A method for quantifying atrial fibrillation organization based on wave-morphology similarity

Abstract

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.

Applications of Signal Analysis to Atrial Fibrillation

“…More precise and successful therapies can be developed through this analysis, like guided radio-frequency ablation [26], analysis of antiarrhythmic drug effects [27] or performance improvement of atrial implantable cardioverter-defibrillators [28]. Within this context, ABS (or a similar methodology) has been applied to the AEG in order to discriminate sinus rhythm from AF [15], to measure AF organization [9] and synchronization [29] and to monitor the effects of ablation procedures and antiarrhythmic drugs [30].…”

“…This relevant concept will be addressed in Section § 4, where the most important methods will be described [9,10]. AF organization has demonstrated its clinical usefulness because indices of organization have been related to the electrophysiological mechanisms sustaining AF, or may be useful in the evaluation of strategies for AF treatment, such as catheter ablation or electrical cardioversion [11].…”

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation

“…In order to analyse VF regularity and organization, two parameters were calculated: Regularity Index (RI) and Number of Occurrences (NO). The algorithm used for the RI computation [4] is a modification of the original [17], in order to adapt it to the electrophysiological characteristics of the used cardiac model. More precisely, the local activation wave duration was increased up to 50 ms.…”

Ventricular activity cancellation in electrograms during atrial fibrillation with constraints on residuals’ power

“…The parameter weights the innovation at time n with respect to the template at the time n − 1. Setting = 0.1, as employed, is substantially equivalent to taking the average of 20 nearby QRSs complexes, a number found sufficient to reduce the effects of uncorrelated noise [11,15]. Coherently, the first template t(0) is obtained as the average, after alignment, of the first twenty QRSs.…”

“…Most studies [3,11,12] commonly performed this operation by means of template matching and subtraction (TMS). Briefly, a running template is obtained by adaptive averaging AEG segments, taken in correspondence of QRS complexes on a concurrent surface ECG recording [13].…”