Analyzing the effect of electrode size on electrogram and activation map properties

Overcoming Uncertainties in Electrogram-Based Atrial Fibrillation Mapping: A Review

In clinical rhythmology, intracardiac bipolar electrograms (EGMs) play a critical role in investigating the triggers and substrates inducing and perpetuating atrial fibrillation (AF). However, the interpretation of bipolar EGMs is ambiguous due to several aspects of electrodes, mapping algorithms and wave propagation dynamics, so it requires several variables to describe the effects of these uncertainties on EGM analysis. In this narrative review, we critically evaluate the potential impact of such uncertainties on the design of cardiac mapping tools on AF-related substrate characterization. Literature suggest uncertainties are due to several variables, including the wave propagation vector, the wave's incidence angle, inter-electrode spacing, electrode size and shape, and tissue contact. The preprocessing of the EGM signals and mapping density will impact the electro-anatomical representation and the features extracted from the local electrical activities. The superposition of multiple waves further complicates EGM interpretation. The inclusion of these uncertainties is a nontrivial problem but their consideration will yield a better interpretation of the intra-atrial dynamics in local activation patterns. From a translational perspective, this review provides a concise but complete overview of the critical variables for developing more precise cardiac mapping tools.

A single-beat algorithm to discriminate farfield from nearfield bipolar voltage electrograms from the pulmonary veins

BACKGROUND

Superimposition of farfield (FF) and nearfield (NF) bipolar voltage electrograms (BVE) complicates the confirmation of pulmonary vein (PV) isolation after catheter ablation of atrial fibrillation. Our aim was to develop an automatic algorithm based on a single-beat analysis to discriminate PV NF from atrial FF BVE from a circular mapping catheter during the cryoballoon PV isolation.

METHODS

During freezing cycles in cryoablation PVI, local NF and distant FF signals were recorded, identified and labelled. BVEs were classified using four different machine learning algorithms based on four frequency domain (high-frequency power (PHF), low-frequency power (PLF), relative high power band, PHF ratio of neighbouring electrodes) and two time domain features (amplitude (Vmax), slew rate). The algorithm-based classification was compared to the true identification gained during the PVI and to a classification by cardiac electrophysiologists.

RESULTS

We included 335 BVEs from 57 consecutive patients. Using a single feature, PHF with a cut-off at 150 Hz showed the best overall accuracy for classification (79.4%). By combining PHF with Vmax, overall accuracy was improved to 82.7% with a specificity of 89% and a sensitivity of 77%. The overall accuracy was highest for the right inferior PV (96.6%) and lowest for the left superior PV (76.9%). The algorithm showed comparable accuracy to the classification by the EP specialists.

CONCLUSIONS

An automated farfield-nearfield discrimination based on two simple features from a single-beat BVE is feasible with a high specificity and comparable accuracy to the assessment by experienced cardiac electrophysiologists.

Atrial substrate characterization based on bipolar voltage electrograms acquired with multipolar, focal and mini-electrode catheters

BACKGROUND

Bipolar voltage (BV) electrograms for left atrial (LA) substrate characterization depend on catheter design and electrode configuration.

AIMS

The aim of the study was to investigate the relationship between the BV amplitude (BVA) using four catheters with different electrode design and to identify their specific LA cutoffs for scar and healthy tissue.

METHODS AND RESULTS

Consecutive high-resolution electroanatomic mapping was performed using a multipolar-minielectrode Orion catheter (Orion-map), a duo-decapolar circular mapping catheter (Lasso-map), and an irrigated focal ablation catheter with minielectrodes (Mifi-map). Virtual remapping using the Mifi-map was performed with a 4.5 mm tip-size electrode configuration (Nav-map). BVAs were compared in voxels of 3 × 3 × 3 mm3. The equivalent BVA cutoff for every catheter was calculated for established reference cutoff values of 0.1, 0.2, 0.5, 1.0, and 1.5 mV. We analyzed 25 patients (72% men, age 68 ± 15 years). For scar tissue, a 0.5 mV cutoff using the Nav corresponds to a lower cutoff of 0.35 mV for the Orion and of 0.48 mV for the Lasso. Accordingly, a 0.2 mV cutoff corresponds to a cutoff of 0.09 mV for the Orion and of 0.14 mV for the Lasso. For healthy tissue cutoff at 1.5 mV, a larger BVA cutoff for the small electrodes of the Orion and the Lasso was determined of 1.68 and 2.21 mV, respectively.

CONCLUSION

When measuring LA BVA, significant differences were seen between focal, multielectrode, and minielectrode catheters. Adapted cutoffs for scar and healthy tissue are required for different catheters.

Clinical Relevance of Sinus Rhythm Mapping to Quantify Electropathology Related to Atrial Fibrillation

Progression of AF is accompanied by structural and electrical remodelling, resulting in complex electrical conduction disorders. This is defined as electropathology and it increases with the progression of AF. The severity of electropathology, thus, defines the stage of AF and is a major determinant of effectiveness of AF therapy. As specific features of AF-related electropathology are still unknown, it is essential to first quantify the electrophysiological properties of atrial tissue and then to examine the inter- and intra-individual variation during normal sinus rhythm. Comparison of these parameters between patients with and without a history of AF unravels quantified electrophysiological features that are specific to AF patients. This can help to identify patients at risk for early onset or progression of AF. This review summarises current knowledge on quantified features of atrial electrophysiological properties during sinus rhythm and discusses its relevance in identifying AF-related electropathology.

Computational Analysis of Mapping Catheter Geometry and Contact Quality Effects on Rotor Detection in Atrial Fibrillation

Atrial fibrillation (AF) is the most common cardiac arrhythmia and catheter mapping has been proved to be an effective approach for detecting AF drivers to be targeted by ablation. Among drivers, the so-called rotors have gained the most attention: their identification and spatial location could help to understand which patient-specific mechanisms are acting, and thus to guide the ablation execution. Since rotor detection by multi-electrode catheters may be influenced by several structural parameters including inter-electrode spacing, catheter coverage, and endocardium-catheter distance, in this study we proposed a tool for testing the ability of different catheter shapes to detect rotors in different conditions. An approach based on the solution of the monodomain equations coupled with a modified Courtemanche ionic atrial model, that considers an electrical remodeling, was applied to simulate spiral wave dynamics on a 2D model for 7.75 s. The developed framework allowed the acquisition of unipolar signals at 2 KHz. Two high-density multipolar catheters were simulated (Advisor™ HD Grid and PentaRay^®) and placed in a 2D region in which the simulated spiral wave persists longer. The configuration of the catheters was then modified by changing the number of electrodes, inter-electrodes distance, position, and atrial-wall distance for assessing how they would affect the rotor detection. In contact with the wall and at 1 mm distance from it, all the configurations detected the rotor correctly, irrespective of geometry, coverage, and inter-electrode distance. In the HDGrid-like geometry, the increase of the inter-electrode distance from 3 to 6 mm caused rotor detection failure at 2 mm distance from the LA wall. In the PentaRay-like configuration, regardless of inter-electrode distance, rotor detection failed at 3 mm endocardium-catheter distance. The asymmetry of this catheter resulted in rotation-dependent rotor detection. To conclude, the computational framework we developed is based on realistic catheter shapes designed with parameter configurations which resemble clinical settings. Results showed it is well suited to investigate how mapping catheter geometry and location affect AF driver detection, therefore it is a reliable tool to design and test new mapping catheters.