Machine Learning Approach and Waves Synchronization Improvement for the Localization of Atrial Flutter Circuit Based on the 12-Leads ECG

Abstract

The localization of the Atrial flutter (AFL) is of great interest for ablation planification. Regardless the direction of rotation of the corresponding reentry loop, its left or right atrium origin needs to be known beforehand. This localization is usually performed by using visual inspection of the 12-leads standard ECG that could be computerized. The aim of the study is to automatically classify the corresponding averaged F-waves by using one to five simple features. The averaged F-wave is computed by introducing a new multi-lead extension of a SVD based method for the wave resynchronization.A dataset of ECG recorded from 56 subjects and comprising 25 left AFL and 31 right AFL will train the classifier. From the averaged 12 leads F-wave, 3 groups (Gi) of features were extracted: G1-(min, max), G2-(integral of the negative, of the positive part), G3-(integral of the wave, integral of the absolute value of the wave). The logistic regression (LR) model is used for the supervised classifications.The mean accuracy ranges for the three groups, without validations, are G1:[0.69-0.83], G2:[0.68-0.81], G3:[0.68-0.80] for one feature up to five. The maximum accuracy comes from G1 with five features and is equal to 93%. The corresponding selected features are [max(I), max(III), max(V3), min(aVL), min(V5)].