Search results for "Atrial arrhythmias"
showing 5 items of 5 documents
Effect of intravenous flecainide on atrial vulnerability in man.
1983
Sixteen patients were investigated by means of programmed atrial stimulation at two different driving rates: 100 and 120/min. All patients had an increased atrial vulnerability at both driving rates. After intravenous flecainide application (1 mg/kg body weight as a bolus followed by the same amount given by infusion over a period of 20 min) the increased vulnerability was abolished in 11 and 9 patients respectively. In the remaining patients the rate of induced atrial tachyarrhythmia decreased. These findings correlate with a significant prolongation of the effective refractory period of the right atrium and a significant shortening of the relative refractory period of the right atrium. It…
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…
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.