  We present the multiscale entropy analysis of short term physiological time
series of simultaneously acquired samples of heart rate, blood pressure and
lung volume, from healthy subjects and from subjects with Chronic Heart
Failure. Evaluating the complexity of signals at the multiple time scales
inherent in physiologic dynamics, we find that healthy subjects show more
complex time series at large time scales; on the other hand, at fast time
scales, which are more influenced by respiration, the pathologic dynamics of
blood pressure is the most random. These results robustly separate healthy and
pathologic groups. We also propose a multiscale approach to evaluate
interactions between time series, by performing a multivariate autoregressive
modelling of the coarse grained time series: this analysis provides several new
quantitative indicators which are statistically correlated with the pathology.
