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Godoy EJ, Lozano M, García-Fernández I, Ferrer-Albero A, MacLeod R, Saiz J, Sebastian R. Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study. Front Physiol 2018; 9:404. [PMID: 29867517 PMCID: PMC5968126 DOI: 10.3389/fphys.2018.00404] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2017] [Accepted: 04/04/2018] [Indexed: 12/13/2022] Open
Abstract
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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Affiliation(s)
- Eduardo Jorge Godoy
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Miguel Lozano
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Ignacio García-Fernández
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Ana Ferrer-Albero
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Rob MacLeod
- Department of Bioengineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States
| | - Javier Saiz
- Centro de Investigación en Bioingeniería, Universitat Politécnica de Valencia, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab, Department of Computer Science, Universitat de Valencia, Valencia, Spain
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Ferrer-Albero A, Godoy EJ, Lozano M, Martínez-Mateu L, Atienza F, Saiz J, Sebastian R. Non-invasive localization of atrial ectopic beats by using simulated body surface P-wave integral maps. PLoS One 2017; 12:e0181263. [PMID: 28704537 PMCID: PMC5509320 DOI: 10.1371/journal.pone.0181263] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2017] [Accepted: 06/28/2017] [Indexed: 01/22/2023] Open
Abstract
Non-invasive localization of continuous atrial ectopic beats remains a cornerstone for the treatment of atrial arrhythmias. The lack of accurate tools to guide electrophysiologists leads to an increase in the recurrence rate of ablation procedures. Existing approaches are based on the analysis of the P-waves main characteristics and the forward body surface potential maps (BSPMs) or on the inverse estimation of the electric activity of the heart from those BSPMs. These methods have not provided an efficient and systematic tool to localize ectopic triggers. In this work, we propose the use of machine learning techniques to spatially cluster and classify ectopic atrial foci into clearly differentiated atrial regions by using the body surface P-wave integral map (BSPiM) as a biomarker. Our simulated results show that ectopic foci with similar BSPiM naturally cluster into differentiated non-intersected atrial regions and that new patterns could be correctly classified with an accuracy of 97% when considering 2 clusters and 96% for 4 clusters. Our results also suggest that an increase in the number of clusters is feasible at the cost of decreasing accuracy.
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Affiliation(s)
- Ana Ferrer-Albero
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
- * E-mail:
| | - Eduardo J. Godoy
- Computational Multiscale Physiology Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Miguel Lozano
- Computational Multiscale Physiology Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
| | - Laura Martínez-Mateu
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | | | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Physiology Lab (CoMMLab), Department of Computer Science, Universitat de Valencia, Valencia, Spain
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Cakulev I, Sahadevan J, Waldo AL. Noninvasive diagnostic mapping of supraventricular arrhythmias (Wolf-Parkinson-White syndrome and atrial arrhythmias). Card Electrophysiol Clin 2015; 7:79-88. [PMID: 25784024 DOI: 10.1016/j.ccep.2014.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
The 12-lead electrocardiogram has limited value in precisely identifying the origin of focal or critical component of reentrant arrhythmias during supraventricular arrhythmias, as well as precisely locating accessory atrioventricular conduction pathways. Because of these limitations, efforts have been made to reconstruct epicardial activation sequences from body surface measurements obtained noninvasively. The last decade has registered significant progress in obtaining clinically useful data from the attempts to noninvasively map the epicardial electrical activity. This article summarizes the recent advances made in this area, specifically addressing the clinical outcomes of such efforts relating to atrial arrhythmias and Wolf-Parkinson-White syndrome.
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Affiliation(s)
- Ivan Cakulev
- Division of Cardiovascular Medicine, Department of Medicine, Harrington Heart & Vascular Institute, University Hospitals Case Medical Center, 11100 Euclid Avenue, MS LKS 5038, Cleveland, OH 44106, USA.
