Analysis of Fractionated Atrial Fibrillation Electrograms by Wavelet Decomposition

• Published in: IEEE transactions on biomedical engineering 2010-06, Vol.57 (6), p.1388-1398
• Main Authors: Houben, Richard P. M., de Groot, Natasja M. S., Allessie, Maurits A.
• Format: Article
• Language: English
• Subjects: Algorithms; Arrhythmias; Atrial fibrillation; atrial fibrillation (AF); Atrial Fibrillation - diagnosis; Biological and medical sciences; Cardiac arrhythmia; Cardiac disease; Cardiac dysrhythmias; Cardiology. Vascular system; Computerized, statistical medical data processing and models in biomedicine; Decomposition; Diagnosis, Computer-Assisted - methods; Electrocardiography. Vectocardiography; Electrodes; Electrodiagnosis. Electric activity recording; electrophysiology; Fractionation; Gaussian distribution; Heart; Histograms; Humans; Investigative techniques, diagnostic techniques (general aspects); mapping; Medical management aid. Diagnosis aid; Medical sciences; Reproducibility of Results; Rhythm; Sensitivity and Specificity; Signal generators; Signal Processing, Computer-Assisted; Surgery; Wavelet analysis; wavelet transform
• ISSN: 0018-9294; 1558-2531
• DOI: 10.1109/TBME.2009.2037974

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.