An overview of observational sleep research with application to sleep stage transitioning (2009)

Sleep is an essential component of human existence, consuming roughly one third of our lives. Fatigue, jet-lag, poor sleep and vivid dreams are frequent points of our morning discussions. We look and feel terrible after getting too little sleep; hence a twenty billion dollar industry of beds, pillows, pills and other tools has cropped up to help us sleep better. Correspondingly, there are plenty of products designed to keep us awake.

Despite the importance of sleep in our lives, and the lives of so many other species, a definitive answer on the specific neuro-biological or physiologic purpose of sleep eludes researchers. However, substantial advancements in the field are uncovering the crucial role that sleep plays in our health, behavior and well being.

For example, studies of sleep duration have found associations with a variety of important health outcomes. Short sleep duration correlates with impaired cognitive function, hypertension, glucose intolerance, altered immune function, obesity and even mortality. This point is driven home by the fact that sleep deprivation is a well recognized form of torture. Consider the following quote from former Israeli Prime Minister Menachem Begin, who suffered forced sleep deprivation as a KGB prisoner:

In the head of the interrogated prisoner, a haze begins to form. His spirit is wearied to death, his legs are unsteady, and he has one sole desire: to sleep, to sleep just a little, not to get up, to lie, to rest, to forget... Anyone who has experienced this desire knows that not even hunger and thirst are comparable with it.

Quantity of sleep is only one measurable facet of sleep that is associated with health. Table 1 gives a few of the more common measurements of sleep and sleep disturbance. The next section describes sleep measurement in greater detail.



Table 1

Measurements of sleep taken during an overnight sleep study and routine clinical evaluation of sleep.



A common sleep disorder that is of particular public health interest is sleep apnea. This is a chronic condition characterized by collapses of the upper airway during sleep. Complete collapses lead to so-called “apneas”, whereas partial collapses to so-called “hypopneas”. Over the last decade, research has shown that these events can lead to several physiologic consequences, including changes in metabolism, glucose tolerance and cardiac function.

The respiratory disturbance index (RDI), sometimes also called the apnea/hypopnea index (AHI), is the principal measure of severity of sleep apnea. This rate index is the count of the number of apneas and hypopneas divided by the total time slept in hours. A severely affected patient may have an RDI of thirty events per hour or higher! Hence such a patient is, on average, having a disruption in their sleep and breathing every two minutes. As one can imagine, such frequent disruptions in sleep and oxygen intake can have negative health consequences. Young, Peppard and Gottlieb review that a high RDI has been shown to be associated with hypertension, cardiovascular disease, cerebrovascular disease, excessive daytime sleepiness, decreased cognitive function, decreased health-related quality of life, increased motor vehicle crashes and occupational accidents, and mortality.

In this article, we relate measures of sleep apnea with transitions that occur between “sleep states”. Sleep states are based on visual classification of brain electroencephalograms (EEGs) patterns (see below). Two major sleep states are rapid eye movement (REM) and non-REM. Sleep states can be seen as a categorical response time series. Crude summaries of these states, such as the percentage time spent in each one, are often used as predictors of health. Instead, we investigate the role that sleep apnea has on the rate of transitioning between the states. We emphasize that the rate of transitioning contains more important additional information than the crude percentage of time spent in each state. Notably, we use matching to account for other variables that might be related to both disease status and sleep behavior and hence compare a severely diseased group with a matched non-diseased group.

In the next section we give a brief overview of sleep measurement. Following that, we discuss an analysis of sleep transition rates comparing those with moderate to severe sleep apnea to those without. We end the article with a discussion.