Automatic Extraction of Recurrent Patterns of High Dominant Frequency Mapping During Human Persistent Atrial Fibrillation

Purpose: Identifying targets for catheter ablation remains challenging in persistent atrial fibrillation (persAF). The dominant frequency (DF) of electrograms during (AF) is believed to primarily reflect local activation. Highest DF (HDF) might be responsible the initiation and perpetuation persAF. However, spatiotemporal behavior not fully understood. Some DFs persAF were shown lack stability, while others exhibit recurrent behavior. We sought develop a tool automatically detect patterns patients. Methods: Non-contact mapping left atrium (LA) was performed 10 patients undergoing HDF ablation. 2,048 virtual (vEGMs, EnSite Array, Abbott Laboratories, USA) collected up 5 min before after Frequency spectrum estimated using fast Fourier transform identified as peak between 4 Hz organization index (OI) calculated. maps per 4-s window an automated pattern recognition algorithm used find recurring spatial patterns. Dominant (DPs) defined with highest recurrence. Results: DPs found all Patients flutter had single DP over recorded time period. interval (median [IQR]) recurrence AF (7 patients) decreased from 21.1 s [11.8 49.7 s] 15.7 [6.5 18.2 s]. inside presented lower temporal standard deviation (0.18 ± 0.06 vs. 0.29 0.12 Hz, p < 0.05) higher OI (0.35 0.03 0.31 0.04, 0.05). regions proportion region septum upper pulmonary vein. Conclusion: Multiple exist proposed method can identify quantify repetition HDFs, where high recurrences may suggest more organized rhythm. consistent compared non-DPs, suggesting that likely recur. Recurring offer comprehensive dynamic insight behavior, targeting such beneficial.