Wavelet decomposition of unipolar fibrillation electrograms successfully detected primary potentials and quantified fractionation, showing good resemblance to laborious manual editing.
Observational (n=46)
Does wavelet decomposition of unipolar fibrillation electrograms accurately detect local activation times and quantify fractionation in patients with atrial fibrillation?
Wavelet transformation of unipolar fibrillation electrograms provides an objective and automated method to detect primary potentials and quantify fractionation, potentially enabling real-time mapping of complex atrial fibrillation.
This study introduces the use of wavelet decomposition of unipolar fibrillation electrograms for the automatic detection of local activation times during complex atrial fibrillation (AF). The purpose of this study was to evaluate this technique in patients with structural heart disease and longstanding persistent AF. In 46 patients undergoing cardiac surgery, unipolar fibrillation electrograms were recorded from the right atrium, using a mapping array of 244 electrodes. In 25 patients with normal sinus rhythm, AF was induced by rapid pacing, whereas 21 patients were in persistent AF. In patients with longstanding AF, the atrial electrograms showed a high degree of fractionation. In each patient, 12 s of AF were analyzed by wavelet transformation (15 scales). The finest scales (1-7) were used to reconstruct a "local" fibrillation electrogram, whereas with the coarse scales (9-15), a far-field signal was generated. With these local and far-field electrograms, the "primary" fibrillation potentials, due to wave propagation underneath the electrode, could be distinguished from double potentials and multiple components generated by remote wavefronts. Wavelet transformation resulted in AF histograms with a closely gaussian distribution and the automatically generated activation maps showed a good resemblance with fibrillation maps obtained by laborious manual editing. A special chaining algorithm was developed to detect multiple components in fractionated electrograms. The degree of fractionation showed a positive correlation with the complexity of fibrillation, thus providing an objective quantification of the degree of electrical dissociation of the atria. Wavelet transformation can be a useful technique to detect the primary potentials and quantify the degree of fractionation of fibrillation electrograms. This could enable real-time mapping of complex cases of human AF and classification of the underlying electropathological substrate.
Houben et al. (Fri,) conducted a observational in Atrial fibrillation (n=46). Wavelet decomposition of unipolar fibrillation electrograms vs. Manual editing was evaluated on Detection of local activation times and quantification of fractionation. Wavelet decomposition of unipolar fibrillation electrograms successfully detected primary potentials and quantified fractionation, showing good resemblance to laborious manual editing.
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