Separating atrial near fields and atrial far fields in simulated intra-atrial electrograms

Abstract

The detailed characterization of complex forms of atrial flutter relies on the correct interpretation of intra-atrial electrograms. For this, the near field components, which represent the local electrical activity, are decisive. However, far field components arising from distant electrical sources in the atria can obscure the diagnosis. We developed a method to separate and characterize atrial near field and atrial far field components from bipolar intra-atrial electrograms. First, a set of bipolar electrograms was created by simulating different propagation scenarios representing common clinical depolarization patterns. Second, near and far fields were detected as active segments using a non-linear energy operator-based approach. Third, the maximum slope and the spectral power were extracted as features for all active segments. Active segments were grouped accounting for both the timing and the location of their occurrence. In a last step, the active segments were classified in near and far fields by comparing their feature values to a threshold. All active segments were detected correctly. On average, near fields showed 15.1x larger maximum slopes and 40.4x larger spectral powers above 100 Hz than far fields. For 135 active segments detected in 72 bipolar electrograms, 5.2% and 6.7% were misclassified using the maximum slope and the spectral power, respectively. All active segments were classified correctly if only one near field segment was assumed to occur per electrogram. The separation of atrial near and atrial far fields was successfully developed and applied to in silico electrograms. These investigations provide a promising basis for a future clinical study to ultimately facilitate the precise clinical diagnosis of atrial flutter.

A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms

“…EGM morphology and amplitude are also affected by electrode polarization, excessive contact pressure, catheter motion ( Oesterlein et al, 2016 ), electromagnetic interference ( Unger et al, 2019 ), near field and far field effects ( Schicketanz et al, 2021 ), and poor grounding. However, most in silico experiments do not consider these factors, which might alter the EGM characteristics.…”