New Computational Approaches to Analysis of Interbeat Intervals in Human Subjects

Abstract

We investigate the Markov nature, Cascade of information from large time scale to small scale and extended self similarity properties of the beat to beat fluctuations of healthy subjects as well as those with congestive heart failure. To check the Markov nature, we use a novel inverse method that utilizes a set of data to construct a simple equation that governs the stochastic process for which the data have been measured, hence enabling us to reconstruct the stochastic process. The inverse method provides a novel technique for distinguishing the two classes of subjects in terms of a drift and a diffusion coefficients which behave completely differently for the two classes of subjects.To investigate the cascade of information from large to small time scales we also analyze the statistical properties of interbeat intervals cascade by considering the joint probability distribution for two interbeat increments. As a result, the joint probability distributions of the increments in the interbeat intervals obey a Fokker-Planck equation. Finally we analyze the extended self-similarity (ESS) in the beat-to-beat fluctuations in the heart rates of healthy and congestive heart failure subjects.The proposed methods provide the novel techniques for distinguishing the two classes of subjects in terms of the drift and diffusion coefficients, intermittency exponents which behave differently for two classes of the subjects, namely, healthy subjects and those with congestive heart failure.