Multiscale analysis of short-term cardiorespiratory signals

We present the multi-scale 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 new quantitative indicators which are statistically correlated with the pathology. As the multi-scale entropy analysis has been applied up to now to 24 hours electrocardiographic signals, these results on short-term recordings enlarge the applicability of the method. In the same spirit of the multi-scale entropy approach, we also propose a multi-scale approach, to evaluate interactions between time series, by performing a multivariate autoregressive modelling of the coarse grained time series. We then address the problem of classifying a subject as healthy or affected by Chronic Heart Failure on the basis of all the collected indicators.