0000000000393551
AUTHOR
Alejandro Liberos
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.
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…
An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility
Personalized cardiac electrophysiology simulations have demonstrated great potential to study cardiac arrhythmias and help in therapy planning of radio-frequency ablation. Its application to analyze vulnerability to ventricular tachycardia and sudden cardiac death in infarcted patients has been recently explored. However, the detailed multi-scale biophysical simulations used in these studies are very demanding in terms of memory and computational resources, which prevents their clinical translation. In this work, we present a fast phenomenological system based on cellular automata (CA) to simulate personalized cardiac electrophysiology. The system is trained on biophysical simulations to re…
Simplified Electrophysiology Modeling Framework to Assess Ventricular Arrhythmia Risk in Infarcted Patients
Patients that have suffered a myocardial infarction are at lifetime high risk for sudden cardiac death (SCD). Personalized 3D computational modeling and simulation can help to find non-invasively arrhythmogenic features of patients’ infarcts, and to provide additional information for stratification and planning of radiofrequency ablation (RFA). Currently, multiscale biophysical models require high computational resources and long simulations times, which make them impractical for clinical environments. In this paper, we develop a phenomenological solver based on cellular automata to simulate cardiac electrophysiology, with results comparable to those of biophysical models. The solver can ru…