Ravikumar, Vasanth

Atrial Fibrillation (AF) is the most common cardiac arrhythmia. Signal-processing approaches are widely used for the analysis of intracardiac electrograms (iEGMs), which are collected during catheter ablation from patients with AF. In order to identify possible targets for ablation therapy, dominant frequency (DF) is widely used and incorporated in electroanatomical mapping systems. Recently, a more robust measure, multiscale frequency (MSF), for iEGM data analysis was adopted and validated. However, before completing any iEGM analysis, a suitable bandpass (BP) filter must be applied to remove noise. Currently, no clear guidelines for BP filter characteristics exist. The lower bound of the BP filter is usually set to 3–5 Hz, while the upper bound (BP¯th) of the BP filter varies from 15 Hz to 50 Hz according to many researchers. This large range of BP¯th subsequently affects the efficiency of further analysis. In this paper, we aimed to develop a data-driven preprocessing framework for iEGM analysis, and validate it based on DF and MSF techniques. To achieve this goal, we optimized the BP¯th using a data-driven approach (DBSCAN clustering) and demonstrated the effects of different BP¯th on subsequent DF and MSF analysis of clinically recorded iEGMs from patients with AF. Our results demonstrated that our preprocessing framework with BP¯th = 15 Hz has the best performance in terms of the highest Dunn index. We further demonstrated that the removal of noisy and contact-loss leads is necessary for performing correct data iEGMs data analysis.

AbstractBackgroundCatheter ablation is associated with limited success rates in patients with persistent atrial fibrillation (AF). Currently, existing mapping systems fail to identify critical target sites for ablation. Recently, we proposed and validated several techniques (multiscale frequency [MSF], Shannon entropy [SE], kurtosis [Kt], and multiscale entropy [MSE]) to identify pivot point of rotors using ex‐vivo optical mapping animal experiments. However, the performance of these techniques is unclear for the clinically recorded intracardiac electrograms (EGMs), due to the different nature of the signals.ObjectiveThis study aims to evaluate the performance of MSF, MSE, SE, and Kt techniques to identify the pivot point of the rotor using unipolar and bipolar EGMs obtained from numerical simulations.MethodsStationary and meandering rotors were simulated in a 2D human atria. The performances of new approaches were quantified by comparing the “true” core of the rotor with the core identified by the techniques. Also, the performances of all techniques were evaluated in the presence of noise, scar, and for the case of the multielectrode multispline and grid catheters.ResultsOur results demonstrate that all the approaches are able to accurately identify the pivot point of both stationary and meandering rotors from both unipolar and bipolar EGMs. The presence of noise and scar tissue did not significantly affect the performance of the techniques. Finally, the core of the rotors was correctly identified for the case of multielectrode multispline and grid catheter simulations.ConclusionThe core of rotors can be successfully identified from EGMs using novel techniques; thus, providing motivation for future clinical implementations.