Summary
Background: Physiological sleep is characterized by different cyclic phenomena, such as REM,
nonREM phases and the Cyclic Alternating Pattern (CAP), that are associated to characteristic
patterns in the heart rate variability (HRV) signal. Disruption of such rhythms due
to sleep disorders, for example insomnia or apnea syndrome, alters the normal sleep
patterns and the dynamics of the HRV recorded during the night.
Objectives: In this paper we analyze long-term and complexity dynamics of the HRV signal recorded
during sleep in different groups of subjects. The aim is to investigate whether the
calculated indices are able to capture the different characteris tics and to discriminate
among the groups of subjects, classified according sleep disorders or cardiovascular
pathologies.
Methods: Parameters, able to detect the fractal-like behavior of a signal and to measure the
regularity and complexity of a time series, are calculated on the HRV signal acquired
during the night. Different groups of subjects were analyzed: healthy subjects with
high sleep efficiency, healthy subjects with low sleep efficiency, subjects affected
by insomnia, heart failure patients, subjects affected by obstructive sleep apnea.
Results: The evaluated parameters show significant differences in the groups of subjects considered
in this work. In particular heart failure patients have significant lower entropy
and complexity values, whereas apnea patients show an increased irregularity when
compared with normal subjects with high sleep efficiency.
Conclusions: This work proposes indices that can be used as global descriptors of the dynamics
of the whole night and can discriminate among different groups of subjects.
Keywords
Entropy - detrended fluctuation analysis - 1/f slope - symbolic dynamics
