Physiology-based regularization of the electrocardiographic inverse problem

The inverse problem of electrocardiography aims at noninvasively reconstructing electrical activity of the heart from recorded body-surface electrocardiograms. A crucial step is regularization, which deals with ill-posedness of the problem by imposing constraints on the possible solutions. We developed a regularization method that includes electrophysiological input. Body-surface potentials are recorded and a computed tomography scan is performed to obtain the torso-heart geometry. Propagating waveforms originating from several positions at the heart are simulated and used to generate a set of basis vectors representing spatial distributions of potentials on the heart surface. The real heart-surface potentials are then reconstructed from the recorded body-surface potentials by finding a sparse representation in terms of this basis. This method, which we named 'physiology-based regularization' (PBR), was compared to traditional Tikhonov regularization and validated using in vivo recordings in dogs. PBR recovered details of heart-surface electrograms that were lost with traditional regularization, attained higher correlation coefficients and led to improved estimation of recovery times. The best results were obtained by including approximate knowledge about the beat origin in the PBR basis

The circle of reentry: Characteristics of trigger-substrate interaction leading to sudden cardiac arrest

Sudden cardiac death is often caused by ventricular arrhythmias driven by reentry. Comprehensive characterization of the potential triggers and substrate in survivors of sudden cardiac arrest has provided insights into the trigger-substrate interaction leading to reentry. Previously, a “Triangle of Arrhythmogenesis”, reflecting interactions between substrate, trigger and modulating factors, has been proposed to reason about arrhythmia initiation. Here, we expand upon this concept by separating the trigger and substrate characteristics in their spatial and temporal components. This yields four key elements that are required for the initiation of reentry: local dispersion of excitability (e.g., the presence of steep repolarization time gradients), a critical relative size of the region of excitability and the region of inexcitability (e.g., a sufficiently large region with early repolarization), a trigger that originates at a time when some tissue is excitable and other tissue is inexcitable (e.g., an early premature complex), and which occurs from an excitable region (e.g., from a region with early repolarization). We discuss how these findings yield a new mechanistic framework for reasoning about reentry initiation, the “Circle of Reentry.” In a patient case of unexplained ventricular fibrillation, we then illustrate how a comprehensive clinical investigation of these trigger-substrate characteristics may help to understand the associated arrhythmia mechanism. We will also discuss how this reentry initiation concept may help to identify patients at risk, and how similar reasoning may apply to other reentrant arrhythmias

Understanding repolarization in the intracardiac unipolar electrogram: A long-lasting controversy revisited

Background: The optimal way to determine repolarization time (RT) from the intracardiac unipolar electrogram (UEG) has been a topic of debate for decades. RT is typically determined by either the Wyatt method or the “alternative method,” which both consider UEG T-wave slope, but differently.Objective: To determine the optimal method to measure RT on the UEG.Methods: Seven pig hearts surrounded by an epicardial sock with 100 electrodes were Langendorff-perfused with selective cannulation of the left anterior descending (LAD) coronary artery and submersed in a torso-shaped tank containing 256 electrodes on the torso surface. Repolarization was prolonged in the non-LAD-regions by infusing dofetilide and shortened in the LAD-region using pinacidil. RT was determined by the Wyatt (tWyatt) and alternative (tAlt) methods, in both invasive (recorded with epicardial electrodes) and in non-invasive UEGs (reconstructed with electrocardiographic imaging). tWyatt and tAlt were compared to local effective refractory period (ERP).Results: With contact mapping, mean absolute error (MAE) of tWyatt and tAlt vs. ERP were 21 ms and 71 ms, respectively. Positive T-waves typically had an earlier ERP than negative T-waves, in line with theory. tWyatt -but not tAlt-shortened by local infusion of pinacidil. Similar results were found for the non-invasive UEGs (MAE of tWyatt and tAlt vs. ERP were 30 ms and 92 ms, respectively).Conclusion: The Wyatt method is the most accurate to determine RT from (non) invasive UEGs, based on novel and historical analyses. Using it to determine RT could unify and facilitate repolarization assessment and amplify its role in cardiac electrophysiology

Noninvasive reconstruction of cardiac electrical activity: Mathematical innovation, in vivo validation and human application

Cardiac arrhythmias can cause serious symptoms and can even be life-threatening. Electrical activity of the heart can be evaluated by performing an electrocardiogram (ECG). This dissertation describes the development, validation and application of and the improvements to electrocardiographic imaging (ECGI). This is a new technique in which three-dimensional images of the electrical activity of a patient’s beating heart are obtained without the necessity of internal measurements. This dissertation describes how mathematical improvements result in more accurate outcomes, and how this technique substantially contributes to the diagnosis of cardiac arrhythmias in patients

CT-Scan Free Neural Network-Based Reconstruction of Heart Surface Potentials From ECG Recordings

The inverse problem in electrocardiography concerns mapping electrical activity measured on the surface of the body back onto the heart in a non-invasive way. With the use of CT-scans and mathematical/geometric models of the human body, it is possible to translate body surface recording into epicardial potentials which provide advanced diagnostic information of the heart activity that a standard ECG or BSPM is unable to, especially for specific heart conditions such as arrhythmia. An encoder-decoder structure is proposed as an approach which encodes body surface potentials into latent representations before using them as input to be decoded into epicardial potentials without the use of geometric information obtained from a CT-scan. Using data from an ECG-Imaging experiment performed on dogs [1], a proof of concept is created by predicting the general wave-forms of 98 heart surface electrodes based on 168 body electrodes. The neural network manages to reconstruct the heart surface potentials with a mean square error of 0.332mV +/- 0.442 on the training set and 0.763mV +/- 0.336 on the testing set

Influence of image artifacts on image-based computer simulations of the cardiac electrophysiology

Myocardial infarct patients have an increased risk of scar-based ventricular tachycardia. Late gadolinium enhanced magnetic resonance (MR) imaging provides the geometric extent of myocardial infarct. Computational electrophysiological models based on such images can provide a personalized prediction of the patient's tachycardia risk. In this work, the effect of respiratory slice alignment image artifacts on image-based electrophysiological simulations is investigated in two series of models. For the first series, a clinical MR image is used in which slice translations are applied to artificially induce and correct for slice misalignment. For the second series, computer simulated MR images with and without slice misalignments are created using a mechanistic anatomical phantom of the torso. From those images, personalized models are created in which electrical stimuli are applied in an attempt to induce tachycardia. The response of slice-aligned and slice-misaligned models to different interval stimuli is used to assess tachycardia risk. The presented results indicate that slice misalignments affect image-based simulation outcomes. The extent to which the assessed risk is affected is found to depend upon the geometry of the infarct area. The number of unidirectional block tachycardias varied from 1 to 3 inducible patterns depending on slice misalignment severity and, along with it, the number of tachycardia inducing stimuli locations varied from 2 to 4 from 6 different locations. For tachycardias sustained by conducting channels through the scar core, no new patterns are induced by altering the slice alignment in the corresponding image. However, it affected the assessed risk as tachycardia inducing stimuli locations varied from 1 to 5 from the 6 stimuli locations. In addition, if the conducting channel is not maintained in the image due to slice misalignments, the channel-dependent tachycardia is not inducible anymore in the image-based model