Nonlinearities in heart rate variability
Jørgen K. Kanters
Laboratory of Experimental Cardiology, Dept. of Medical Physiology,
University of Copenhagen, Blegdamsvej 3C, Copenhagen, Denmark
The heartbeat is not regular but varies in an apparent irregular matter.
This variation termed heart rate variability (HRV) is not only due to
changes in activity or respiration, but occurs even at rest or at night.
Atropine abolishes most of these variations, indicating the significance of
parasympathetic tone. The importance of HRV was demonstrated by Kleiger.
He showed that decreased HRV measured in the time domain as the standard
deviation of 24-h RR-intervals in msec (SDNN), was a strong predictor of
mortality after myocardial infarction. However the clinical utility of SDNN
is limited, and only permits the identification of a high risk group, but
lacks the sensitivity and the specificity to identify individual subjects at
high risk for sudden death.
The drawback of time and frequency domain measures is that they only take
linear information into account ignoring any form of nonlinear dynamics.
Previous studies have shown that a small but significant amount of nonlinear
dynamics exists in HRV. Various methods exist to quantify nonlinearities
in HRV. Strange attractors have been postulated by plotting a RR interval
against the following one, revealing different patterns in healthy subjects
and patients suffering sudden cardiac death. These patterns can be
quantified by determining their dimension, ideally giving the minimum number
of independent variables needed to describe HRV. Specific structures in HRV
can be identified using nonlinear predictability or neural networks showing
that some specific dynamics exist in HRV. Newer methods introducing
nonlinear autoregressive modelling seems to be able to identify these
characteristic patterns. The strength of these methods is the ability to
discriminate between inherent dynamics and external perturbations (noise).
There has been much effort to apply these methods in the clinical setting.
Preliminary studies has shown that dimension measures can distinguish
patients that later developed ventricular fibrillation from those who did
not, despite that the groups had the same SDNN. Unfortunately, we still need
larger studies to establish the role of nonlinear dynamics in the clinical
setting for predicting sudden cardiac death.
 R.E. Kleiger et al., Am J Cardiol, 59: 256-262, 1987.
 J.K. Kanters et al. J Cardiovasc Electrophysiol, 5:591-601, 1994