Coupled Networks, Patterns and Complexity - Abstract
Makowiec, Danuta
Typical measurements of physiological signals, like RR-signals, i.e., series of time intervals between subsequent heart contractions, usually provide characteristics with limited set of values. Therefore any discrete method applied to them in further analysis demands a special care. In the following we propose the method of processing of the RR-signal which leads to a discrete space of states of changes in the RR-signal, and then allows to construct the network which represents transitions between the changes. By comparing networks obtained from actual RR-signals to surrogate data (randomly shuffled RR-signals) we have found that the construction offers a significantly new representation of RR-signal properties. In particular, we have searched for the essential part --- the, so-called, core, of these networks. We have found that the cores alter critically with respect to the value of the threshold for the probability of transitions which are included into the core. The critical value of the threshold evidently differentiates real RR-signals from surrogate series. Moreover, in human RR-signals we have found a marked asymmetry in patterns describing events of accelerations and decelerations. After a large acceleration, namely larger than 48 msec, it is more likely that a deceleration would occur (antipersitency) but not vice versa.