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Catheter ablation is effective in acute suppression of ES and often represents a life-saving therapy. In the long term, it prevents recurrences of any VT in about half of the treated patients.

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 rho = 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.

Electrical storm due to focally triggered polymorphic VT/VF may occur either in subacute phase of myocardial infarction or substantially later after index event. Catheter ablation of ectopic beats triggering these arrhythmias can successfully abolish electrical storm and become a life-saving procedure.