In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations

Doste, Rubén; Sebastián, Rafael; Gómez, Juan Francisco Cerón; Soto‐Iglesias, David; Alcaine, Alejandro; Mont, Lluís; Berruezo, Antonio; Penela, Diego; Cámara, Óscar

doi:10.1093/europace/euaa102

Cited by 12 publications

(11 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The general conclusion is that the BSP maps were found to be similar using various torso models, but the detailed inhomogeneous models produced potential amplitudes closer to the true potentials, with blood and the anisotropic skeletal muscle being the most influential tissues. More recently, the liver tissue has also been included as a significant inhomogeneity in addition to the other tissues (Potyagaylo et al, 2016;Doste et al, 2020;Schuler et al, 2022). Thus, future work could focus on the systematic evaluation of the effects of individual tissues on PVC localization with various source models.…”

Section: Limitations Of the Studymentioning

confidence: 99%

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Doğrusöz¹,

Rasoolzadeh²,

Ondrusova³

et al. 2023

Front. Physiol.

View full text Add to dashboard Cite

Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances.Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial–endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated.Results: For paced data, the median LE values were 25.2 and 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2–33.0 mm for the dipole-based model and 28.9–39.2 mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial–endocardial ventricular surface.Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).

show abstract

Section: Limitations Of the Studymentioning

confidence: 99%

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Doğrusöz¹,

Rasoolzadeh²,

Ondrusova³

et al. 2023

Front. Physiol.

View full text Add to dashboard Cite

show abstract

“…Every element was labeled according to its cellular properties as, endocardial, midmyocardial or epicardial cells. As described in Doste et al (2020), for each digital twin, we simulated OTVAs from 12 different SOOs (see Figure 1, digital twins, spheres on biventricular geometry) chosen following clinical observations (Anderson et al, 2019), seven from the LVOT and five from the RVOT.To perform the simulations at the organ level, we used the software ELVIRA (Heidenreich et al, 2010), which solves the anisotropic reaction-diffusion equation of the monodomain model for cardiac EP using finite element methods. For the numerical solution of our simulations, we applied the conjugate gradient method with an integration time step of 0.02 ms, using implicit integration for the parabolic partial differential equation of monodomain model and explicit integration with adaptive time stepping for ordinary differential equation of the ionic model (ten Tusscher et al, 2004).…”

Section: Virtual Electrocardiogram Generationmentioning

confidence: 99%

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

et al. 2022

Self Cite

View full text Add to dashboard Cite

In order to determine the site of origin (SOO) in outflow tract ventricular arrhythmias (OTVAs) before an ablation procedure, several algorithms based on manual identification of electrocardiogram (ECG) features, have been developed. However, the reported accuracy decreases when tested with different datasets. Machine learning algorithms can automatize the process and improve generalization, but their performance is hampered by the lack of large enough OTVA databases. We propose the use of detailed electrophysiological simulations of OTVAs to train a machine learning classification model to predict the ventricular origin of the SOO of ectopic beats. We generated a synthetic database of 12-lead ECGs (2,496 signals) by running multiple simulations from the most typical OTVA SOO in 16 patient-specific geometries. Two types of input data were considered in the classification, raw and feature ECG signals. From the simulated raw 12-lead ECG, we analyzed the contribution of each lead in the predictions, keeping the best ones for the training process. For feature-based analysis, we used entropy-based methods to rank the obtained features. A cross-validation process was included to evaluate the machine learning model. Following, two clinical OTVA databases from different hospitals, including ECGs from 365 patients, were used as test-sets to assess the generalization of the proposed approach. The results show that V2 was the best lead for classification. Prediction of the SOO in OTVA, using both raw signals or features for classification, presented high accuracy values (>0.96). Generalization of the network trained on simulated data was good for both patient datasets (accuracy of 0.86 and 0.84, respectively) and presented better values than using exclusively real ECGs for classification (accuracy of 0.84 and 0.76 for each dataset). The use of simulated ECG data for training machine learning-based classification algorithms is critical to obtain good SOO predictions in OTVA compared to real data alone. The fast implementation and generalization of the proposed methodology may contribute towards its application to a clinical routine.

show abstract

“…Some solutions exist for addressing these issues. Data scarcity has been addressed in the literature through pseudo labels [43] or through synthetic data generation, either using simulations [44] or generative adversarial networks (GANs), but these present efficiency issues (simulations) or face difficulties when extending beyond the training data manifold (data-driven approaches). Data priors, on their behalf, have been enforced either by producing representations that explicitly exclude previously known information via minimizing mutual information [20] or by providing the specific prior as input data (e.g., by including the label as an input to the model, such as in conditional GANs [45]), but the ability to explicitly control data-side priors is still limited.…”

Section: Loss Functionmentioning

confidence: 99%

Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation

Jiménez-Pérez¹,

Acosta²,

Alcaine³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Obtaining per-beat information is a key task in the analysis of cardiac electrocardiograms (ECG), as many downstream diagnosis tasks are dependent on ECG-based measurements. Those measurements, however, are costly to produce and time-consuming to process in bulk, especially in recordings that change throughout long periods of time. Currently, ECG delineation is performed either using digital signal processing (DSP), which are able to produce high-quality delineations but are difficult to generalize, and machine learning (ML), which commonly produces increased performance at the cost of needing large databases of annotated data. However, the existing annotated databases for ECG delineation are small, being insufficient in size and in the array of pathological conditions they represent. This article delves into the latter with two main contributions. First, a pseudo-synthetic data generation scheme was developed, based in probabilistically composing unseen ECG traces given "pools" of fundamental segments cropped from the original databases and a set of rules for their arrangement into coherent synthetic traces. The generation of conditions is controlled by imposing expert knowledge on the generated trace, which increases the input variability for training the model. Second, two novel segmentation-based loss functions have been developed, which attempt at enforcing the prediction of an exact number of independent structures and at producing closer segmentation boundaries by focusing on a reduced number of samples. The best performing model obtained an F 1 -score of 99.38% and a delineation error of 2.19 ± 17.73 ms and 4.45 ± 18.32 ms for all wave's fiducials (onsets and offsets, respectively), as averaged across the P, QRS and T waves for three distinct freely available databases. The excellent results were obtained despite the heterogeneous characteristics of the tested databases, in terms of lead configurations (Holter, 12-lead), sampling frequencies (250, 500 and 2, 000 Hz) and represented pathophysiologies (e.g., different types of arrhythmias, sinus rhythm with structural heart disease), hinting at its generalization capabilities, while outperforming current state-of-the-art delineation approaches.

show abstract

In silico pace-mapping: prediction of left vs. right outflow tract origin in idiopathic ventricular arrhythmias with patient-specific electrophysiological simulations

Cited by 12 publications

References 20 publications

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data

Training machine learning models with synthetic data improves the prediction of ventricular origin in outflow tract ventricular arrhythmias

Generalizing electrocardiogram delineation -- Training convolutional neural networks with synthetic data augmentation

Contact Info

Product

Resources

About