Atrial fibrillation (AFib) is the most common cardiac arrhythmia. Areas in atrial tissue with complex fractionated atrial electrograms (CFAEs) are among others responsible for the maintenance of AFib. Those areas are ideal target sites for ablation to eliminate AFib and restore sinus rhythm. As CFAEs are associated with high fibrillatory frequency, automated identification of CFAEs with spectral analysis helps developing objective strategies for AFib ablation. While the application of current techniques is restricted, this paper introduces a new approach to determine characteristic frequencies during AFib. By using Teagers energy operator we calculate the signal envelope and study its spectrum after Fast Fourier Transformation. Harmonic analysis of distinctive peaks in the power spectrum is carried out to assess characteristic frequencies of a CFAE. While the currently available methods only find one dominant frequency in the spectrum of the signal, our method is capable to find multiple characteristic frequencies, if present. Since it is believed that during AFib the atrium is activated by one or multiple wavelets, our method opens new opportunities for investigation of multiple wavelets propagation.
M. P. Nguyen, C. Schilling, and O. Dössel. A new approach for automated location of active segments in intracardiac electrograms. In IFMBE Proceedings World Congress on Medical Physics and Biomedical Engineering, vol. 25/4, pp. 763-766, 2009
Areas in atrium tissue with complex fractionated atrial electrograms (CFAEs) are among others responsible for the maintenance of atrial fibrillation (AFib). Those areas are ideal target sites for ablation to eliminate AFib and restore normal rhythm. An automated identification of CFAEs with signal processing algorithms is essential to develop an objective strategy for AFib ablation. This paper introduces a new approach to locate signal complexes corresponding to electrophysiological activity. The idea behind this algorithm is based on the idea of Pan-Tompkins QRS-detection algorithm. However in this approach, the extracted signal feature is the signal energy and therefore the algorithm takes into account not only information of the frequency but also of the amplitude. With adaptive thresholding the algorithm is capable to manage changes in the signal dynamics. The results were validated by experts and the algorithm shows a robust performance.
The curative therapy of atrial fibrillation (AF) is still challenging. Although the electrophysiologists know many strategies to cure AF, the underlying mechanisms are still mostly unknown. Also the optimal ablation strategy for paroxysmal and long-lasting persistent AF is not known. Complex fractionated atrial electrograms (CFAEs) are becoming more and more important in the ablation strategies, especially for long-lasting persistant AF. Automated detection and signal analysis of CFAEs is essential in supporting the physicians during the ablation procedure. The robust algorithm to locate CFAEs presented in the contribution by Nguyen, Schilling and Dössel delivers a good bases for postprocessing and signal analysis of CFAEs. It is employing a non-linear energy operator combined with thresholding. In this paper this new algorithm is tested on clinical data and compared to clinically accepted algorithms.