Abstract

BackgroundIdentification of recurrent patterns in complex fractionated atrial electrograms (CFAE) has been used to differentiate paroxysmal from persistent atrial fibrillation (AF). Detection of the atrial CFAE patterns might therefore be assistive in guiding radiofrequency catheter ablation to drivers of the arrhythmia. In this study a technique for robust detection and classification of recurrent CFAE patterns is described.MethodCFAE were obtained from the four pulmonary vein ostia, and from the anterior and posterior left atrium, in 10 patients with paroxysmal AF and 10 patients with longstanding persistent AF (216 recordings in total). Sequences 8.4 s in length were analyzed (8,192 sample points, 977 Hz sampling). Among the 216 sequences, two recurrent patterns A and B were substituted for 4 and 5 of the sequences, respectively. To this data, random interference, and random interference + noise were separately added. Basis vectors were constructed using a new transform that is derived from ensemble averaging. Patterns A and B were then detected and classified using a threshold level of Euclidean distance between spectral signatures as constructed with transform coefficients.ResultsIn the presence of interference, sensitivity to detect and distinguish two patterns A and B was 96.2%, while specificity to exclude nonpatterns was 98.0%. In the presence of interference + noise, sensitivity was 89.1% while specificity was 97.0%.ConclusionsTransform coefficients computed from ensemble averages can be used to succinctly quantify synchronized patterns present in AF data. The technique is useful to automatically detect recurrent patterns in CFAE that are embedded in interference without user bias. This quantitation can be implemented in real-time to map the AF substrate prior to and during catheter ablation.

Highlights

  • Identification of recurrent patterns in complex fractionated atrial electrograms (CFAE) has been used to differentiate paroxysmal from persistent atrial fibrillation (AF)

  • Transform coefficients computed from ensemble averages can be used to succinctly quantify synchronized patterns present in AF data

  • The technique is useful to automatically detect recurrent patterns in CFAE that are embedded in interference without user bias

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Summary

Introduction

Identification of recurrent patterns in complex fractionated atrial electrograms (CFAE) has been used to differentiate paroxysmal from persistent atrial fibrillation (AF). Detection of the atrial CFAE patterns might be assistive in guiding radiofrequency catheter ablation to drivers of the arrhythmia. CFAE identified outside the PVs often represent a large surface area of tissue that when ablated in its entirety, can increase procedure time and the possibility of patient morbidity. The presence of morphologic differences and repetitive patterns in CFAE have been observed and quantified as a way to distinguish paroxysmal from longstanding persistent AF patients [ , ]. If such patterns could be distinguished from one recorded sequence to the the substrate could be mapped based on pattern recurrence, which is likely related to degree of arrhythmogenicity [ , ]

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