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

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

Computational models of the heart for planning and treatment of outflow tract ventricular arrhythmias

The purpose of this thesis was to develop personalised cardiovascular therapy guided by multimodal noninvasive imaging and simulations, combined with artificial intelligence tools, for the management of the outflow tract ventricular arrhythmias. The main contributions of this thesis are twofold: -We propose a pipeline to build heart computational models for simulation of ventricular tachycardia that incorporates a new specific rule-base method for fiber generation, including the ventricular outflow tracts. The pipeline allows carrying out multiscale simulations, obtaining the patient ECG for different scenarios. -We exploit the simulated data in two different ways. First, we analyse the patient's ECG preoperatively and compare it with the simulated ECGs to find the most probable site of origin of the tachycardia. In cases in which we do not have patient imaging data, we classify the patient ECG by machine learning techniques to predict the site of origin, using the simulated ECGs for training.El objetivo principal de esta tesis fue el desarrollo de una terapia cardiovascular personalizada guiada por información multimodal no invasiva y simulaciones, combinadas con herramientas de inteligencia artificial, para el manejo de taquicardias ventriculares idiopáticas originadas en los tractos de salida del ventrículo. Las principales contribuciones de esta tesis son dos: -Desarrollo de un método de creación de modelos computacionales del corazón con el fin de simular taquicardias ventriculares, que incluye un nuevo modelo específico para calcular la orientación de las fibras en los tractos de salida del corazón. Este método permite realizar simulaciones multiescala, obteniendo el ECG virtual de cada paciente para diferentes escenarios. -Tratamiento de los resultados de las simulaciones. Primero, los ECG reales de los pacientes fueron comparados con ECGs simulados para encontrar el sitio de origen más probable de la taquicardia. En los casos en los que no se disponía de datos de imagen del paciente, el ECG del paciente fue clasificado mediante técnicas de aprendizaje automático, entrenadas con los datos simulados, para predecir el sitio de origen

Computational models of the heart for planning and treatment of outflow tract ventricular arrhythmias

The purpose of this thesis was to develop personalised cardiovascular therapy guided by multimodal noninvasive imaging and simulations, combined with artificial intelligence tools, for the management of the outflow tract ventricular arrhythmias. The main contributions of this thesis are twofold: -We propose a pipeline to build heart computational models for simulation of ventricular tachycardia that incorporates a new specific rule-base method for fiber generation, including the ventricular outflow tracts. The pipeline allows carrying out multiscale simulations, obtaining the patient ECG for different scenarios. -We exploit the simulated data in two different ways. First, we analyse the patient's ECG preoperatively and compare it with the simulated ECGs to find the most probable site of origin of the tachycardia. In cases in which we do not have patient imaging data, we classify the patient ECG by machine learning techniques to predict the site of origin, using the simulated ECGs for training.El objetivo principal de esta tesis fue el desarrollo de una terapia cardiovascular personalizada guiada por información multimodal no invasiva y simulaciones, combinadas con herramientas de inteligencia artificial, para el manejo de taquicardias ventriculares idiopáticas originadas en los tractos de salida del ventrículo. Las principales contribuciones de esta tesis son dos: -Desarrollo de un método de creación de modelos computacionales del corazón con el fin de simular taquicardias ventriculares, que incluye un nuevo modelo específico para calcular la orientación de las fibras en los tractos de salida del corazón. Este método permite realizar simulaciones multiescala, obteniendo el ECG virtual de cada paciente para diferentes escenarios. -Tratamiento de los resultados de las simulaciones. Primero, los ECG reales de los pacientes fueron comparados con ECGs simulados para encontrar el sitio de origen más probable de la taquicardia. En los casos en los que no se disponía de datos de imagen del paciente, el ECG del paciente fue clasificado mediante técnicas de aprendizaje automático, entrenadas con los datos simulados, para predecir el sitio de origen

Standard quasi-conformal flattening of the right and left atria

Two-dimensional standard representations of 3D anatomical structures are a simple and intuitive way for analysing patient information across populations and image modalities. They also allow convenient visualizations that can be included in clinical reports for a fast overview of the whole structure. While cardiac ventricles, especially the left ventricle, have an established standard representation (e.g. bull’s eye plot), the 2D depiction of the left (LA) and right atrium (RA) remains challenging due to their sub-structural complexity. Quasi-conformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the atria to correctly place the adjacent structures, i.e. the pulmonary veins, the vena cava (VC) or the appendages. Some registration-based methods exist to flatten the LA but they can be time-consuming and prone to errors if the geometries are very different. We propose a novel atrial flattening methodology where a quasi-conformal 2D map of both (left and right) atria is obtained quickly and without errors related to registration. In our approach the RA is mapped to a standard 2D map where the holes corresponding to superior and inferior VC are fixed within a disk. Similarly, the LA is divided into 5 regions which are then mapped to their analogous two-dimensional regions. We illustrate the application of the method to visualize atrial wall thickness measurements, and late gadolinium enhanced magnetic resonance data.This study was partially funded by the Spanish Ministry of Economy and Competitiveness (DPI2015-71640-R), by the “Fundació La Marató de TV3” (no 20154031) and by European Union Horizon 2020 Programme for Research and Innovation, under grant agreement No. 642676 (CardioFunXion)

Smoothed particle hydrodynamics for electrophysiological modeling: an alternative to finite element methods

Comunicació presentada a la 9th international conference on Functional Imaging and Modeling of the Heart (FIMH 2017), celebrada els dies 11 a 13 de juny de 2017 a Toronto, Canadà.Finite element methods (FEM) are generally used in cardiac 3D-electromechanical modeling. For FEM modeling, a step of a suitable mesh construction is required, which is non-trivial and time-consuming for complex geometries. A meshless method is proposed to avoid meshing. The smoothed particle hydrodynamics (SPH) method was used to solve an electrophysiological model on a left ventricle extracted from medical imaging straightforwardly, without any need of a complex mesh. The proposed method was compared against FEM in the same left-ventricular model. Both FEM and SPH methods provide similar solutions of the models in terms of depolarization times. Main differences were up to 10.9% at the apex. Finally, a pathological application of SPH is shown on the same ventricular geometry with an added scar on the heart wall.The work is supported by the European Union Horizon 2020 research and innovation programme under grant agreement No 642676 (CardioFunXion)

Smoothed particle hydrodynamics for electrophysiological modeling: an alternative to finite element methods

Comunicació presentada a la 9th international conference on Functional Imaging and Modeling of the Heart (FIMH 2017), celebrada els dies 11 a 13 de juny de 2017 a Toronto, Canadà.Finite element methods (FEM) are generally used in cardiac 3D-electromechanical modeling. For FEM modeling, a step of a suitable mesh construction is required, which is non-trivial and time-consuming for complex geometries. A meshless method is proposed to avoid meshing. The smoothed particle hydrodynamics (SPH) method was used to solve an electrophysiological model on a left ventricle extracted from medical imaging straightforwardly, without any need of a complex mesh. The proposed method was compared against FEM in the same left-ventricular model. Both FEM and SPH methods provide similar solutions of the models in terms of depolarization times. Main differences were up to 10.9% at the apex. Finally, a pathological application of SPH is shown on the same ventricular geometry with an added scar on the heart wall.The work is supported by the European Union Horizon 2020 research and innovation programme under grant agreement No 642676 (CardioFunXion)

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

Aims A pre-operative non-invasive identification of the site of origin (SOO) of outflow tract ventricular arrhythmias (OTVAs) is important to properly plan radiofrequency ablation procedures. Although some algorithms based on electrocardiograms (ECGs) have been developed to predict left vs. right ventricular origins, their accuracy is still limited, especially in complex anatomies. The aim of this work is to use patient-specific electrophysiological simulations of the heart to predict the SOO in OTVA patients. Methods and results An in silico pace-mapping procedure was designed and used on 11 heart geometries, generating for each case simulated ECGs from 12 clinically plausible SOO. Subsequently, the simulated ECGs were compared with patient ECG data obtained during the clinical tachycardia using the 12-lead correlation coefficient (12-lead ρ). Left ventricle (LV) vs. right ventricle (RV) SOO was estimated by computing the LV/RV ratio for each patient, obtained by dividing the average 12-lead ρ value of the LV- and RV-SOO simulated ECGs, respectively. Simulated ECGs that had virtual sites close to the ablation points that stopped the arrhythmia presented higher correlation coefficients. The LV/RV ratio correctly predicted LV vs. RV SOO in 10/11 cases; 1.07 vs. 0.93 P < 0.05 for 12-lead ρ. Conclusion The obtained results demonstrate the potential of the developed in silico pace-mapping technique to complement standard ECG for the pre-operative planning of complex ventricular arrhythmias.This work was supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme [MDM-2015-0502]

A Rule-Based method to model myocardial fiber orientation for simulating ventricular outflow tract arrhythmias

Comunicació presentada a: FIMH 2017 9th International Conference, celebrada a Toronto, Canadà, de l'11 al 13 de juny de 2017.Myocardial fiber orientation determines the propagation of electrical waves in the heart and the contraction of cardiac tissue. One common approach for assigning fiber orientation to cardiac anatomi- cal models are Rule-Based Methods (RBM). However, RBM have been developed to assimilate data mostly from the Left Ventricle. In conse- quence, fiber information from RBM does not match with histological data in other areas of the heart, having a negative impact in cardiac simulations beyond the LV. In this work, we present a RBM where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the right ventricle endocardium, the interventricular sep- tum and the outow tracts. Electrophysiological simulations including these anatomical structures were then performed, with patient-specific data of outow tract ventricular arrhythmias (OTVA) cases. A compar- ison between the obtained simulations and electro-anatomical data of these patients confirm the potential for in silico identification of the site of origin in OTVAs before the intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunX- ion

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunXion)

A rule-based method to model myocardial fiber orientation in cardiac biventricular geometries with outflow tracts

Rule-based methods are often used for assigning fiber orientation to cardiac anatomical models. However, existing methods have been developed using data mostly from the left ventricle. As a consequence, fiber information obtained from rule-based methods often does not match histological data in other areas of the heart such as the right ventricle, having a negative impact in cardiac simulations beyond the left ventricle. In this work, we present a rule-based method where fiber orientation is separately modeled in each ventricle following observations from histology. This allows to create detailed fiber orientation in specific regions such as the endocardium of the right ventricle, the interventricular septum and the outflow tracts. We also carried out electrophysiological simulations involving these structures and with different fiber configurations. In particular, we built a modelling pipeline for creating patient-specific volumetric meshes of biventricular geometries, including the outflow tracts, and subsequently simulate the electrical wavefront propagation in outflow tract ventricular arrhythmias with different origins for the ectopic focus. The resulting simulations with the proposed rule-based method showed a very good agreement with clinical parameters such as the 10 ms isochrone ratio in a cohort of nine patients suffering from this type of arrhythmia. The developed modelling pipeline confirms its potential for an in silico identification of the site of origin in outflow tract ventricular arrhythmias before clinical intervention.This work was partially funded by the European Union under the Horizon 2020 Programme for Research, Innovation (grant agreement No. 642676 CardioFunXion