A method for quantifying atrial fibrillation organization based on wave-morphology similarity.
A new method for quantifying the organization of single bipolar electrograms recorded in the human atria during atrial fibrillation (AF) is presented. The algorithm relies on the comparison between pairs of local activation waves (LAWs) to estimate their morphological similarity, and returns a regularity index (rho) which measures the extent of repetitiveness over time of the detected activations. The database consisted of endocardial data from a multipolar basket catheter during AF and intraatrial recordings during atrial flutter. The index showed maximum regularity (rho = 1) for all atrial flutter episodes and decreased significantly when increasing AF complexity as defined by Wells (type I: rho = 0.75 +/- 0.23; type II: rho = 0.35 +/- 0.11; type III: rho = 0.15 +/- 0.08; P < 0.01). The ability to distinguish different AF episodes was assessed by designing a classification scheme based on a minimum distance analysis, obtaining an accuracy of 85.5%. The algorithm was able to discriminate among AF types even in presence of few depolarizations as no significant rho changes were observed by reducing the signal length down to include five LAWs. Finally, the capability to detect transient instances of AF complexity and to map the local regularity over the atrial surface was addressed by the dynamic and multisite evaluation of rho, suggesting that our algorithm could improve the understanding of AF mechanisms and become useful for its clinical treatment.
Faes
Luca
L
Laboratorio Biosegnali, Dipartimento di Fisica, Università di Trento, Trento, Italy. faes@science.unitn.it
Nollo
Giandomenico
G
Antolini
Renzo
R
Gaita
Fiorenzo
F
Ravelli
Flavia
F
D000465:Algorithms; D001281:Atrial Fibrillation / Q000145:classification* / Q000503:physiopathology*; D002036:Bundle of His / Q000503:physiopathology; D004562:Electrocardiography / Q000145:classification / Q000379:methods*; D006339:Heart Rate; D006801:Humans; D008955:Models, Cardiovascular; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted