Turnover is a typical intermittent body movement while asleep.
Exploring its behavior may provide insights into the mechanisms and
management of sleep. However, little is undertood about the dynamic
nature of turnover in healthy humans and how it can be modified in
disease. Here we present a detailed analysis of turnover signals that
are collected by accelerometry from healthy elderly subjects and
age-matched patients with neurodegenerative disorders such as
Parkinson's disease. In healthy subjects, the time intervals between
consecutive turnover events exhibit a well-separated bimodal
distribution with one mode at less than equal 10 s and the other at
greater than equal 100 s, whereas such bimodality tends to disappear
in neurodegenerative patients. The discovery of bimodality and fine
temporal structures (less than equal 10 s) is a contribution that is
not revealed by conventional sleep recordings with less time
resolution (approximately equal 30 s). Moreover, we estimate the
scaling exponent of the interval fluctuations, which also shows a
clear difference between healthy subjects and patients. We incorporate
these experimental results into a computational model of human
decision making. A decision is to be made at each simulation step
between two choices: to keep on sleeping or to make a turnover, the
selection of which is determined dynamically by comparing a pair of
random numbers assigned to each choice. This decision is weighted by a
single parameter that reflects the depth of sleep. The resulting
simulated behavior accurately replicates many aspects of observed
turnover patterns including the appearance/disappearance of bimodality
and leads to several predictions, suggesting that the depth parameter
may be useful as a quantitative measure for differentiating between
normal and pathological sleep. These findings have significant
clinical implications and may pave the way for the development of
practical sleep assessment technologies.
