The Use of Electrocardiographic Imaging in Localising the Origin of Arrhythmias During Catheter Ablation of Ventricular Tachycardia

Non-invasive electrocardiographic imaging (ECGI) is a novel clinical tool for mapping ventricular arrhythmia. Using multiple body surface electrodes to collect unipolar electrograms and conventional medical imaging of the heart, an epicardial shell can be created to display calculated electrograms. This calculation is achieved by solving the inverse problem and allows activation times to be calculated from a single beat. The technology was initially pioneered in the US using an experimental torso-shaped tank. Accuracy from studies in humans has varied. Early data was promising, with more recent work suggesting only moderate accuracy when reproducing cardiac activation. Despite these limitations, the system has been successfully used in pioneering work with non-invasive cardiac radioablation to treat ventricular arrhythmia. This suggests that the resolution may be sufficient for treatment of large target areas. Although untested in a well conducted clinical study it is likely that it would not be accurate enough to guide more discreet radiofrequency ablation.

Ex vivo Validation of Noninvasive Epicardial and Endocardial Repolarization Mapping

Background: The detection and localization of electrophysiological substrates currently involve invasive cardiac mapping. Electrocardiographic imaging (ECGI) using the equivalent dipole layer (EDL) method allows the noninvasive estimation of endocardial and epicardial activation and repolarization times (AT and RT), but the RT validation is limited to in silico studies. We aimed to assess the temporal and spatial accuracy of the EDL method in reconstructing the RTs from the surface ECG under physiological circumstances and situations with artificially induced increased repolarization heterogeneity.Methods: In four Langendorff-perfused pig hearts, we simultaneously recorded unipolar electrograms from plunge needles and pseudo-ECGs from a volume-conducting container equipped with 61 electrodes. The RTs were computed from the ECGs during atrial and ventricular pacing and compared with those measured from the local unipolar electrograms. Regional RT prolongation (cooling) or shortening (pinacidil) was achieved by selective perfusion of the left anterior descending artery (LAD) region.Results: The differences between the computed and measured RTs were 19.0 ± 17.8 and 18.6 ± 13.7 ms for atrial and ventricular paced beats, respectively. The region of artificially delayed or shortened repolarization was correctly identified, with minimum/maximum RT roughly in the center of the region in three hearts. In one heart, the reconstructed region was shifted by ~2.5 cm. The total absolute difference between the measured and calculated RTs for all analyzed patterns in selectively perfused hearts (n = 5) was 39.6 ± 27.1 ms.Conclusion: The noninvasive ECG repolarization imaging using the EDL method of atrial and ventricular paced beats allows adequate quantitative reconstruction of regions of altered repolarization.

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 catheter measures electrograms 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 bipolar electrograms. However, unipolar electrograms are more affected by noise and the ventricular far field. Therefore, only bipolar electrograms are typically used in clinical settings. A recently published ventricular far field removal technique models the ventricular far field by a set of dipoles and yielded promising results in a simulation study. However, the method lacks quantitative clinical validation. Therefore, we adapted the technique to clinical needs and applied it to data sets of two patients using 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 approximately 89% using a removal window of optimal length for both patients. The results showed that the dipole method provides an alternative to other VFF removal techniques in clinical practice because it can reveal AA originally hidden by VFF without leading to a prolongation of the electrophysiological study.

Characterization of Atrial Propagation Patterns and Fibrotic Substrate With a Modified Omnipolar Electrogram Strategy in Multi-Electrode Arrays

Introduction: The omnipolar electrogram method was recently proposed to try to generate orientation-independent electrograms. It estimates the electric field from the bipolar electrograms of a clique, under the assumption of locally plane and homogeneous propagation. The local electric field evolution over time describes a loop trajectory from which omnipolar signals in the propagation direction, substrate and propagation features, are derived. In this work, we propose substrate and conduction velocity mapping modalities based on a modified version of the omnipolar electrogram method, which aims to reduce orientation-dependent residual components in the standard approach.Methods: A simulated electrical propagation in 2D, with a tissue including a circular patch of diffuse fibrosis, was used for validation. Unipolar electrograms were calculated in a multi-electrode array, also deriving bipolar electrograms along the two main directions of the grid. Simulated bipolar electrograms were also contaminated with real noise, to assess the robustness of the mapping strategies against noise. The performance of the maps in identifying fibrosis and in reproducing unipolar reference voltage maps was evaluated. Bipolar voltage maps were also considered for performance comparison.Results: Results show that the modified omnipolar mapping strategies are more accurate and robust against noise than bipolar and standard omnipolar maps in fibrosis detection (accuracies higher than 85 vs. 80% and 70%, respectively). They present better correlation with unipolar reference voltage maps than bipolar and original omnipolar maps (Pearson's correlations higher than 0.75 vs. 0.60 and 0.70, respectively).Conclusion: The modified omnipolar method improves fibrosis detection, characterization of substrate and propagation, also reducing the residual sensitivity to directionality over the standard approach and improving robustness against noise. Nevertheless, studies with real electrograms will elucidate its impact in catheter ablation interventions.

Deep Learning Classification of Unipolar Electrograms in Human Atrial Fibrillation: Application in Focal Source Mapping

Focal sources are potential targets for atrial fibrillation (AF) catheter ablation, but they can be time-consuming and challenging to identify when unipolar electrograms (EGM) are numerous and complex. Our aim was to apply deep learning (DL) to raw unipolar EGMs in order to automate putative focal sources detection. We included 78 patients from the Focal Source and Trigger (FaST) randomized controlled trial that evaluated the efficacy of adjunctive FaST ablation compared to pulmonary vein isolation alone in reducing AF recurrence. FaST sites were identified based on manual classification of sustained periodic unipolar QS EGMs over 5-s. All periodic unipolar EGMs were divided into training (n = 10,004) and testing cohorts (n = 3,180). DL was developed using residual convolutional neural network to discriminate between FaST and non-FaST. A gradient-based method was applied to interpret the DL model. DL classified FaST with a receiver operator characteristic area under curve of 0.904 ± 0.010 (cross-validation) and 0.923 ± 0.003 (testing). At a prespecified sensitivity of 90%, the specificity and accuracy were 81.9 and 82.5%, respectively, in detecting FaST. DL had similar performance (sensitivity 78%, specificity 89%) to that of FaST re-classification by cardiologists (sensitivity 78%, specificity 79%). The gradient-based interpretation demonstrated accurate tracking of unipolar QS complexes by select DL convolutional layers. In conclusion, our novel DL model trained on raw unipolar EGMs allowed automated and accurate classification of FaST sites. Performance was similar to FaST re-classification by cardiologists. Future application of DL to classify FaST may improve the efficiency of real-time focal source detection for targeted AF ablation therapy.