Modelling and exploring human sleep with event history analysis

• Published in: Journal of sleep research Vol. 8; no. 1; pp. 25 - 36
• Main Authors: YASSOURIDIS, ALEXANDER, STEIGER, AXEL, KLINGER, ARTUR, FAHRMEIR, LUDWIG
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Science Ltd 01.03.1999
• Subjects: Adult; cortisol; Electroencephalography; event history analysis; Humans; Hydrocortisone - blood; Male; Models, Statistical; relative intensity; sleep; Sleep, REM - physiology; Time Factors; Wakefulness
• ISSN: 0962-1105; 1365-2869
• DOI: 10.1046/j.1365-2869.1999.00133.x

Summary In this paper we propose the use of statistical models of event history analysis for investigating human sleep. These models provide appropriate tools for statistical evaluation when sleep data are recorded continuously over time or on a fine time grid, and are classified into sleep stages such as REM and nonREM as defined by Rechtschaffen and Kales (1968). In contrast to conventional statistical procedures, event history analysis makes full use of the information contained in sleep data , and can therefore provide new insights into non‐stationary properties of sleep. Probabilities of or intensities for transitions between sleep stages are the basic quantities for characterising sleep processes. The statistical methods of event history analysis aim at modelling and estimating these intensities as functions of time, taking into account individual sleep history and assessing the influence of factors of interest, such as hormonal secretion. In this study we suggest the use of non‐parametric approaches to reveal unknown functional forms of transition intensities and to explore time‐varying and non‐stationary effects. We then apply these techniques in a study of 30 healthy male volunteers to assess the mean population intensity and the effects of plasma cortisol concentration on the transition between selected sleep stages as well as the influence of elapsed time in a current REM period on the intensity for a transition to nonREM. The most interesting findings are that (a) the intensity of the nonREM‐to‐REM transitions after sleep onset in young men shows a periodicity which is similar to that of nonREM/REM cycles; (b) 30–45 min after sleep onset, young men reveal a great propensity to pass from light sleep (stages 1 or 2) into slow‐wave sleep (SWS) (stages 3 or 4); (c) high cortisol levels imposed additional impulses on the transition intensity of (i) wake to sleep around 2 h after sleep onset, (ii) nonREM to REM around 6 h later, (iii) stage 1 or stage 2 sleep to SWS around 2, 4 and 6 h later and (iv) SWS to stage 1 or stage 2 sleep about 2 h later. Moreover, high cortisol concentrations at the beginning of REM periods favoured the change to nonREM sleep, whereas later their influence on a nonREM change became weak and weaker. As sleep data are also available as event‐oriented data in many studies in sleep research, event history analysis applied additionally to conventional statistical procedures, such as regression analysis or analyis of variance, could help to acquire more information and knowledge about the mechanisms behind the sleep process.