Stochastic Petri Net Modeling of Wave Sequences in Cardiac Arrhythmias

摘要

We describe a methodology for modeling heart rhythms observed in electrocardiograms. In particular, we present a procedure to derive simple dynamic models that capture the cardiac mechanisms which control the particular timing sequences of P and R waves characteristic of different arrhythmias. Important aspects of our models are their ease of construction and conciseness. Specifically, these models consist of a structural level, representing interactions among various cardiac electrical events, and a parameter level, defining timing statistics of these events and their interactions. The modeling procedure is a two-step process: By treating the cardiac electro-physiology at an aggregate level, simple network models of the wave generating system under a variety of diseased conditions can be developed. These network models are then systematically converted to stochastic Petri nets which offer a compact mathematical framework to express the dynamics and statistical variability of the wave generating mechanisms. Models of several arrhythmias are included in order to illustrate the methodology. One potential application for these models is in the development of automatic classification schemes for cardiac arrhythmias, as the models can be used as the basis for hypothesis testing and parameter estimation algorithms.