Early detection of Ventricular Bigeminy/Trigeminy rhythms

Bruno Rodrigues De Oliveira,Jozue Vieira Filho,Marco Aparecido Queiroz Duarte

doi:10.33837/msj.v5i1.1525

Abstract

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.

Highlights

Heart rhythm depends on the specific initialization and propagation of the electrical impulses from the specialized cardiac cells
It has been studied by several researchers, who have proposed algorithms for its automatic recognition based on two template-matching procedures using the correlation coefficients (Li et al, 2014); eigenvectors obtained from Principal Component Analysis and Linear regression analysis (Hadia et al, 2017); Gaussian process classifiers, wavelet and S transforms (Bazi et al, 2013); geometrical features and ensemble of machine learning using analytic hierarchy process (Oliveira et al, 2019)
Such studies are very important because Premature Ventricular Contraction (PVC) can be associated to the risk of sudden death (Fred, 2009) and a high frequency of its occurrence can lead to hemodynamic problems (Garcia & Miller, 2004)

Summary

Introduction

Heart rhythm depends on the specific initialization and propagation of the electrical impulses from the specialized cardiac cells. When the ventricles assume the function of pacemaker, Premature Ventricular Contraction (PVC) is characterized This arrhythmia is due to three main effects: abnormal impulse formation, reentry and triggered activity (Latchamsetty & Bogun, 2015). It has been studied by several researchers, who have proposed algorithms for its automatic recognition based on two template-matching procedures using the correlation coefficients (Li et al, 2014); eigenvectors obtained from Principal Component Analysis and Linear regression analysis (Hadia et al, 2017); Gaussian process classifiers, wavelet and S transforms (Bazi et al, 2013); geometrical features and ensemble of machine learning using analytic hierarchy process (Oliveira et al, 2019). Such studies are very important because PVC can be associated to the risk of sudden death (Fred, 2009) and a high frequency of its occurrence can lead to hemodynamic problems (Garcia & Miller, 2004)

Methods

Results

Conclusion