Measures of spatiotemporal organization differentiate persistent from long-standing atrial fibrillation.
OBJECTIVE
This study presents an automatic diagnostic method for the discrimination between persistent and long-standing atrial fibrillation (AF) based on the surface electrocardiogram (ECG).


RESULTS
Standard 12-lead ECG recordings were acquired in 53 patients with either persistent (N= 20) or long-standing AF (N= 33), the latter including both long-standing persistent and permanent AF. A combined frequency analysis of multiple ECG leads followed by the computation of standard complexity measures provided a method for the quantification of spatiotemporal AF organization. All possible pairs of precordial ECG leads were analysed by this method and resulting organization measures were used for automatic classification of persistent and long-standing AF signals. Correct classification rates of 84.9% were obtained, with a predictive value for long-standing AF of 93.1%. Spatiotemporal organization as measured in lateral precordial leads V5 and V6 was shown to be significantly lower during long-standing AF than persistent AF, suggesting that time-related alterations in left atrial electrical activity can be detected in the ECG.


CONCLUSIONS
Accurate discrimination between persistent and long-standing AF based on standard surface recordings was demonstrated. This information could contribute to optimize the management of sustained AF, permitting appropriate therapeutic decisions and thereby providing substantial clinical cost savings.
Applied Signal Processing Group, Swiss Federal Institute of Technology, EPFL STI GR-SCI-STI SCI-STI-JMV, ELD 234-Bâtiment ELD, CH-1015 Lausanne, Switzerland. laurent.uldry@epfl.ch
Laurent
L
Uldry
Jérôme
J
Van Zaen
Yann
Y
Prudat
Lukas
L
Kappenberger
Jean-Marc
JM
Vesin
D001281:Atrial Fibrillation / Q000175:diagnosis* / Q000503:physiopathology; D004562:Electrocardiography / Q000379:methods*; D005260:Female; D006325:Heart Atria / Q000503:physiopathology*; D006801:Humans; D008297:Male; D011237:Predictive Value of Tests; D062211:Spatio-Temporal Analysis