Abstract
The amount and quality of sleep substantially influences health, daily behaviour and overall quality of life. The
main goal of this study was to investigate to what extent sleep structure, as derived from the polysomnographic
(PSG) recordings of nocturnal human sleep, can provide information about sleep quality in terms of correlating
with a set of variables representing the daytime subjective, neurophysiological and cognitive states of a healthy
population without serious sleep problems. We focused on a continuous sleep representation derived from the
probabilistic sleep model (PSM), which describes the microstructure of sleep by a set of sleep probabilistic curves
representing a finite number of sleep microstates. This contrasts with approaches where sleep is characterised by
a set of one-dimensional sleep measures derived from the standard discrete sleep staging. Considering this
continuous sleep representation, we aimed to identify typical sleep profiles that represent the dynamic aspect of
sleep during the night and that are associated with a set of studied daily life quality measures.
Cluster analysis of sleep probabilistic curves has proven to be a helpful tool when identifying specific sleep
temporal profiles, but it faces problems when curves are complex and time misalignment is present. To overcome
these problems, we proposed and validated a novel 2-step iterative clustering and time alignment method. We
compared the quality of alignment and cluster homogeneity produced by the method with existing approaches in
which (i) the time alignment of curves precedes the clustering step, and (ii) time alignment and clustering are
performed simultaneously.
The obtained homogeneous clusters of REM, Wake and Slow Wave Sleep resembled the clustering structure of
subjects with significantly different subjective scores of sleep quality and mood, as well as more objective
cognitive test scores. Moreover, the sleep profiles associated with individual clusters help to better understand
the existing associations between the overnight dynamics of specific sleep states and daily measures.
Go back