Novel approach to search for individual signal complexes in complex fractionated atrial electrograms using wavelet transform

Abstract

Complex fractionated atrial electrograms (CFAEs) represent the electrophysiologic substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify CFAEs sites is crucial for the development of AF ablation strategies. Individual signal complexes in CFAEs reflect electrical activity of electrophysiologic substrate at given time. We developed a novel algorithm for automated search of individual signal complexes in CFAEs. This algorithm based on wavelet transform enables to describe CFAEs in a novel way and helps to classify CFAEs level of complexity (degree of fractionation). The method was tested using a representative set of 1.5s A-EGMs (n = 113) ranked by an expert into 4 categories: 1 -organized atrial activity; 2 -mild; 3 -intermediate; 4 -high degree of fractionation. Individual signal complexes were marked by an expert in every A-EGM in the dataset. This ranking was used as gold standard for comparison with the novel automatic search method. Following hit rates were achieved by performed automatic search on representative set of data: category 1: 100%, category 2: 98.2%, category 3: 92.06%, category 4: 63.89%. These results indicate that wavelet signal decomposition could carry high level of predictive information about the state of electrophysiologic substrate for AF.