Causal relationships are important to understand the dynamics of coupled processes and, moreover, to influence or control the effects by acting on the causes. Among the different approaches to determine cause-effect relationships and, in particular, coupling directions in interacting random or deterministic processes, we focus in this paper on information-theoretic measures. So, we study in the theoretical part the difference between...

We introduce an entropy-based classification method for pairs of sequences (ECPS) for quantifying mutual dependencies in heart rate and beat-to-beat blood pressure recordings. The purpose of the method is to build a classifier for data in which each item consists of two intertwined data series taken for each subject. The method is based on ordinal patterns and uses entropy-like indices. Machine learning is used to select a subset...

In this paper, we examined whether entropy-based methods are able to differentiate healthy individuals from patients with congestive heart failure. To this aim, we applied two methods: Permutation Entropy and Block Entropy. Long-term ECG recordings (75 000 RR intervals) were analyzed. The results proved that both methods can distinguish those groups on condition that the parameters are appropriately chosen.