Publications associées à Rythme sinusal

Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

David Atienza Alonso, Miguel Peon Quiros, Simone Machetti, Luca Benini, Benoît Walter Denkinger, Elisabetta De Giovanni, Fabio Montagna

In the last years, remote health monitoring is becoming an essential branch of health care with the rapid development of wearable sensors technology. To meet the demand of new more complex applications and ensuring adequate battery lifetime, wearable senso ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Mapping Biological Current Densities With Ultrafast Acoustoelectric Imaging: Application to the Beating Rat Heart

Pierre-Marc Jean Marie Dansette

Ultrafast acoustoelectric imaging (UAI) is a novel method for the mapping of biological current densities, which may improve the diagnosis and monitoring of cardiac activation diseases such as arrhythmias. This paper evaluates the feasibility of performing ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

Jean-Marc Vesin, Mathieu Lemay, Célestin Leupi, Sibylle Fallet, Etienne Pruvot, Philippe Renevey

This study aims at evaluating the potential of a wrist-type photoplethysmographic (PPG) device to discriminate between atrial fibrillation (AF) and other types of rhythm. Data from 17 patients undergoing catheter ablation of various arrhythmias were proces ...

SPRINGER HEIDELBERG2019

Severe and uniform bi-atrial remodeling measured by dominant frequency analysis in persistent atrial fibrillation unresponsive to ablation

Jean-Marc Vesin, Adrian Luca, Andréa Buttu, Etienne Pruvot

Background High values of ECG and intracardiac dominant frequency (DF) are indicative of significant atrial remodeling in persistent atrial fibrillation (peAF). We hypothesized that patients with peAF unresponsive to ablation display higher ECG and intraca ...

2019

Concealed abnormal atrial phenotype in patients with Brugada syndrome and no history of atrial fibrillation

Jean-Marc Vesin, Adrian Luca, Sasan Yazdani, Lukas Kappenberger

Objectives The electrocardiogram (ECG) of patients with BrS in sinus rhythm might reflect intrinsic atrial electrical abnormalities independent from any previous atrial fibrillation (AF). Aim of this study is to investigate the presence of P-wave abnormali ...

Elsevier2018

A modular low-complexity ECG delineation algorithm for real-time embedded systems

David Atienza Alonso, Francisco Javier Rincon Vallejos, Roman Hermida Correa

This work presents a new modular and lowcomplexity algorithm for the delineation of the different ECG waves (QRS, P and T peaks, onsets and end). Involving a reduced number of operations per second and having a small memory footprint, this algorithm is int ...

2018

Usefulness of P-Wave Duration and Morphologic Variability to Identify Patients Prone to Paroxysmal Atrial Fibrillation

Jean-Marc Vesin, Adrian Luca, Sasan Yazdani, Lukas Kappenberger

Few data are available on the assessment of P-wave beat-to-beat morphology variability and its ability to identify patients prone to paroxysmal atrial fibrillation (AF) occurrence. Aim of this study was to determine whether electrocardiographic (ECG) param ...

Excerpta Medica Inc-Elsevier Science Inc2017

Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology

David Atienza Alonso, Amir Aminifar, Elisabetta De Giovanni, Dionisije Sopic

Atrial Fibrillation (AF) is a type of cardiac arrhythmia that significantly increases the risk of stroke and heart failure. In general, in the case of patients affected by AF, their electrocardiogram (ECG) shows a typical pattern of irregular RR intervals ...

2017

ECG-derived Markers to Identify Patients Prone to Atrial Fibrillation

Jean-Marc Vesin, Adrian Luca, Sasan Yazdani

This study was undertaken to determine the ability of different markers extracted from single lead ECG recorded in sinus rhythm to identify patients prone to atrial fibrillation (AF). For this purpose, 5-minute ECGs recorded in sinus rhythm from two popula ...

2016

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Rythme sinusal

Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble

Mapping Biological Current Densities With Ultrafast Acoustoelectric Imaging: Application to the Beating Rat Heart

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

Severe and uniform bi-atrial remodeling measured by dominant frequency analysis in persistent atrial fibrillation unresponsive to ablation

Concealed abnormal atrial phenotype in patients with Brugada syndrome and no history of atrial fibrillation

A modular low-complexity ECG delineation algorithm for real-time embedded systems

Usefulness of P-Wave Duration and Morphologic Variability to Identify Patients Prone to Paroxysmal Atrial Fibrillation

Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology

ECG-derived Markers to Identify Patients Prone to Atrial Fibrillation

Graph Chatbot

Chattez avec Graph Search

Concealed abnormal atrial phenotype in patients with Brugada syndrome and no history of atrial fibrillation

A modular low-complexity ECG delineation algorithm for real-time embedded systems

Severe and uniform bi-atrial remodeling measured by dominant frequency analysis in persistent atrial fibrillation unresponsive to ablation

Usefulness of P-Wave Duration and Morphologic Variability to Identify Patients Prone to Paroxysmal Atrial Fibrillation

ECG-derived Markers to Identify Patients Prone to Atrial Fibrillation

Hierarchical Cardiac-Rhythm Classification Based on Electrocardiogram Morphology

Modular Design and Optimization of Biomedical Applications for Ultralow Power Heterogeneous Platforms

Can one detect atrial fibrillation using a wrist-type photoplethysmographic device?

Mapping Biological Current Densities With Ultrafast Acoustoelectric Imaging: Application to the Beating Rat Heart

Automatic 12-lead ECG Classification Using a Convolutional Network Ensemble