Unsupervised classification of dimension-reduced principal component scores from persistent atrial fibrillation electrograms

We have recently shown the feasibility of unsupervised classification to identify clusters of persistent atrial fibrillation (persAF) atrial electrograms (AEGs) using attributes calculated by electroanatomic mapping systems. In the present work, we sought to extend this analysis to a set of reduced dimension principal component scores (PCSs) estimated from AEG-derived markers. 956 bipolar AEGs were collected from 11 persAF patients. Principal component analysis (PCA) was implemented on 28 markers obtained from different perspectives of the AEGs – such as information theory, spectral analysis, and nonlinear dynamics. The three PCSs with the highest variances were selected and used to perform an unsupervised classification with k-means. The electrophysiological characteristics of the identified groups were further investigated using six AEG markers: sample entropy (SampEn), wave similarity (WS), cycle length (CL), CARTO’s interval confidence level (ICL), peak-to-peak (PP) amplitude and determinism (DET). The three most relevant PCSs explained 81.3% of the variance (56% PCS1, 14.6% PCS2 and 10.7% PCS3). Six AEG groups were identified from the PCSs (F=485, P<0.0001). Group 1 (15%) had highest SampEn, group 4 (16%) highest ICL, and group 5 (13%) highest WS. Markers suggest mild fractionation on group 2 (30%). Group 3 (14%) are AEGs with low ICL and lowest PP. Finally, group 6 (12%) clustered AEGs with highest PP and DET, and with lowest SampEn. The six clusters of AEGs found in this work are in line with recent findings, highlighting the complex pathophysiology of persAF. These could provide a more complete characterization of persAF substrate during ablation target identification in future clinical studies.