Wavelet-based electrogram onset identification for ventricular electroanatomical mapping

Identification of the earliest activation area is a common task in focal tachycardia catheter ablation treatments. In these procedures the detection of the electrogram (EGM) activation onset during electroanatomical mapping (EAM) helps to define the ablation target area. However, EAM systems do not automatically detect the EGM activation onset and this is currently done manually in clinical routine, thus highly dependent on observer experience and very time consuming. In this work we propose a method that combines surface electrocardiogram (ECG) information with EGM signals in order to determine each activation onset for helping the determination of the ablation target area. The algorithm detects those instants from the wavelet decomposition of the EGM signal envelope using the QRS complex width as a reference search window. The automatically detected activation onsets were compared with those made manually during the intervention by an expert technician, obtaining a difference of 4.0 ± 13.7 ms evaluated in 10 patients suffering from ventricular extrasystole beats, in a total of 2163 EGM mapping points.