A frequency domain analysis of spatial organization of epicardial maps

Mapping of organized rhythms like sinus rhythm uses activation times from individual electrograms, and often assumes that the map for a single 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 complexity of data make analysis of more than a few seconds of fibrillation difficult. Magnitude squared coherence (MSC), a frequency domain measure of the phase consistency between two signals, can be used to help interpret longer data segments without defining activation times or electrograms. Sinus rhythm, flutter, and fibrillation in humans and swine were mapped with an array of unipolar electrodes (2.5 mm apart) at 240 sites on the atrial or ventricular epicardium. Four-second data segments were analyzed. One site near the center of the array was chosen 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. Isocoherence contours were drawn. The effects of bias in the coherence estimate due to misalignment were investigated. Average MSC versus distance from the reference was measured for all rhythms. Results indicate that in a 4-s segment of fibrillation, there can exist some phase consistency between one site and the reference and little or none between a second site and the reference even when both sites are equidistant from the reference. In fibrillation, isocoherence contours are elongated and irregularly shaped, reflecting long-term, but nonuniform, spatial organization. That is, activation during fibrillation cannot be considered as random over a 4-s interval, Bias in the coherence estimate due to misalignment is significant for sinus rhythm and flutter, but can 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.<
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