Modeling Electrocardiograms Using Interacting Markov Chains.
MASSACHUSETTS INST OF TECH CAMBRIDGE LAB FOR INFORMATION AND DECISION SYSTEMS
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In this paper we develop a methodology for the statistical modeling of cardiac behavior and electrocardiograms ECGs that emphasizes a the physiological eventdetailed waveform hierarchy and b the importance of control and timing in describing the interactions among the several anatomical subunits of the heat. This methodology has been motivated by a desire to develop improved algorithms for statistical rhythm analysis that capture cardiac behavior in a more fundamental way but that stops short of complete accuracy in order to highlight decompositions that can be exploited to simplify statistical inference based on these models. Out models consist of interacting finite-state processes, where a very few of the transition probabilities for each process can take on a small number of different values depending upon the states of neighboring processes. Each finite-state process is constructed from a very small set of elementary structural elements. We illustrate our methodology by describing models for three cardiac rhythms and include simulation results for one of these, namely the rhythm known as Wenckebach.
- Medicine and Medical Research