| | - Jayakumar Sahadevan
- Department of Cardiology, Louis Stokes Cleveland Veterans Affairs Medical Center, 10701 East Boulevard, Cleveland, OH 44106, USA
| | - Albert L Waldo
- Division of Cardiovascular Medicine, Department of Medicine, Harrington Heart & Vascular Institute, University Hospitals Case Medical Center, 11100 Euclid Avenue, MS LKS 5038, Cleveland, OH 44106, USA
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Rodrigo M, Pedrón-Torecilla J, Hernández I, Liberos A, Climent AM, Guillem MS. Data analysis in cardiac arrhythmias. Methods Mol Biol 2014; 1246:217-35. [PMID: 25417089 DOI: 10.1007/978-1-4939-1985-7_14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Abstract
Cardiac arrhythmias are an increasingly present in developed countries and represent a major health and economic burden. The occurrence of cardiac arrhythmias is closely linked to the electrical function of the heart. Consequently, the analysis of the electrical signal generated by the heart tissue, either recorded invasively or noninvasively, provides valuable information for the study of cardiac arrhythmias. In this chapter, novel cardiac signal analysis techniques that allow the study and diagnosis of cardiac arrhythmias are described, with emphasis on cardiac mapping which allows for spatiotemporal analysis of cardiac signals.Cardiac mapping can serve as a diagnostic tool by recording cardiac signals either in close contact to the heart tissue or noninvasively from the body surface, and allows the identification of cardiac sites responsible of the development or maintenance of arrhythmias. Cardiac mapping can also be used for research in cardiac arrhythmias in order to understand their mechanisms. For this purpose, both synthetic signals generated by computer simulations and animal experimental models allow for more controlled physiological conditions and complete access to the organ.
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Affiliation(s)
- Miguel Rodrigo
- BIO-ITACA, Universitat Politècnica de València, Edificio 8G, Camino de Vera, S/N, 46022, Valencia, Spain
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GUILLEM MARIAS, CLIMENT ANDREUM, CASTELLS FRANCISCO, HUSSER DANIELA, MILLET JOSE, ARYA ARASH, PIORKOWSKI CHRISTOPHER, BOLLMANN ANDREAS. Noninvasive Mapping of Human Atrial Fibrillation. J Cardiovasc Electrophysiol 2009; 20:507-13. [DOI: 10.1111/j.1540-8167.2008.01356.x] [Citation(s) in RCA: 47] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Modre R, Seger M, Fischer G, Hintermüller C, Hayn D, Pfeifer B, Hanser F, Schreier G, Tilg B. Cardiac anisotropy: is it negligible regarding noninvasive activation time imaging? IEEE Trans Biomed Eng 2006; 53:569-80. [PMID: 16602563 DOI: 10.1109/tbme.2006.870253] [Citation(s) in RCA: 24] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The aim of this study was to quantify the effect of cardiac anisotropy in the activation-based inverse problem of electrocardiography. Differences of the patterns of simulated body surface potential maps for isotropic and anisotropic conditions were investigated with regard to activation time (AT) imaging of ventricular depolarization. AT maps were estimated by solving the nonlinear inverse ill-posed problem employing spatio-temporal regularization. Four different reference AT maps (sinus rhythm, right-ventricular and septal pacing, accessory pathway) were calculated with a bidomain theory based anisotropic finite-element heart model in combination with a cellular automaton. In this heart model a realistic fiber architecture and conduction system was implemented. Although the anisotropy has some effects on forward solutions, effects on inverse solutions are small indicating that cardiac anisotropy might be negligible for some clinical applications (e.g., imaging of focal events) of our AT imaging approach. The main characteristic events of the AT maps were estimated despite neglected electrical anisotropy in the inverse formulation. The worst correlation coefficient of the estimated AT maps was 0.810 in case of sinus rhythm. However, all characteristic events of the activation pattern were found. The results of this study confirm our clinical validation studies of noninvasive AT imaging in which cardiac anisotropy was neglected.
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Affiliation(s)
- Robert Modre
- University for Health Sciences, Medical Informatics and Technology (UMIT), Hall in Tirol 6060, Austria.
