Data Augmentation for Discrimination of Atrial Flutter Mechanism Using 12-Lead Surface Electrocardiogram

Abstract

In the previous study, the atrial flutter mechanism (i.e., Focal or Macroreentrant) was differentiated from the standard 12-lead ECG by the variability of the cycle length of visible successive P-waves (between the R-R waves). This study aims to help researchers reduce imbalances through two different techniques, especially in atrial flutter. Besides, early detection of the AFL mechanism can increase the efficacy of invasive elimination. The proposed model has been extracted several features derived from statistical analysis of the intervals of successive atrial rhythm. Forty-eight patients were undergone endoscopic catheter ablation for the identifications of the AFL mechanism. Two different techniques, SMOTE and Smoothed-Bootstrap, have been used to augment and re-balance the dataset. The synthetic data generated by Smoothed-Bootstrap has been much closer to the original dataset and relatively better than SMOTE technique. The performance has been evaluated by three linear classifiers Linear Discriminant Analysis (LDA), Logistic Regression (LOG), and Support Vector Machine (SVM). The LOG classifier achieved its average performance with accuracy, specificity, sensitivity, 71.08%, 77.13%, and 65.12%, respectively. Smoothed-Bootstrap is a suitable technique in AFL cases to minimize the imbalance issue. The variability in cycle length of consecutive P-waves from the surface ECG has differentiated the Focal AFLfrom Macrorrentrant AFL.