A wavelet transform for atrial fibrillation Cycle Length measurements

We describe a new algorithm for the estimation of Cycle Lengths (CL) in the atria. In the spirit of wavelet transforms, the algorithm correlates the electrogram (EGM) signal to a set of functions that are specifically designed to extract the cycle length present in the signal on a given time window. This provides 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 unravels the distribution of the dominant CLs.. Finally, a sliding window tracks automatically the changes in CLs over a larger time scale. Results on both synthetic and real data are presented. 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.