Search results for " Arrhythmias"
showing 10 items of 22 documents
Nerves projecting from the intrinsic cardiac ganglia of the pulmonary veins modulate sinoatrial node pacemaker function
2013
Rationale: Autonomic nerves from sinoatrial node (SAN) ganglia are known to regulate SAN function. However, it is unclear whether remote pulmonary vein ganglia (PVG) also modulate SAN pacemaker rhythm. Objective: To investigate whether in the mouse heart PVG modulate SAN function. Methods and Results: In hearts from 45 C57BL and 7 Connexin40+/GFP mice, we used tyrosine-hydroxylase (TH) and choline-acetyltransferase (ChAT) immunofluorescence labeling to characterize adrenergic and cholinergic elements, repectively, within the PVG and SAN. PVG project postganglionic nerves to the SAN. TH and ChAT stained nerves, enter the SAN as an extensive, dense mesh-like neural network. Neurons in PVG are…
Development and Long-Term Follow-Up of an Experimental Model of Myocardial Infarction in Rabbits
2020
Simple Summary Ischemic heart disease is one of the leading causes of death. A series of processes occur during acute myocardial infarction that contribute to the development of ventricular dysfunction, with subsequent heart failure and ventricular arrhythmias, which account for most episodes of sudden cardiac death in these patients. These complications are associated with the adverse cardiac remodeling that occurs during the healing process following an acute episode. The remodeling causes the appearance of a substrate that can trigger life-threatening arrhythmias, such as tachycardia and/or ventricular fibrillation. The development of experimental models for analyzing the basic mechanism…
SYNCOPE AND ARRHYTHMIAS IN PAEDIATRIC AGE
2007
SYNCOPE AND ARRHYTHMIAS IN PAEDIATRIC AGE
Quantification of synchronization during atrial fibrillation by Shannon entropy: Validation in patients and computer model of atrial arrhythmias
2005
Atrial fibrillation (AF), a cardiac arrhythmia classically described as completely desynchronized, is now known to show a certain amount of synchronized electrical activity. In the present work a new method for quantifying the level of synchronization of the electrical activity recorded in pairs of atrial sites during atrial fibrillation is presented. A synchronization index (Sy) was defined by quantifying the degree of complexity of the distribution of the time delays between sites by Shannon entropy estimation. The capability of Sy to discriminate different AF types in patients was assessed on a database of 60 pairs of endocardial recordings from a multipolar basket catheter. The analysis…
Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps
2017
Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly diffe…
Ventricular arrhythmias in children: The uselessness of MRI
2008
Intra-cardiac Signatures of Atrial Arrhythmias Identified by Machine Learning and Traditional Features
2021
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.
Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias
2022
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…