Search results for " Fibrillation"

Registration and fusion of segmented left atrium CT images with CARTO electrical maps for the ablative treatment of atrial fibrillation

2005

This study aims to extract the interior surface of the left atrium (LA) and pulmonary veins (PVs) from threedimensional tomographic data and to integrate it with LA CARTO electrical maps. The separation of LA and PVs from other overlapping structures of the heart was performed processing 3D CT data by marker-controlled watershed segmentation and surface extraction. CARTO maps were then registered on the L A internal surface by a stochastic optimization algorithm based on simulated annealing. The residual registration error resulted inferior to 3 mm. The integration between electrophysiological and high resolved anatomic information of LA results feasible and may constitute a significant sup…

Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset

2021

[EN] In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinic…

Cerebrovascular risk factors and clinical classification of strokes

2005

Cerebrovascular risk represents a progressive and evolving concept owing to the particular distribution of risk factors in patients with ischemic stroke and in light of the newest stroke subtype classifications that account for pathophysiological, instrumental, and clinical criteria. Age represents the strongest nonmodifiable risk factor associated with ischemic stroke, while hypertension constitutes the most important modifiable cerebrovascular risk factor, confirmed by a host of epidemiological data and by more recent intervention trials of primary (HOT, Syst-Eur, LIFE) and secondary (PROGRESS) prevention of stroke in hypertensive patients. To be sure, a curious relationship exists betwee…

2015

Background and purpose Silent atrial fibrillation (AF) and tachycardia (AT) are considered precursors of ischaemic stroke. Therefore, detection of paroxysmal atrial rhythm disorders is highly relevant, but is clinically challenging. We aimed to evaluate the diagnostic value of natriuretic peptide levels in the detection of paroxysmal AT/AF in a pilot study. Methods Natriuretic peptide levels were analysed in two independent patient cohorts (162 patients with arterial hypertension or other cardiovascular risk factors and 82 patients with retinal vessel disease). N-terminal-pro-brain natriuretic peptide (NT-proBNP) and BNP were measured before the start of a 7-day Holter monitoring period car…

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 Fibrillation and Tachycardia detection from surface ECG using time-frequency representation images as input dataset for machine learning

2017

Parameter-less ventricular fibrillation detection with time-frequency representation.Time-frequency representations are treated as images for a classifier.A comparison for four classifiers demonstrates the validity of the proposed method.The proposed technique could be applied to any signal and research field.This is a novel approach to signal analysis. Background and objectiveTo safely select the proper therapy for Ventricullar Fibrillation (VF) is essential to distinct it correctly from Ventricular Tachycardia (VT) and other rhythms. Provided that the required therapy would not be the same, an erroneous detection might lead to serious injuries to the patient or even cause Ventricular Fibr…

The supraventricular tachycardias: Proposal of a diagnostic algorithm for the narrow complex tachycardias

2013

AbstractThe narrow complex tachycardias (NCTs) are defined by the presence in a 12-lead electrocardiogram (ECG) of a QRS complex duration less than 120ms and a heart rate greater than 100 beats per minute; those are typically of supraventricular origin, although rarely narrow complex ventricular tachycardias have been reported in the literature.As some studies document, to diagnose correctly the NCTs is an arduous exercise because sometimes those have similar presentation on the ECG. In this paper, we have reviewed the physiopathological, clinical, and ECG findings of all known supraventricular tachycardias and, in order to reduce the possible diagnostic errors on the ECG, we have proposed …

Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class

2021

Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the mo…

Identification of atrial fibrillation drivers by means of concentric ring electrodes

2022

The prevalence of atrial fibrillation (AF) has tripled in the last 50 years due to population aging. High-frequency (DFdriver) activated atrial regions lead the activation of the rest of the atria, disrupting the propagation wavefront. Fourier based spectral analysis of body surface potential maps have been proposed for DFdriver identification, although these approaches present serious drawbacks due to their limited spectral resolution for short AF epochs and the blurring effect of the volume conductor. Laplacian signals (BC-ECG) from bipolar concentric ring electrodes (CRE) have been shown to outperform the spatial resolution achieved with conventional unipolar recordings. Our aimed was to…

An IoT and Fog Computing-Based Monitoring System for Cardiovascular Patients with Automatic ECG Classification Using Deep Neural Networks

2020

Telemedicine and all types of monitoring systems have proven to be a useful and low-cost tool with a high level of applicability in cardiology. The objective of this work is to present an IoT-based monitoring system for cardiovascular patients. The system sends the ECG signal to a Fog layer service by using the LoRa communication protocol. Also, it includes an AI algorithm based on deep learning for the detection of Atrial Fibrillation and other heart rhythms. The automatic detection of arrhythmias can be complementary to the diagnosis made by the physician, achieving a better clinical vision that improves therapeutic decision making. The performance of the proposed system is evaluated on a…