Non-Invasive Localization of Atrial Flutter Circuit Using Recurrence Quantification Analysis and Machine Learning

Abstract

Atrial flutter presents quasi-periodic atrial activity due to circular depolarization. Given the different structure of right and left atria, spatiotemporal variability should be different. This was analyzed using recurrence quantification analysis. Autocorrelation signals were estimated from the unthresholded recurrence plot, calculated with a properly processed ECG to remove variability related to external sources (noise, respiratory motion, T wave overlap). Simple features were considered from the autocorre-lation that attempts to describe the atrial activity in terms of range of recurrence and periodicity. Linear classification using support vector machines and logistic regression both allowed good classification performance (max accuracy 0.8 for both). Feature selection showed that right and left AFL have significantly different cycle lengths (right vs. left: 230.63 ms vs. 206.50 ms, p < 0.01).