TY - GEN
T1 - Analysis of RR interval and fibrillation frequency and amplitude for predicting spontaneous termination of atrial fibrillation
AU - Langley, P.
AU - Allen, John
AU - Bowers, E.J.
AU - Drinnan, M.J.
AU - Garcia, E.V.
AU - King, S. T.
AU - Olbrich, T.
AU - Sims, A.J.
AU - Smith, F.E.
AU - Wild, J.
AU - Zheng, D.
AU - Murray, A.
PY - 2004
Y1 - 2004
N2 - We assessed characteristics of atrial and ventricular activity from the ECG for predicting the oflset of atrial fibrillation for the 2004 PhysioNet/Computers in Cardiology Challenge. Seven parameters were analysed with five based on the statistical characteristics of the RR interval (mean, standard deviation, skewness, kurtosis and median beat-to-beat change), and fibrillation frequency and atrial signal amplitude. The power of the parameters to predict termination of the arrhythmia was assessed individually and in combination using linear discriminant analysis ( D A ) and a?t$cial neural network (ANN) techniques. Fibrillation fiequency with a threshold value of 5.55 Hz was able to idenrify 10/10 learning set records which terminated immediately (T) and 8/10 of non-terminating records (N) and was the best of the individual parameters. Classifcations for the test set for event 1 of the challenge for algorithms based on fibrillation frequency alone, LDA and ANN received scores of 24/30, 18/30 and 23/30 respectively. Low jibrillation frequency is an indicator of spontaneous termination of atrial fibrillation.
AB - We assessed characteristics of atrial and ventricular activity from the ECG for predicting the oflset of atrial fibrillation for the 2004 PhysioNet/Computers in Cardiology Challenge. Seven parameters were analysed with five based on the statistical characteristics of the RR interval (mean, standard deviation, skewness, kurtosis and median beat-to-beat change), and fibrillation frequency and atrial signal amplitude. The power of the parameters to predict termination of the arrhythmia was assessed individually and in combination using linear discriminant analysis ( D A ) and a?t$cial neural network (ANN) techniques. Fibrillation fiequency with a threshold value of 5.55 Hz was able to idenrify 10/10 learning set records which terminated immediately (T) and 8/10 of non-terminating records (N) and was the best of the individual parameters. Classifcations for the test set for event 1 of the challenge for algorithms based on fibrillation frequency alone, LDA and ANN received scores of 24/30, 18/30 and 23/30 respectively. Low jibrillation frequency is an indicator of spontaneous termination of atrial fibrillation.
UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-28244476908&partnerID=MN8TOARS
U2 - 10.1109/CIC.2004.1443019
DO - 10.1109/CIC.2004.1443019
M3 - Conference proceeding
SN - 0-7803-8927-1
VL - 31
T3 - Computers in Cardiology
SP - 637
EP - 640
BT - Computers in Cardiology
PB - IEEE
T2 - Computers in Cardiology Conference
Y2 - 19 September 2004 through 22 September 2004
ER -