Early detection of Ventricular Bigeminy/Trigeminy rhythms
Keywords:Premature Ventricular Contraction, Random Forest, Machine Learning
Premature Ventricular Contraction is an arrhythmia that can be associated with several cardiac disorders that affect from 40% to 75% of the general population. Premature Ventricular Contraction occurrence is diagnosed from the Electrocardiogram. If in an Electrocardiogram one (or two) Premature Ventricular Contraction occurs between two Normal heartbeats, then there is a Ventricular Bigeminy (or Trigeminy). The prevalence of Ventricular Bigeminy/Trigeminy rhythms is associated with angina, hypertension, congestive heart failure and myocardial infarction. In this work it is proposed a new approach for these rhythms early diagnosis using Decision Tree models. The proposed approach uses the information before occurrence of Ventricular Bigeminy/Trigeminy, i.e., the number of normal and abnormal heartbeats and the heart rhythm. In order to rhythm prediction, the models obtained from Random Forest algorithm, induced by cross-validation approach, are used. Proposed approach predicted Ventricular Bigeminy/Trigeminy occurrence with accuracy, sensitivity and specificity of 98.94%, 96.28% and 99.83, respectively. Furthermore, the results showed that the Ventricular Bigeminy/Trigeminy is preceded for Normal, Atrioventricular Junctional and Paced heart rhythms in most of the examples. Besides that, it is presented a simple algorithm for decision about the occurrence of Ventricular Bigeminy/Trigeminy rhythms.
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