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Bukhari HA, Sánchez C, Laguna P, Potse M, Pueyo E. Differences in ventricular wall composition may explain inter-patient variability in the ECG response to variations in serum potassium and calcium. Front Physiol 2023; 14:1060919. [PMID: 37885805 PMCID: PMC10598848 DOI: 10.3389/fphys.2023.1060919] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 09/18/2023] [Indexed: 10/28/2023] Open
Abstract
Objective: Chronic kidney disease patients have a decreased ability to maintain normal electrolyte concentrations in their blood, which increases the risk for ventricular arrhythmias and sudden cardiac death. Non-invasive monitoring of serum potassium and calcium concentration, [K+] and [Ca2+], can help to prevent arrhythmias in these patients. Electrocardiogram (ECG) markers that significantly correlate with [K+] and [Ca2+] have been proposed, but these relations are highly variable between patients. We hypothesized that inter-individual differences in cell type distribution across the ventricular wall can help to explain this variability. Methods: A population of human heart-torso models were built with different proportions of endocardial, midmyocardial and epicardial cells. Propagation of ventricular electrical activity was described by a reaction-diffusion model, with modified Ten Tusscher-Panfilov dynamics. [K+] and [Ca2+] were varied individually and in combination. Twelve-lead ECGs were simulated and the width, amplitude and morphological variability of T waves and QRS complexes were quantified. Results were compared to measurements from 29 end-stage renal disease (ESRD) patients undergoing hemodialysis (HD). Results: Both simulations and patients data showed that most of the analyzed T wave and QRS complex markers correlated strongly with [K+] (absolute median Pearson correlation coefficients, r, ranging from 0.68 to 0.98) and [Ca2+] (ranging from 0.70 to 0.98). The same sign and similar magnitude of median r was observed in the simulations and the patients. Different cell type distributions in the ventricular wall led to variability in ECG markers that was accentuated at high [K+] and low [Ca2+], in agreement with the larger variability between patients measured at the onset of HD. The simulated ECG variability explained part of the measured inter-patient variability. Conclusion: Changes in ECG markers were similarly related to [K+] and [Ca2+] variations in our models and in the ESRD patients. The high inter-patient ECG variability may be explained by variations in cell type distribution across the ventricular wall, with high sensitivity to variations in the proportion of epicardial cells. Significance: Differences in ventricular wall composition help to explain inter-patient variability in ECG response to [K+] and [Ca2+]. This finding can be used to improve serum electrolyte monitoring in ESRD patients.
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Affiliation(s)
- Hassaan A. Bukhari
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
- Carmen Team, Inria Bordeaux—Sud-Ouest, Talence, France
- University of Bordeaux, IMB, UMR 5251, Talence, France
| | - Carlos Sánchez
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Pablo Laguna
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
| | - Mark Potse
- Carmen Team, Inria Bordeaux—Sud-Ouest, Talence, France
- University of Bordeaux, IMB, UMR 5251, Talence, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France
| | - Esther Pueyo
- BSICoS Group, I3A Institute, University of Zaragoza, IIS Aragón, Zaragoza, Spain
- CIBER en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Zaragoza, Spain
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Gander L, Pezzuto S, Gharaviri A, Krause R, Perdikaris P, Sahli Costabal F. Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification. Front Physiol 2022; 13:757159. [PMID: 35330935 PMCID: PMC8940533 DOI: 10.3389/fphys.2022.757159] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Accepted: 01/31/2022] [Indexed: 11/13/2022] Open
Abstract
Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.
