Unsupervised k‐mean classification of atrial electrograms from human persistent atrial fibrillation

The dichotomous criterion for atrial electrogram (AEG) classification as proposed by commercial systems (normal/fractionated) to guide ablation has been shown insufficient for persistent atrial fibrillation (persAF) therapy. In this study, we used unsupervised classification to investigate possible sub-groups of persAF AEGs. 3745 bipolar AEGs were collected from 14 persAF patients after pulmonary vein isolation. Automated AEG classification (normal/fractionated) was performed using the CARTO criterion (Biosense Webster). The CARTO attributes (ICL, ACI and SCI) were used to create a 3D space distribution. K-mean with five groups was implemented. Group 1 (43%) represents normal AEGs with low ICL, high ACI and SCI. Groups 2 (9%) and 3 (9%) have shown similar low ICL, but Group 3 has shown AEGs with short activation intervals, as opposed to Group 2. Group 4 (23%) suggests moderated fractionation, with high ACI but low SCI. Group 5 (15%) has shown highly fractionated AEGs with high ICL, low ACI and SCI. The three attributes were significantly different among the five groups (P<0.0001), except ICL between Groups 3 and 4 (P>0.999) and SCI between Groups 3 and 5 (P>0.999). The five sub-groups of AEGs found by the k-mean have shown distinct characteristics, which could provide a more detailed characterization of the atrial substrate during ablation

Time and frequency domain similarities of atrial activations during chronic atrial fibrillation

Time and frequency domain similarities of atrial activations during chronic atrial fibrillatio