Key result
A Random Forest machine learning approach predicted the occurrence of Ventricular Bigeminy and Trigeminy rhythms with an accuracy of 98.94%, sensitivity of 96.28%, and specificity of 99.83%.
Why the study?
Ventricular Bigeminy and Trigeminy rhythms are associated with cardiac disorders such as angina, hypertension, heart failure, and myocardial infarction, prompting the development of a method for their early diagnosis.
Can a Random Forest-based Decision Tree model accurately predict the occurrence of Ventricular Bigeminy/Trigeminy using preceding ECG segment data?
Can a Random Forest-based Decision Tree model accurately predict the occurrence of Ventricular Bigeminy/Trigeminy using preceding ECG segment data?
A simple Decision Tree model using preceding ECG rhythm and heartbeat counts can accurately predict the onset of Ventricular Bigeminy and Trigeminy.
May prompt angina evaluation in bigeminy patients; leaves open causal links and management impact.
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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Oliveira et al. (2022) studied Ventricular Bigeminy/Trigeminy rhythms. Random Forest algorithm was evaluated on Prediction of Ventricular Bigeminy/Trigeminy occurrence. A Random Forest machine learning approach predicted the occurrence of Ventricular Bigeminy and Trigeminy rhythms with an accuracy of 98.94%, sensitivity of 96.28%, and specificity of 99.83%.
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