A FREQUENCY-DOMAIN ANALYSIS OF SPATIAL-ORGANIZATION OF EPICARDIAL MAPS

Mapping of organized rhythms like sinus rhythm uses activation times f rom individual electrograms, and often assumes that the map for a sing le activation is similar to maps for subsequent activations, However, during fibrillation, activation times and electrograms are not easy to define, and maps change from activation to activation, Volume and com plexity of data make analysis of more than a few seconds of fibrillati on difficult. Magnitude Squared Coherence (MSC), a frequency domain me asure of the phase consistency between two signals, can be used to hel p interpret longer data segments without defining activation times or electrograms, Sinus rhythm, flutter, and fibrillation in humans and sw ine were mapped with an array of unipolar electrodes (2.5 mm apart) at 240 sites on the atrial or ventricular epicardium, Four-second data s egments were analyzed, One site near the center of the array was chose n ad hoc as a reference, MSC maps were made by measuring mean MSC from 0-50 Hz between every point in the array relative to the reference, I socoherence contours were drawn, The effects of bias in the coherence estimate due to misalignment were investigated, Average MSC versus dis tance from the reference was measured for all rhythms, Results indicat e that in a 4-s segment of fibrillation, there can exist some phase co nsistency between one site and the reference and little or none betwee n a second site and the reference even when both sites are equidistant from the reference. In fibrillation, isocoherence contours are elonga ted and irregularly shaped, reflecting longterm, but nonuniform, spati al organization, That is, activation during fibrillation cannot be con sidered as random over a 4-s interval, Bias in the coherence estimate due to misalignment is significant for sinus rhythm and flutter, but c an be corrected by manual realignment, Average MSC drops with distance for all rhythms, being most pronounced for fibrillation. MSC maps may provide insights into long-term spatial organization of rhythms that would otherwise be cumbersome and difficult to interpret with standard time domain analysis.