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Affiliation(s)
- Lia Gander
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Ali Gharaviri
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Euler Institute, Università della Svizzera italiana, Lugano, Switzerland
| | - Paris Perdikaris
- Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, PA, United States
| | - Francisco Sahli Costabal
- Department of Mechanical and Metallurgical Engineering, School of Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile.,Institute for Biological and Medical Engineering, Schools of Engineering, Medicine and Biological Sciences, Pontificia Universidad Católica de Chile, Santiago, Chile.,Millennium Nucleus for Cardiovascular Magnetic Resonance, Santiago, Chile
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3
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Gharaviri A, Pezzuto S, Potse M, Conte G, Zeemering S, Sobota V, Verheule S, Krause R, Auricchio A, Schotten U. Synergistic antiarrhythmic effect of inward rectifier current inhibition and pulmonary vein isolation in a 3D computer model for atrial fibrillation. Europace 2021; 23:i161-i168. [PMID: 33751085 DOI: 10.1093/europace/euaa413] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 12/15/2020] [Indexed: 12/16/2022] Open
Abstract
AIMS Recent clinical studies showed that antiarrhythmic drug (AAD) treatment and pulmonary vein isolation (PVI) synergistically reduce atrial fibrillation (AF) recurrences after initially successful ablation. Among newly developed atrial-selective AADs, inhibitors of the G-protein-gated acetylcholine-activated inward rectifier current (IKACh) were shown to effectively suppress AF in an experimental model but have not yet been evaluated clinically. We tested in silico whether inhibition of inward rectifier current or its combination with PVI reduces AF inducibility. METHODS AND RESULTS We simulated the effect of inward rectifier current blockade (IK blockade), PVI, and their combination on AF inducibility in a detailed three-dimensional model of the human atria with different degrees of fibrosis. IK blockade was simulated with a 30% reduction of its conductivity. Atrial fibrillation was initiated using incremental pacing applied at 20 different locations, in both atria. IK blockade effectively prevented AF induction in simulations without fibrosis as did PVI in simulations without fibrosis and with moderate fibrosis. Both interventions lost their efficacy in severe fibrosis. The combination of IK blockade and PVI prevented AF in simulations without fibrosis, with moderate fibrosis, and even with severe fibrosis. The combined therapy strongly decreased the number of fibrillation waves, due to a synergistic reduction of wavefront generation rate while the wavefront lifespan remained unchanged. CONCLUSION Newly developed blockers of atrial-specific inward rectifier currents, such as IKAch, might prevent AF occurrences and when combined with PVI effectively supress AF recurrences in human.
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Affiliation(s)
- Ali Gharaviri
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Mark Potse
- Carmen Team, Inria Bordeaux-Sud-Ouest, Talence, France.,Université de Bordeaux, IMB, UMR 5251, F-33400, Talence, France.,IHU Liryc, Electrophysiology and Heart Modeling Institute, Foundation Bordeaux Université, Bordeaux, France
| | - Giulio Conte
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, 6900 Lugano, Switzerland
| | - Stef Zeemering
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Vladimír Sobota
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Sander Verheule
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland.,Division of Cardiology, Fondazione Cardiocentro Ticino, Via Tesserete 48, 6900 Lugano, Switzerland
| | - Ulrich Schotten
- Department of Physiology, Maastricht University, Maastricht, The Netherlands
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Abstract
Computer modeling of the electrophysiology of the heart has undergone significant progress. A healthy heart can be modeled starting from the ion channels via the spread of a depolarization wave on a realistic geometry of the human heart up to the potentials on the body surface and the ECG. Research is advancing regarding modeling diseases of the heart. This article reviews progress in calculating and analyzing the corresponding electrocardiogram (ECG) from simulated depolarization and repolarization waves. First, we describe modeling of the P-wave, the QRS complex and the T-wave of a healthy heart. Then, both the modeling and the corresponding ECGs of several important diseases and arrhythmias are delineated: ischemia and infarction, ectopic beats and extrasystoles, ventricular tachycardia, bundle branch blocks, atrial tachycardia, flutter and fibrillation, genetic diseases and channelopathies, imbalance of electrolytes and drug-induced changes. Finally, we outline the potential impact of computer modeling on ECG interpretation. Computer modeling can contribute to a better comprehension of the relation between features in the ECG and the underlying cardiac condition and disease. It can pave the way for a quantitative analysis of the ECG and can support the cardiologist in identifying events or non-invasively localizing diseased areas. Finally, it can deliver very large databases of reliably labeled ECGs as training data for machine learning.
