from PhysioNet, the research resource for complex physiologic signals


Spontaneous Termination of Atrial Fibrillation: Papers about the Challenge

These papers were presented at Computers in Cardiology 2004. Please cite this publication when referencing any of these papers. Links below are to copies of these papers on the CinC web site.

Spontaneous Termination of Atrial Fibrillation: A Challenge from PhysioNet and Computers in Cardiology 2004
GB Moody

Analysis of the Surface Electrocardiogram to Predict Termination of Atrial Fibrillation: The 2004 Computers in Cardiology/PhysioNet Challenge
S Petrutiu, AV Sahakian, J Ng, S Swiryn

Prediction of Spontaneous Termination of Atrial Fibrillation Using Time-Frequency Analysis of the Atrial Fibrillatory Wave
C Mora, J Castells, R Ruiz, JJ Rieta, J Millet, C Sánchez, S Morell

Prediction of Spontaneous Termination of Atrial Fibrillation in Surface ECG by Frequency Analysis
Q Xi, S Shkurovich

Automated Prediction of Spontaneous Termination of Atrial Fibrillation from Electrocardiograms
D Hayn, K Edegger, D Scherr, P Lercher, B Rotman, W Klein, G Schreier

Predicting the End of an Atrial Fibrillation Episode: The PhysioNet Challenge
F Cantini, F Conforti, M Varanini, F Chiarugi, G Vrouchos

Detection of Spontaneous Termination of Atrial Fibrillation
B Logan, J Healey

Predicting Spontaneous Termination of Atrial Fibrillation with Time-Frequency Information
F Nilsson, M Stridh, A Bollmann, L Sörnmo

Electrocardiogram Signal Classification Based on Fractal Features
AN Esgiar, PK Chakravorty

On Predicting the Spontaneous Termination of Atrial Fibrillation Episodes Using Linear and Non-Linear Parameters of ECG Signal and RR Series
LT Mainardi, M Matteucci, R Sassi

Computers in Cardiology/Physionet Challenge 2004: AF Classification Based on Clinical Features
M Lemay, Z Ihara, JM Vesin, L Kappenberger

A Statistical Feature Based Approach to Predicting Termination of Atrial Fibrillation
FM Roberts, RJ Povinelli