DOI: 10.1161/circ.148.suppl_1.17924 ISSN: 0009-7322

Abstract 17924: Machine Learning Analysis of the Atrial ECG in Patients With Persistent Atrial Fibrillation Predicts Long-Term Outcomes

Jorge Bohorquez, Ghaith Zaatari, Patrick Ganzer, Odelia Schwartz, Abhishek Prasad, Raul Mitrani, Jeffrey Goldberger
  • Physiology (medical)
  • Cardiology and Cardiovascular Medicine

Background: The surface Atrial ECG (A-ECG) time and frequency domains features are not currently used clinically in patients with persistent atrial fibrillation (PeAF). Machine Learning (ML) can provide novel insight into various data sources.

Research question: Can ML analysis of the A-ECG predict the outcome of catheter ablation (CA) of PeAF?

Aim: To use ML Tools to assess A-ECG prediction of CA outcomes in PeAF patients at 1 and 2 years.

Methods: Thirty-two patients (age 63±9 years; 71% males) with PeAF underwent two 5-minute epochs of surface ECG; 4 were excluded for significant artifacts and 2 more for unrelated clinical issues. The Pre-CA A-ECG was derived by QRST subtraction and independent component analysis filtering and divided into 50% overlap 10-second windows for spectral analysis. A-ECG mean dominant frequency DF, mean organization index and median amplitude M-AMP were computed for each lead. Freedom from atrial arrhythmia AA (Responders) was assessed at one and two years. The two 5-minute segments per patient were included in the data set. Classification Learner (Mathworks Inc., Natick, MA, USA) was used to train classifiers, looking for the minimum number of features able to explain the outcomes. Once the best (2-3) features were identified, a MATLAB program was used to visualize the decision regions and correlate with ablation outcomes.

Results: ML identified A-ECG features DF and M-AMP of V 1 to well explain outcome. Specifically, an Artificial Neural Network (ANN) with 1 fully connected hidden layer of size 7 can explain the data with 90.4% accuracy. The figure shows the datasets for year 1 and year 2 CA outcomes, superimposed with ANN decision regions, for Responders and non-Responders. High DF and high M-AMP are predictors of responders and Low DF low M-AMP for non-Responders.

Conclusion: ML analysis of the A-ECG is a promising tool to predict Responders to PVI CA for treatment of PeAF. This predictive algorithm needs to be tested on a prospective cohort.

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