1) transition-types are modeled for the S states considered. The proposed approach, Swihart 2015, is handling more subjects, more groups, more states and more transition-types. On the vertical axis are the labels for the cited paper, and in each facet they are ordered based on the attribute of the facet. Four facets are displayed: Number of Subjects, Number of Groups, Number of States, and Number of Transition-Types. (in press) implemented a random effects Bayesian Poisson regression.Ī summarization of statistical methodology literature with sleep applications. (2008) analyzed the full 3-state 6 transition-type paradigm with Poisson regression for relative transition counts and multi-state survival models for relative transition rates to study sleep stability. For S states, a full and flat model paradigm would include all S × (S − 1) pairwise transition-types. (2006) established differences in the stability of sleep and severity of sleep-disordered breathing in a sleep-runs analysis (which is a 2-state 1-transition-formulation with no intra-subject repeated events clustering). For example, previous statistical methodology for sleep focused on an important clinical goal of relating time-varying hormone levels to the sleep process modeled as a reduced set of transition-types for the number of states considered (see Fahrmeir and Klinger, 1998 Yassouridis et al., 1999 Aalen et al., 2004 Kneib and Hennerfeind, 2008 Kalus et al., 2009). Hypnogram data have been the topic of previous analytic frameworks and data applications demonstrated on modest sample sizes involving one homogeneous group or for comparing no more than two groups. To remove potential confusion about novelty, we provide a brief review of related publications with the problems they addressed, along with a summarizing graphic ( Fig. Previous methods used in the analysis of hypnogram data Given that every 30 s the trajectory can change to any other of the four states or remain in the current state makes for a diverse functional space for one hour snippets of three individuals, let alone for an overall sleep time of typically 7 h for the thousands of individuals in sleep epidemiology investigations.ġ.1. The top panel fits the generalization well, but the other two do not, with many detours and interruptions to the charted course of a “typical” sleep: the middle panel has more alternations between Stage 2 and Stage Slow-wave while the bottom panel has a duration in Wake before leaving Stage 1 and a much more fragmented Stage Slow-wave portion. 1, sleep is often more complex than the purportedly ‘typical’ pattern shown of the top panel. Note: these hypnograms ignore the first transition from Wake. The horizontal axis denotes time from sleep onset in 30-s epochs for 60 min. The vertical axis displays the five stages of sleep: Wake, Rapid-Eye Movement (REM), Stage 1, Stage 2, and Stage Slow-wave labeled “W”, “R”, “1”, “2”, “S”, respectively. The first hour of sleep for three individuals, visualized by discrete-time discrete-state spaghetti plots known as sleep hypnograms. The example data and both R and SAS code are included in online supplementary materials. The analysis provides detailed new insights into the association between sleep-disordered breathing and sleep architecture. Application of these methods to the hypnograms of 5598 subjects from the Sleep Heart Health Study provide: the first analysis of sleep hypnogram data of such size and complexity in a community cohort with a range of sleep-disordered breathing severity introduce a novel approach to compare 5-state (20-transition-type) to 3-state (6-transition-type) sleep structures to assess information loss from combining sleep state categories extend current approaches of multivariate survival data analysis to clustered, recurrent event discrete-state discrete-time processes and provide scalable solutions for data analyses required by the case study. Methods are introduced for the analysis of large sets of sleep study data (hypnograms) using a 5-state 20-transition-type structure defined by the American Academy of Sleep Medicine. ![]()
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