Spontaneous Termination of Atrial Fibrillation
The following paper describes the PhysioNet/Computing in Cardiology Challenge. Please cite this publication when referencing the Challenge.
The following papers were presented at the Computing in Cardiology Conference.
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