Defining the Predictive Ceiling of Electrogram Features Alone for Predicting Outcomes From Atrial Fibrillation Ablation

The aim of this study is to improve the prediction of long-term outcomes in patients with atrial fibrillation solely using electrogram (EGM) features. We developed three distinct models based on data from a cohort of
$N=561$
patients, each targeting different aspects of EGM analysis: •Principal Component Analysis (PCA): We applied PCA to analyze the variances of eigenvectors projecting more than a fixed threshold of the overall variance (15%). To identify common projection axes among these eigenvectors, we employed the k-means algorithm for clustering. •Auto Regressive: This technique involves applying a bijective transformation to the coefficients, which are subsequently used as input for various machine learning classifiers, including Random Forest or Support Vector Classifier. •Feature Engineering: We performed feature engineering by extracting voltage, rate, and shape similarity metrics from raw EGM (Electrogram) data.