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Gharaviri A, Bidar E, Potse M, Zeemering S, Verheule S, Pezzuto S, Krause R, Maessen JG, Auricchio A, Schotten U. Epicardial Fibrosis Explains Increased Endo-Epicardial Dissociation and Epicardial Breakthroughs in Human Atrial Fibrillation. Front Physiol 2020; 11:68. [PMID: 32153419 PMCID: PMC7047215 DOI: 10.3389/fphys.2020.00068] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2019] [Accepted: 01/21/2020] [Indexed: 01/22/2023] Open
Abstract
Background Atrial fibrillation (AF) is accompanied by progressive epicardial fibrosis, dissociation of electrical activity between the epicardial layer and the endocardial bundle network, and transmural conduction (breakthroughs). However, causal relationships between these phenomena have not been demonstrated yet. Our goal was to test the hypothesis that epicardial fibrosis suffices to increase endo–epicardial dissociation (EED) and breakthroughs (BT) during AF. Methods We simulated the effect of fibrosis in the epicardial layer on EED and BT in a detailed, high-resolution, three-dimensional model of the human atria with realistic electrophysiology. The model results were compared with simultaneous endo–epicardial mapping in human atria. The model geometry, specifically built for this study, was based on MR images and histo-anatomical studies. Clinical data were obtained in four patients with longstanding persistent AF (persAF) and three patients without a history of AF. Results The AF cycle length (AFCL), conduction velocity (CV), and EED were comparable in the mapping studies and the simulations. EED increased from 24.1 ± 3.4 to 56.58 ± 6.2% (p < 0.05), and number of BTs per cycle from 0.89 ± 0.55 to 6.74 ± 2.11% (p < 0.05), in different degrees of fibrosis in the epicardial layer. In both mapping data and simulations, EED correlated with prevalence of BTs. Fibrosis also increased the number of fibrillation waves per cycle in the model. Conclusion A realistic 3D computer model of AF in which epicardial fibrosis was increased, in the absence of other pathological changes, showed increases in EED and epicardial BT comparable to those in longstanding persAF. Thus, epicardial fibrosis can explain both phenomena.
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Affiliation(s)
- Ali Gharaviri
- Department of Physiology, Maastricht University, Maastricht, Netherlands.,Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera Italiana, Lugano, Switzerland
| | - Elham Bidar
- Maastricht University Medical Centre, Maastricht, Netherlands
| | - Mark Potse
- Inria Bordeaux - Sud-Ouest Research Centre, Talence, France.,IMB, UMR 5251, Université de Bordeaux, Talence, France.,IHU Liryc, Electrophysiology and Heart Modeling Institute, Foundation Bordeaux Université, Bordeaux, France
| | - Stef Zeemering
- Department of Physiology, Maastricht University, Maastricht, Netherlands
| | - Sander Verheule
- Department of Physiology, Maastricht University, Maastricht, Netherlands
| | - Simone Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera Italiana, Lugano, Switzerland
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera Italiana, Lugano, Switzerland
| | - Jos G Maessen
- Maastricht University Medical Centre, Maastricht, Netherlands
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera Italiana, Lugano, Switzerland.,Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Ulrich Schotten
- Department of Physiology, Maastricht University, Maastricht, Netherlands
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Pezzuto S, Gharaviri A, Schotten U, Potse M, Conte G, Caputo ML, Regoli F, Krause R, Auricchio A. Beat-to-beat P-wave morphological variability in patients with paroxysmal atrial fibrillation: anin silicostudy. Europace 2018; 20:iii26-iii35. [DOI: 10.1093/europace/euy227] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 09/19/2018] [Indexed: 11/12/2022] Open
Affiliation(s)
- Simone Pezzuto
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, CH-6904 Lugano, Switzerland
| | - Ali Gharaviri
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, CH-6904 Lugano, Switzerland
| | - Ulrich Schotten
- Department of Physiology, Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, the Netherlands
| | - Mark Potse
- CARMEN Research Team, INRIA, Talence, France
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Foundation Bordeaux Université, Pessac, France
- Univ. Bordeaux, IMB, UMR 5251, Talence, France
| | - Giulio Conte
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Maria Luce Caputo
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Francois Regoli
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - Rolf Krause
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Institute of Computational Science, Università della Svizzera italiana, Via Giuseppe Buffi 13, CH-6904 Lugano, Switzerland
| | - Angelo Auricchio
- Center for Computational Medicine in Cardiology, Università della Svizzera italiana, Lugano, Switzerland
- Fondazione Cardiocentro Ticino, Lugano, Switzerland
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7
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Quaglino A, Pezzuto S, Koutsourelakis PS, Auricchio A, Krause R. Fast uncertainty quantification of activation sequences in patient-specific cardiac electrophysiology meeting clinical time constraints. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2018; 34:e2985. [PMID: 29577657 DOI: 10.1002/cnm.2985] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 01/16/2018] [Accepted: 03/15/2018] [Indexed: 06/08/2023]
Abstract
We present a fast, patient-specific methodology for uncertainty quantification in electrophysiology, aimed at meeting the time constraints of clinical practitioners. We focus on computing the statistics of the activation map, given the uncertainties associated with the conductivity tensor modeling the fiber orientation in the heart. We use a fast parallel solution method implemented on a graphics processing unit for the eikonal approximation, in order to compute the activation map and to sample the random fiber field with correlation on the basis of geodesic distances. While this enables to perform uncertainty quantification studies with a manageable computational effort, the required time frame still exceeds clinically suitable time expectations. In order to reduce it further by 2 orders of magnitude, we rely on Bayesian multifidelity methods. In particular, we propose a low-fidelity model that is patient-specific and free from the additional training cost associated with reduced models. This is achieved by a sound physics-based simplification of the full eikonal model. The low-fidelity output is then corrected by the standard multifidelity framework. In practice, the complete procedure only requires approximately 100 new runs of our eikonal graphics processing unit solver for producing the sought estimates and their associated credible intervals, enabling a full online analysis in less than 5 minutes.
