0000000000345162
AUTHOR
Sanjiv M. Narayan
Machine Learning–Enabled Multimodal Fusion of Intra-Atrial and Body Surface Signals in Prediction of Atrial Fibrillation Ablation Outcomes
Background: Machine learning is a promising approach to personalize atrial fibrillation management strategies for patients after catheter ablation. Prior atrial fibrillation ablation outcome prediction studies applied classical machine learning methods to hand-crafted clinical scores, and none have leveraged intracardiac electrograms or 12-lead surface electrocardiograms for outcome prediction. We hypothesized that (1) machine learning models trained on electrograms or electrocardiogram (ECG) signals can perform better at predicting patient outcomes after atrial fibrillation ablation than existing clinical scores and (2) multimodal fusion of electrogram, ECG, and clinical features can furt…
Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features
Intracardiac devices separate atrial arrhythmias (AA) from sinus rhythm (SR) using electrogram (EGM) features such as rate, that are imperfect. We hypothesized that machine learning could improve this classification.
Atrial fibrillation signatures on intracardiac electrograms identified by deep learning
BACKGROUND: Automatic detection of atrial fibrillation (AF) by cardiac devices is increasingly common yet sub-optimally groups AF, flutter or tachycardia (AT) together as ‘high rate events’. This may delay or misdirect therapy. OBJECTIVE: We hypothesized that deep learning (DL) can accurately classify AF from AT by revealing electrogram (EGM) signatures. METHODS: We studied 86 patients in whom the diagnosis of AF or AT was established at electrophysiological study (25 female, 65 ± 11 years). Custom DL architectures were trained to identify AF using N = 29,340 unipolar and N = 23,760 bipolar EGM segments. We compared DL to traditional classifiers based on rate or regularity. We explained DL …
Non-invasive Spatial Mapping of Frequencies in Atrial Fibrillation: Correlation With Contact Mapping
[EN] Introduction: Regional differences in activation rates may contribute to the electrical substrates that maintain atrial fibrillation (AF), and estimating them non-invasively may help guide ablation or select anti-arrhythmic medications. We tested whether non-invasive assessment of regional AF rate accurately represents intracardiac recordings. Methods: In 47 patients with AF (27 persistent, age 63 +/- 13 years) we performed 57-lead non-invasive Electrocardiographic Imaging (ECGI) in AF, simultaneously with 64-pole intracardiac signals of both atria. ECGI was reconstructed by Tikhonov regularization. We constructed personalized 3D AF rate distribution maps by Dominant Frequency (DF) ana…
Three dimensional reconstruction to visualize atrial fibrillation activation patterns on curved atrial geometry
BackgroundThe rotational activation created by spiral waves may be a mechanism for atrial fibrillation (AF), yet it is unclear how activation patterns obtained from endocardial baskets are influenced by the 3D geometric curvature of the atrium or ‘unfolding’ into 2D maps. We develop algorithms that can visualize spiral waves and their tip locations on curved atrial geometries. We use these algorithms to quantify differences in AF maps and spiral tip locations between 3D basket reconstructions, projection onto 3D anatomical shells and unfolded 2D surfaces.MethodsWe tested our algorithms in N = 20 patients in whom AF was recorded from 64-pole baskets (Abbott, CA). Phase maps were generated by…
Re-interpreting complex atrial tachycardia maps using global atrial vectors.
Activation maps of scar-related atrial tachycardias (AT) can be challenging to interpret due to difficulty in inaccurate annotation of electrograms, and an arbitrarily predefined mapping window. A novel mapping software integrating vector data and applying an algorithmic solution taking into consideration global activation pattern has been recently described (Coherent™, Biosense Webster "Investigational").We aimed to assess the investigational algorithm to determine the mechanism of AT compared with the standard algorithm.This study included patients who underwent ablation of scar-related AT using the Carto 3 and the standard activation algorithm. The mapping data were analyzed retrospectiv…