  In this work we present a method to detect, identify and characterize
stochastic information contained in an electrocardiogram (ECG). We assume, as
it is well known, that the ECG has information corresponding to many different
processes related to the cardiac activity. We analyze scaling and Markov
processes properties of the detected stochastic information using the power
spectrum of the ECG and the Fokker-Planck equation respectively. The detected
stochastic information is then characterized by three measures. First, the
slope of the power spectrum in a particular range of frequencies as a scaling
parameter. Second, an empirical estimation of the drift and diffusion
coefficients of the Fokker-Planck equation through the Kramers-Moyal
coefficients which define the evolution of the probability distribution of the
detected stochastic information.