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Messnarz B, Seger M, Modre R, Fischer G, Hanser F, Tilg B. A comparison of noninvasive reconstruction of epicardial versus transmembrane potentials in consideration of the null space. IEEE Trans Biomed Eng 2004; 51:1609-18. [PMID: 15376509 DOI: 10.1109/tbme.2004.828038] [Citation(s) in RCA: 40] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
We compare two source formulations for the electrocardiographic forward problem in consideration of their implications for regularizing the ill-posed inverse problem. The established epicardial potential source model is compared with a bidomain-theory-based transmembrane potential source formulation. The epicardial source approach is extended to the whole heart surface including the endocardial surfaces. We introduce the concept of the numerical null and signal space to draw attention to the problems associated with the nonuniqueness of the inverse solution and show that reconstruction of null-space components is an important issue for physiologically meaningful inverse solutions. Both formulations were tested with simulated data generated with an anisotropic heart model and with clinically measured data of two patients. A linear and a recently proposed quasi-linear inverse algorithm were applied for reconstructions of the epicardial and transmembrane potential, respectively. A direct comparison of both formulations was performed in terms of computed activation times. We found the transmembrane potential-based formulation is a more promising source formulation as stronger regularization by incorporation of biophysical a priori information is permitted.
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Affiliation(s)
- Bernd Messnarz
- University for Health Informatics and Technology, Tyrol, Innsbruck, Austria.
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Modre R, Tilg B, Fischer G, Hanser F, Messnarz B, Seger M, Schocke MFH, Berger T, Hintringer F, Roithinger FX. Atrial noninvasive activation mapping of paced rhythm data. J Cardiovasc Electrophysiol 2003; 14:712-9. [PMID: 12930250 DOI: 10.1046/j.1540-8167.2003.02558.x] [Citation(s) in RCA: 45] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Atrial arrhythmias have emerged as a topic of great interest for clinical electrophysiologists. Noninvasive imaging of electrical function in humans may be useful for computer-aided diagnosis and treatment of cardiac arrhythmias, which can be accomplished by the fusion of data from ECG mapping and magnetic resonance imaging (MRI). METHODS AND RESULTS In this study, a bidomain-theory-based surface heart model activation time (AT) imaging approach was applied to paced rhythm data from four patients. Pacing sites were the right superior pulmonary vein, left inferior pulmonary vein, left superior pulmonary vein, coronary sinus, posterior wall of right atrium, and high right atrium. For coronary sinus pacing, the AT pattern of the right atrium was compared with a CARTO map. The root mean square error between CARTO geometry (85 nodal points) and the surface model of the right atrium was 8.6 mm. The correlation coefficient of the noninvasively obtained AT map of the right atrium and the CARTO map was 0.76. All pulmonary vein pacing sites were identified. The reconstructed pacing site of right posterior atrial pacing correlates with the invasively determined pacing catheter position with a localization distance of 4 mm. CONCLUSION The individual anatomic model of the atria of each patient enables accurate noninvasive AT imaging within the atria, resulting in a localization error for the pacing sites within 10 mm. Our findings may have implications for imaging of atrial activity in patients with focal arrhythmias or focal triggers.
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Affiliation(s)
- Robert Modre
- Institute for Medical Signal Processing and Imaging, University for Health Informatics and Technology Tyrol, Innrain 98, 6020 Innsbruck, Austria.
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Tilg B, Fischer G, Modre R, Hanser F, Messnarz B, Schocke M, Kremser C, Berger T, Hintringer F, Roithinger FX. Model-based imaging of cardiac electrical excitation in humans. IEEE TRANSACTIONS ON MEDICAL IMAGING 2002; 21:1031-1039. [PMID: 12564871 DOI: 10.1109/tmi.2002.804438] [Citation(s) in RCA: 59] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Activation time (AT) imaging from electrocardiographic (ECG) mapping data has been developing for several years. By coupling ECG mapping and three-dimensional (3-D) + time anatomical data, the electrical excitation sequence can be imaged completely noninvasively in the human heart. In this paper, a bidomain theory-based surface heart model AT imaging approach was applied to single-beat data of atrial and ventricular depolarization in two patients with structurally normal hearts. In both patients, the AT map was reconstructed from sinus and paced rhythm data. Pacing sites were the apex of the right ventricle and the coronary sinus (CS) ostium. For CS pacing, the reconstructed AT pattern on the endocardium of the right atrium was compared with the CARTO map in both patients. The localization errors of the origins of the initial endocardial breakthroughs were determined to be 6 and 12 mm. The sites of early activation and the areas with late activation were estimated with sufficient accuracy. The reconstructed sinus rhythm sequence was in good qualitative agreement with the pattern previously published for the isolated Langendorff-perfused human heart.
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Affiliation(s)
- Bernhard Tilg
- Institute of Biomedical Engineering, Graz University of Technology.
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