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.