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Affiliation(s)
- A Quaglino
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | - S Pezzuto
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
| | | | - A Auricchio
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
- Division of Cardiology, Fondazione Cardiocentro Ticino, Lugano, Switzerland
| | - R Krause
- Center for Computational Medicine in Cardiology, Institute of Computational Science, Università della Svizzera italiana, Lugano, Switzerland
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Potse M. Scalable and Accurate ECG Simulation for Reaction-Diffusion Models of the Human Heart. Front Physiol 2018; 9:370. [PMID: 29731720 PMCID: PMC5920200 DOI: 10.3389/fphys.2018.00370] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Accepted: 03/27/2018] [Indexed: 11/13/2022] Open
Abstract
Realistic electrocardiogram (ECG) simulation with numerical models is important for research linking cellular and molecular physiology to clinically observable signals, and crucial for patient tailoring of numerical heart models. However, ECG simulation with a realistic torso model is computationally much harder than simulation of cardiac activity itself, so that many studies with sophisticated heart models have resorted to crude approximations of the ECG. This paper shows how the classical concept of electrocardiographic lead fields can be used for an ECG simulation method that matches the realism of modern heart models. The accuracy and resource requirements were compared to those of a full-torso solution for the potential and scaling was tested up to 14,336 cores with a heart model consisting of 11 million nodes. Reference ECGs were computed on a 3.3 billion-node heart-torso mesh at 0.2 mm resolution. The results show that the lead-field method is more efficient than a full-torso solution when the number of simulated samples is larger than the number of computed ECG leads. While the initial computation of the lead fields remains a hard and poorly scalable problem, the ECG computation itself scales almost perfectly and, even for several hundreds of ECG leads, takes much less time than the underlying simulation of cardiac activity.
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Affiliation(s)
- Mark Potse
- CARMEN Research Team, Inria Bordeaux Sud-Ouest, Talence, France.,Institut de Mathématiques de Bordeaux, UMR 5251, Université de Bordeaux, Talence, France.,IHU Liryc, Electrophysiology and Heart Modeling Institute, Foundation Bordeaux Université, Pessac-Bordeaux, France
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Duchateau J, Potse M, Dubois R. Spatially Coherent Activation Maps for Electrocardiographic Imaging. IEEE Trans Biomed Eng 2016; 64:1149-1156. [PMID: 27448338 DOI: 10.1109/tbme.2016.2593003] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Cardiac mapping is an important diagnostic step in cardiac electrophysiology. One of its purposes is to generate a map of the depolarization sequence. This map is constructed in clinical routine either by directly analyzing cardiac electrograms (EGMs) recorded invasively or an estimate of these EGMs obtained by a noninvasive technique. Activation maps based on noninvasively estimated EGMs often show artefactual jumps in activation times. To overcome this problem, we present a new method to construct the activation maps from reconstructed unipolar EGMs. METHODS On top of the standard estimation of local activation time from unipolar intrinsic deflections, we propose to mutually compare the EGMs in order to estimate the delays in activation for neighboring recording locations. We then describe a workflow to construct a spatially coherent activation map from local activation times and delay estimates in order to create more accurate maps. The method is optimized using simulated data and evaluated on clinical data from 12 different activation sequences. RESULTS We found that the standard methodology created lines of artificially strong activation time gradient. The proposed workflow enhanced these maps significantly. CONCLUSION Estimating delays between neighbors is an interesting option for activation map computation in electrocardiographic imaging.
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