ABSTRACT
Heart rate variability, that is, the spontaneous fluctuations of the inverse of heart period (HP) over time, is one of the most studied physiological time series. The key features of its success are: the relevance of the information encoded in it, thus making more and more clinically relevant HP variability assessment; and the richness of the observed dynamics, thus prompting for the application of virtually any tool for signal processing to it. ECG, continuous plethysmographic arterial pressure, and respiratory movements via thoracic belt were digitalized using a commercial device. After detecting the QRS complex on the ECG and locating the peak of the QRS complex using parabolic interpolation, HP was approximated as the temporal distance between two consecutive parabolic apexes. This chapter stresses the importance of applying a model-free data-driven multivariate approach for the characterization of HP variability and its dependence on variations of physiological variables different from HP.
