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Signal Reconstruction of Pulmonary Vein Recordings Using a Phenomenological Mathematical Model: Application to Pulmonary Vein Isolation Therapy.

ABSTRACT: Atrial fibrillation (AF), the most prevalent cardiac arrhythmia, is commonly initiated by ectopic beats originating from a small myocardial sleeve extending over the pulmonary veins. Pulmonary vein isolation therapy attempts to isolate the pulmonary veins from the left atrium by ablating tissue, commonly by using radiofrequency ablation. During this procedure, the cardiologist records electrical activity using a lasso catheter, and the activation pattern recorded is used as a guide toward which regions to ablate. However, poor contact between electrode and tissue can lead to important regions of electrical activity not being recorded in clinic. We reproduce these signals through the use of a phenomenological model of the cardiac action potential on a cylinder, which we fit to post-AF atrial cells, and model the bipolar electrodes of the lasso catheter by an approximation of the surface potential. The resulting activation pattern is validated by direct comparison with those of clinical recordings. A potential application of the model is to reconstruct the missing electrical activity, minimizing the impact of the information loss on the clinical procedure, and we present results to demonstrate this.

SUBMITTER: Green HD

PROVIDER: S-EPMC5511835 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Project description:BackgroundPulmonary vein isolation (PVI) is an effective treatment strategy for patients with atrial fibrillation (AF), but many experience AF recurrence and require repeat ablation procedures. The goal of this study was to develop and evaluate a methodology that combines machine learning (ML) and personalized computational modeling to predict, before PVI, which patients are most likely to experience AF recurrence after PVI.MethodsThis single-center retrospective proof-of-concept study included 32 patients with documented paroxysmal AF who underwent PVI and had preprocedural late gadolinium enhanced magnetic resonance imaging. For each patient, a personalized computational model of the left atrium simulated AF induction via rapid pacing. Features were derived from pre-PVI late gadolinium enhanced magnetic resonance images and from results of simulations of AF induction. The most predictive features were used as input to a quadratic discriminant analysis ML classifier, which was trained, optimized, and evaluated with 10-fold nested cross-validation to predict the probability of AF recurrence post-PVI.ResultsIn our cohort, the ML classifier predicted probability of AF recurrence with an average validation sensitivity and specificity of 82% and 89%, respectively, and a validation area under the curve of 0.82. Dissecting the relative contributions of simulations of AF induction and raw images to the predictive capability of the ML classifier, we found that when only features from simulations of AF induction were used to train the ML classifier, its performance remained similar (validation area under the curve, 0.81). However, when only features extracted from raw images were used for training, the validation area under the curve significantly decreased (0.47).ConclusionsML and personalized computational modeling can be used together to accurately predict, using only pre-PVI late gadolinium enhanced magnetic resonance imaging scans as input, whether a patient is likely to experience AF recurrence following PVI, even when the patient cohort is small.

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Signal Reconstruction of Pulmonary Vein Recordings Using a Phenomenological Mathematical Model: Application to Pulmonary Vein Isolation Therapy.

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