  In this paper we show that different physiological states and pathological
conditions may be characterized in terms of predictability of time series
signals from the underlying biological system. In particular we consider
systolic arterial pressure time series from healthy subjects and Chronic Heart
Failure patients, undergoing paced respiration. We model time series by the
regularized least squares approach and quantify predictability by the
leave-one-out error. We find that the entrainment mechanism connected to paced
breath, that renders the arterial blood pressure signal more regular, thus more
predictable, is less effective in patients, and this effect correlates with the
seriousness of the heart failure. The leave-one-out error separates controls
from patients and, when all orders of nonlinearity are taken into account,
alive patients from patients for which cardiac death occurred.
