Session P86.1

A Wavelet Transform for Atrial Fibrillation Cycle Length Measurements

R Dubois*, P Roussel, M Hocini, F Sacher,
M Haissaguerre, G Dreyfus

ESPCI-ParisTech
Paris, France

Background: Catheter ablation is a common treatment for atrial fibrillation. During the procedure, endocardial electrograms (EGM) are recorded in various locations of the atria and analyzed by the physician in order to decide whether ablation must be performed or not. Traditionally, the mean cycle length is extracted from EGMs: this feature describes the mean duration between two consecutive activations in the tissue. It turned out to be a valuable guideline during the procedure, as it greatly increases after the ablation of a site involved in sustaining the atrial fibrillation (AF), and conversely, does not change after the ablation of an inactive site.
Method: We describe here a new wavelet-based algorithm for the measurement of the Cycle Lengths (CL) in the atria, which provides more information than the usual time average. The proposed algorithm aims at decomposing the EMG signal over a set of functions specifically designed to exhibit cycle lengths. Unlike standard wavelet functions that are local both in time and frequencies, these new functions are local in time and CLs. Therefore the decomposition leads to a CL vs time map, which is a highly informative representation of the electrical activation of the tissue. Subsequently, the information from this map is compressed into a histogram that shows the distribution of the dominant CLs on a given time window. Finally, a sliding window tracks automatically the changes in CLs over a large time scale. The performance of this method was assessed and compared to existing algorithms (Dominant Frequency and autocorrelation) on synthetic EGMs that contain known cycle lengths, and on real data in 6 patients who underwent RF-catheter ablation for AF (18 epochs of AF).
Results and Conclusion: The proposed algorithm extracts the CL efficiently on both synthetic and real data. The correlation with known cycle lengths in the synthetic cases is strong, and the CL distributions on real data are similar to those obtained from manually annotated EGMs. Unlike the usual tools, this method allows the detection of bursts and the recognition of multiple cycle lengths; furthermore, it provides highly informative maps that characterize the signal and may be used for guidance in the ablation procedure.

(Abstract Control Number: 76)