  In this work we present a new idea to develop a method to separate stochastic
and deterministic information contained in an electrocardiogram, ECG, which may
provide new sources of information with diagnostic purposes. We assume that the
ECG has information corresponding to many different processes related with the
cardiac activity as well as contamination from different sources related with
the measurement procedure and the nature of the observed system itself. The
method starts with the application of an improuved archetypal analysis to
separate the mentioned stochastic and deterministic information. From the
stochastic point of view we analyze Renyi entropies, and with respect to the
deterministic perspective we calculate the autocorrelation function and the
corresponding correlation time. We show that healthy and pathologic information
may be stochastic and/or deterministic, can be identified by different measures
and located in different parts of the ECG.
