Investigating the underlying Markovian dynamics of ECG rhythms by information flow

Several approaches have been recently introduced to characterize and classify signals based on the underlying hidden dynamic. Markov models represent a natural choice for describing the dynamic evolution of a signal. However, the right selection of the memory of the process is essential for the correctness of the Markov model and a mathematically well-founded criterion is necessary to establish when the Markov model is a good approximation of the process. We review an information-theoretic based method that introduces the concept of information flow as such a criterion. The information flow describes the progressive loss of statistical dependence between the entire past and a point ahead in the future, which is indirectly related with the hidden dynamic of the signal. An approximated measure of information flow can be used as the discriminating statistic for selecting the optimal Markov model in terms of the shortest memory required. This technique is applied to investigate the underlying Markovian dynamics of the heart rate variability (HRV) for subjects in different patho-physiological conditions. Markov models with different memories seem to be associated with the circadian cycle and with different pathologies. Furthermore, the precursor character of the information flow for predicting ventricular tachycardia (VT) is discussed.