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• W2031199863 abstract "Complex fractionated atrial electrograms (CFAEs) may represent the electrophysiological substrate for atrial fibrillation (AF). Progress in signal processing algorithms to identify sites of CFAEs is crucial for the development of AF ablation strategies. A novel algorithm for automated description of fractionation of atrial electrograms (A-EGMs) based on the wavelet transform has been proposed. The algorithm was developed and validated using a representative set of 1.5 s A-EGM (n = 113) ranked by three experts into four categories: 1—organized atrial activity; 2—mild; 3—intermediate; 4—high degree of fractionation. A tight relationship between a fractionation index and expert classification of A-EGMs (Spearman correlation ρ = 0.87) was documented with a sensitivity of 82% and specificity of 90% for the identification of highly fractionated A-EGMs. This operator-independent description of A-EGM complexity may be easily incorporated into mapping systems to facilitate CFAE identification and to guide AF substrate ablation." @default.
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• W2031199863 date "2008-10-22" @default.
• W2031199863 modified "2023-10-17" @default.
• W2031199863 title "A new approach to automated assessment of fractionation of endocardial electrograms during atrial fibrillation" @default.
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• W2031199863 doi "https://doi.org/10.1088/0967-3334/29/12/002" @default.
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