Quantitative assessment of ventricular far field removal techniques for clinical unipolar electrograms

The incidence of atrial tachycardia steadily increases in industrial nations. During invasive electrophysiological studies, a cathetermeasureselectrograms within the atrium to assist detailed diagnosis and treatment planning. With unipolar and bipolar electrograms, two different acquisition modes are clinically available.Unipolar electrograms have several advantages over bipolarelectrograms. However, unipolar electrograms are more affected by noise and the ventricular far field. Therefore, only bipolar electrograms are typicallyused in clinical settings.A recently published ventricular far field removal technique models the ventricular far field by a set of dipoles and yieldedpromising results in a simulation study.However, the method lacks quantitative clinical validation.Therefore, we adapted thetechnique to clinical needsand applied it todatasetsoftwo patientsusing four different lengths of the removal window.Results were compared quantitatively by a tailored residual error measure.The used method resulted in a median reduction of the ventricular far field by approximately89% using a removal window of optimal length forbothpatients.The results showedthatthe dipole methodprovides an alternative to other VFF removal techniques in clinical practice because itcan reveal AA originally hidden by VFF without leading to a prolongation of the electrophysiological study

Basket-Type Catheters : Diagnostic Pitfalls Caused by Deformation and Limited Coverage

Whole-chamber mapping using a 64-pole basket catheter (BC) has become a featured approach for the analysis of excitation patterns during atrial fibrillation. A flexible catheter design avoids perforation but may lead to spline bunching and influence coverage. We aim to quantify the catheter deformation and endocardial coverage in clinical situations and study the effect of catheter size and electrode arrangement using an in silico basket model. Atrial coverage and spline separation were evaluated quantitatively in an ensemble of clinical measurements. A computational model of the BC was implemented including an algorithm to adapt its shape to the atrial anatomy. Two clinically relevant mapping positions in each atrium were assessed in both clinical and simulated data. The simulation environment allowed varying both BC size and electrode arrangement. Results showed that interspline distances of more than 20 mm are common, leading to a coverage of less than 50% of the left atrial (LA) surface. In an ideal in silico scenario with variable catheter designs, a maximum coverage of 65% could be reached. As spline bunching and insufficient coverage can hardly be avoided, this has to be taken into account for interpretation of excitation patterns and development of new panoramic mapping techniques

Improving Clinical ECG-based Atrial Fibrosis Quantification With Neural Networks Through in silico P waves From an Extensive Virtual Patient Cohort

Fibrotic atrial cardiomyopathy is characterized by a replacement of healthy atrial tissue with diffuse patches exhibiting slow electrical conduction properties and altered myocardial tissue structure, which provides a substrate for the maintenance of reentrant activity during atrial fibrillation (AF). Therefore, an early detection of atrial fibrosis could be a valuable risk marker for new-onset AF episodes to select asymptomatic subjects for screening, allowing for timely intervention and optimizing therapy planning. We examined the potential of estimating the fibrotic tissue volume fraction in the atria based on P waves of the 12-lead ECG recorded in sinus rhythm in a quantitative and noninvasive way. Our dataset comprised 68,282 P waves from healthy subjects and 42,227 P waves from AF patients with low voltage areas in the atria, as well as 642,400 simulated P waves of a virtual cohort derived from statistical shape models with different extents of the left atrial myocardium replaced by fibrosis. The root mean squared error for estimating the left atrial fibrotic volume fraction on a clinical test set with a neural network trained on features extracted from simulated and clinical P waves was 16.57 %. Our study shows that the 12-lead ECG contains valuable information on atrial tissue structure. As such it could potentially be employed as an inexpensive and widely available tool to support AF risk stratification in clinical practic

In Silico Study of Local Electrical Impedance Measurements in the Atria - Towards Understanding and Quantifying Dependencies in Human

Background: Electrical impedance measurements have become an accepted tool for monitoring intracardiac radio frequency ablation. Recently, the long-established generator impedance was joined by novel local impedance measurement capabilities with all electrical circuit terminals being accommodated within the catheter. Objective: This work aims at in silico quantification of distinct influencing factors that have remained challenges due to the lack of ground truth knowledge and the superposition of effects in clinical settings. Methods: We introduced a highly detailed in silico model of two local impedance enabled catheters, namely IntellaNav MiFi™ OI and IntellaNav Stablepoint™, embedded in a series of clinically relevant environments. Assigning material and frequency specific conductivities and subsequently calculating the spread of the electrical field with the finite element method yielded in silico local impedances. The in silico model was validated by comparison to in vitro measurements of standardized sodium chloride solutions. We then investigated the effect of the withdrawal of the catheter into the transseptal sheath, catheter-tissue interaction, insertion of the catheter into pulmonary veins, and catheter irrigation. Results: All simulated setups were in line with in vitro experiments and in human measurements and gave detailed insight into determinants of local impedance changes as well as the relation between values measured with two different devices. Conclusion: The in silico environment proved to be capable of resembling clinical scenarios and quantifying local impedance changes. Significance: The tool can assists the interpretation of measurements in humans and has the potential to support future catheter development

Patient-Specific Identification of Atrial Flutter Vulnerability–A Computational Approach to Reveal Latent Reentry Pathways

Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut