Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

Ferrer-Albero, Ana; Godoy, Eduardo Jorge; Lozano, Miguel; Martinez-Mateu, Laura; Atienza, Felipe; Sáiz, Javier; Sebastián, Ramón Alonso

doi:10.1371/journal.pone.0181263

Cited by 19 publications

(18 citation statements)

References 48 publications

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“…It consists of a highly detailed 3D geometric model of the atria (754,893 nodes and 515,005 hexahedral elements with a homogeneous resolution of 300 μm) coupled to a torso model (254,976 nodes and 1.5 M tetrahedral elements) made up of lungs, bones, liver, ventricles, blood, and general torso, see Figure 1A . The atrial model includes specific fiber orientations in 21 different atrial regions, heterogeneous tissue conductivity and anisotropy ratios and heterogeneous cellular properties adjusted following the model by Ferrer-Albero et al ( 2017 ) and summarized in Table 1 .…”

Section: Methodsmentioning

confidence: 99%

“…For the electrophysiology simulations, we considered electrophysiological cellular heterogeneity in 10 different regions by adjusting I to , I CaL , and I Kr in the Courtemanche-Ramirez-Nattel (CRN) ionic model (Courtemanche et al, 1998 ), plus the well-established fibroblast cell model by MacCannell et al ( 2007 ) coupled to the CRN model. The tissue conductivities for each region defined together with their anisotropy ratios were obtained from Ferrer-Albero et al ( 2017 ) and summarized in Table 1 . The first three rows are the multiplicative factors used for the maximum conductance of three ( g to , g CaL , and g Kr ) ion channels with respect to the base Courtemanche-Ramirez-Nattel (CRN) ionic model (Courtemanche et al, 1998 ), and the next three rows are the longitudinal conductivity (σ l ), the ratio between the transverse and longitudinal conductivities (σ t /σ l ), and the longitudinal conduction velocity ( CV L ).…”

Section: Methodsmentioning

confidence: 99%

“…Triggering points were present in both the left and right atria, and were chosen to cover most of the atrial wall, in regions prone to elicit ectopic activity. Only in the case of the atrial model without fibrosis, did we consider an additional set of 38 extra triggering points in order to have more information on the healthy activation maps as in Ferrer-Albero et al ( 2017 ). Figure 1B shows the 57 ectopic locations used.…”

Section: Methodsmentioning

confidence: 99%

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Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

et al. 2018

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Introduction: Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria.Methodology: We have developed a 3D atrial model that includes 5 distributions of patchy fibrosis in the left atrium at 5 different stages. Each stage corresponds to a different amount of fibrosis that ranges from 2 to 40%. The 25 resulting 3D models were used for simulation of Focal Atrial Tachycardia (FAT), triggered from 19 different locations described in clinical studies. BSPM were obtained for all simulations, and the body surface potential integral maps (BSPiM) were calculated to describe atrial activations. A machine learning (ML) pipeline using a supervised learning model and support vector machine was developed to learn the BSPM patterns of each of the 475 activation sequences and relate them to the origin of the FAT source.Results: Activation maps for stages with more than 15% of fibrosis were greatly affected, producing conduction blocks and delays in propagation. BSPiMs did not always cluster into non-overlapped groups since BSPiMs were highly altered by the conduction blocks. From stage 3 (15% fibrosis) the BSPiMs showed differences for ectopic beats placed around the area of the pulmonary veins. Classification results were mostly above 84% for all the configurations studied when a large enough number of electrodes were used to map the torso. However, the presence of fibrosis increases the area of the ectopic focus location and therefore decreases the utility for the electrophysiologist.Conclusions: The results indicate that the proposed ML pipeline is a promising methodology for non-invasive ectopic foci localization from BSPM signal even when fibrosis is present.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

et al. 2018

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“…The biophysical simulations were carried out in a 3D model of the atria and torso previously developed [6,7], that considered the specific fibre orientation in 21 different anatomical regions, and tissue and cellular heterogeneity in 10 regions. The ionic cellular model used was Courtemanche for the healthy tissue and MacCannell for fibrotic tissue.…”

Section: Biophysical Modellingmentioning

confidence: 99%

Combining Biophysical Modeling and Machine Learning to Predict Location of Atrial Ectopic Triggers

Godoy¹,

Lozano²,

García-Fernández³

et al. 2018

2018 Computing in Cardiology Conference (CinC)

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The search for focal ectopic activity in the atria triggered from non-standard regions can be time consuming. The use of body surface potential maps to plan the intervention can be helpful, but require an advance processing of the data, that usually involves to solve an ill-posed inverse problem. In addition, changes in maps due to pathological substrate such as fibrosis might affect the expected electrical patterns. In this work, we use a machine learning approach to relate ectopic focus activity in different atrial regions with body surface potential maps, and consider the effects of fibrosis in various densities and distributions. Results show that as fibrosis increases over 15% the systems has to increase the region size in which an ectopic focus should be searched, but keeps the performance over 90% when at least 64 electrodes are used.

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“…Ferrer et al used this torso model to stablish the relationship between atrial electrical signals and BSPM in SR conditions, differentiating each region of the atria, 148 and to localize ectopic beats in the atria through BSPM. 162 Keller et al 158 developed a tetrahedron torso model based on the Visible Man dataset to solve the forward problem in cardiac electrophysiology, containing the following tissue types: myocardium, blood (both intracavitary and in the main vessels), lungs, fat (both visceral and subcutaneous), anisotropic skeletal muscle, intestine, liver, kidneys, bone, cartilage and spleen. They studied the influence of tissue conductivities on the ECG.…”

Section: D Torso Modelsmentioning

confidence: 99%

Mapping of the electrical activity of human atria. Multiscale modelling and simulations

Martinez-Mateu¹

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Atrial fibrillation is one of the most common cardiac arrhythmias seen in clinical practice. Therefore, it is of vital importance to develop new technologies aimed at diagnosing and terminating this kind of arrhythmia, to improve the quality of life of patients and to reduce costs to national health systems. In the last years, new atrial mapping techniques based on multi-electrode systems are increasingly being used to map the atrial electrical activity in humans and localise and target atrial fibrillation drivers in the form of focal sources or rotors. However, significant concerns remain about their accuracy and experimental approaches to analyse them are limited due to their invasive character. Therefore, computer simulations are a helpful tool to overcome these limitations since they can reproduce with fidelity experimental observations, permit to split the problem to treat into more simple substudies, and allow the possibility of performing preliminary investigations impossible to carry out in the clinical practice. This PhD thesis is focused on the analysis for accuracy of the multielectrode mapping systems through computational models and simulations. For this purpose, we developed realistic multiscale models in order to simulate atrial electrical reentrant activity, first in a sheet of atrial tissue and, then, in the whole atria. Then, we analysed the effects of the multi-electrode geometrical configurations on the accuracy of localizing rotors, by using multi-electrode arrays with equidistant inter-electrode distances, as well as multi-electrode basket catheters with non-equidistant inter-electrode distances. After computing the intracavitary unipolar electrograms, we performed phase maps, phase singularity detections to track rotors, and dominant frequency maps. We finally found out that the accuracy of multi-electrode mapping systems depends on their position inside the atrial cavity, the electrodeto-tissue distance, the inter-electrode distance, and the contribution of far field sources. Furthermore, as a consequence of these factors, false rotors might appear and could contribute to failure of atrial fibrillation ablation procedures. xii RESUM llunyà. A més, com a conseqüència d'aquests factors, es va observar l'aparició de rotors falsos que podrien contribuir al fracàs de l'ablació de la fibril•lació auricular.

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Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps

Cited by 19 publications

References 48 publications

Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

Combining Biophysical Modeling and Machine Learning to Predict Location of Atrial Ectopic Triggers

Mapping of the electrical activity of human atria. Multiscale modelling and simulations

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