0000000000954645
AUTHOR
Diego Penela
In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations
Abstract Aims A pre-operative non-invasive identification of the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is important to properly plan radiofrequency ablation procedures. Although some algorithms based on electrocardiograms (ECGs) have been developed to predict left vs. right ventricular origins, their accuracy is still limited, especially in complex anatomies. The aim of this work is to use patient-specific electrophysiological simulations of the heart to predict the SOO in OTVA patients. Methods and results An in silico pace-mapping procedure was designed and used on 11 heart geometries, generating for each case simulated ECGs from 12 clinically plausible SOO…
Quasi-Conformal Technique for Integrating and Validating Myocardial Tissue Characterization in MRI with Ex-Vivo Human Histological Data
Ventricular tachycardia caused by a circuit of re-entry is one of the most critical arrhythmias. It is usually related with heterogeneous scar regions where slow velocity of conduction tissue is mixed with non-conductive tissue, creating pathways (CC) responsible for the tachycardia. Pre-operative DE-MRI can provide information on myocardial tissue viability and then improve therapy planning. However, the current DE-MRI resolution is not sufficient for identifying small CCs and therefore they have to be identified during the intervention, which requires considerable operator experience. In this work, we studied the relationship of histological data (with 10 \(\mu \)m resolution), with in-vi…
Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias
In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-l…