Abstract
This article seeks to differentiate the mechanism of atrial flutter (AFL) between focal and macroreentrant from the surface ECG. It focuses on the hypothesis that the cycle length of visible consecutive P-waves (F-waves) from the surface ECG can differentiate the mechanism (focal or macroreentry) of atrial flutter. Furthermore, early identification of the AFL mechanism from non-invasive techniques can improve the efficacy of invasive ablation. 12-lead surface electrocardiograms of AFL were collected from 48 patients whose mechanism was diagnosed by catheter ablation. Out of 48 recordings 42 were associated with a macroreentrant and remaining 6 were focal mechanism. The proposed model incorporates a wide range of features based on morphological and temporal properties of atrial activity. The wrapper technique has been used for the selection of best feature subsets and its performance is evaluated by using three different classifiers: Linear Discriminant Analysis (LDA), Logistic Regression (LOG), and Support Vector Machine (SVM). An over-sampling technique has been used to balance the dataset at seven different ratios. The best performance, at 5 times of minority (focal) dataset, has been achieved by LOG with maximum accuracy, specificity, and sensitivity of 92.41, 99.26, and 99.23 respectively.
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