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Serinagaoglu Dogrusoz Y, Bear LR, Bergquist JA, Rababah AS, Good W, Stoks J, Svehlikova J, van Dam E, Brooks DH, MacLeod RS. Evaluation of five methods for the interpolation of bad leads in the solution of the inverse electrocardiography problem. Physiol Meas 2024; 45:095012. [PMID: 39197474 DOI: 10.1088/1361-6579/ad74d6] [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] [Received: 03/06/2024] [Accepted: 08/28/2024] [Indexed: 09/01/2024]
Abstract
Objective.This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.Approach.We utilized experimental data from two distinct centers. Langendorff-perfused pig (n= 2) and dog (n= 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.Main results.The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.Significance.This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.
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Affiliation(s)
- Y Serinagaoglu Dogrusoz
- Middle East Technical University, Department of Electrical and Electronics Engineering, Ankara, Turkey
| | - L R Bear
- IHU-LIRYC, Fondation Bordeaux Université, Pessac, France
- Univ. Bordeaux, CRCTB, U1045 Bordeaux, France
- INSERM, Centre de recherche Cardio-Thoracique de Bordeaux, U1045 Bordeaux, France
| | - J A Bergquist
- Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), University of Utah, Salt Lake City, UT, United States of America
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America
| | - A S Rababah
- Jordanian Royal Medical Services, Amman, Jordan
| | - W Good
- Acutus Medical, Carlsbad, CA, United States of America
| | - J Stoks
- Cardiovascular Research Institute Maastricht, Maastricht University, Maastricht, The Netherlands
| | - J Svehlikova
- Slovak Academy of Sciences, Institute of Measurement Science, Bratislava, Slovakia
| | | | - D H Brooks
- Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, United States of America
| | - R S MacLeod
- Scientific Computing and Imaging (SCI) Institute, University of Utah, Salt Lake City, UT, United States of America
- Nora Eccles Harrison Cardiovascular Research and Training Institute (CVRTI), University of Utah, Salt Lake City, UT, United States of America
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, United States of America
- School of Medicine, University of Utah, Salt Lake City, UT, United States of America
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Molero R, Martínez-Pérez M, Herrero-Martín C, Reventós-Presmanes J, Roca-Luque I, Mont L, Climent AM, Guillem MS. Improving electrocardiographic imaging solutions: A comprehensive study on regularization parameter selection in L-curve optimization in the Atria. Comput Biol Med 2024; 182:109141. [PMID: 39293337 DOI: 10.1016/j.compbiomed.2024.109141] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 09/06/2024] [Accepted: 09/08/2024] [Indexed: 09/20/2024]
Abstract
BACKGROUND In electrocardiographic imaging (ECGI), selecting an optimal regularization parameter (λ) is crucial for obtaining accurate inverse electrograms. The effects of signal and geometry uncertainties on the inverse problem regularization have not been thoroughly quantified, and there is no established methodology to identify when λ is sub-optimal due to these uncertainties. This study introduces a novel approach to λ selection using Tikhonov regularization and L-curve optimization, specifically addressing the impact of electrical noise in body surface potential map (BSPM) signals and geometrical inaccuracies in the cardiac mesh. METHODS Nineteen atrial simulations (5 of regular rhythms and 14 of atrial fibrillation) ensuring variability in substrate complexity and activation patterns were used for computing the ECGI with added white Gaussian noise from 40 dB to -3dB. Cardiac mesh displacements (1-3 cm) were applied to simulate the uncertainty of atrial positioning and study its impact on the L-curve shape. The regularization parameter, the maximum curvature, and the most horizontal angle of the L-curve (β) were quantified. In addition, BSPM signals from real patients were used to validate our findings. RESULTS The maximum curvature of the L-curve was found to be inversely related to signal-to-noise ratio and atrial positioning errors. In contrast, the β angle is directly related to electrical noise and remains unaffected by geometrical errors. Our proposed adjustment of λ, based on the β angle, provides a more reliable ECGI solution than traditional corner-based methods. Our findings have been validated with simulations and real patient data, demonstrating practical applicability. CONCLUSION Adjusting λ based on the amount of noise in the data (or on the β angle) allows finding optimal ECGI solutions than a λ purely found at the corner of the L-curve. It was observed that the relevant information in ECGI activation maps is preserved even under the presence of uncertainties when the regularization parameter is correctly selected. The proposed criteria for regularization parameter selection have the potential to enhance the accuracy and reliability of ECGI solutions.
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Affiliation(s)
- Rubén Molero
- COR Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain; Corify Care SL, Madrid, Spain.
| | - Marta Martínez-Pérez
- COR Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain
| | - Clara Herrero-Martín
- COR Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain
| | - Jana Reventós-Presmanes
- Arrhythmia Section, Cardiology Department, Hospital Clínic, Universitat de Barcelona, Barcelona, Catalonia, Spain; Corify Care SL, Madrid, Spain
| | - Ivo Roca-Luque
- Arrhythmia Section, Cardiology Department, Hospital Clínic, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Lluis Mont
- Arrhythmia Section, Cardiology Department, Hospital Clínic, Universitat de Barcelona, Barcelona, Catalonia, Spain
| | - Andreu M Climent
- COR Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain; Corify Care SL, Madrid, Spain
| | - María S Guillem
- COR Group, ITACA Institute, Universitat Politècnica de València, Valencia, Spain; Corify Care SL, Madrid, Spain
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Li L. Toward Enabling Cardiac Digital Twins of Myocardial Infarction Using Deep Computational Models for Inverse Inference. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:2466-2478. [PMID: 38373128 PMCID: PMC7616288 DOI: 10.1109/tmi.2024.3367409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/21/2024]
Abstract
Cardiac digital twins (CDTs) have the potential to offer individualized evaluation of cardiac function in a non-invasive manner, making them a promising approach for personalized diagnosis and treatment planning of myocardial infarction (MI). The inference of accurate myocardial tissue properties is crucial in creating a reliable CDT of MI. In this work, we investigate the feasibility of inferring myocardial tissue properties from the electrocardiogram (ECG) within a CDT platform. The platform integrates multi-modal data, such as cardiac MRI and ECG, to enhance the accuracy and reliability of the inferred tissue properties. We perform a sensitivity analysis based on computer simulations, systematically exploring the effects of infarct location, size, degree of transmurality, and electrical activity alteration on the simulated QRS complex of ECG, to establish the limits of the approach. We subsequently present a novel deep computational model, comprising a dual-branch variational autoencoder and an inference model, to infer infarct location and distribution from the simulated QRS. The proposed model achieves mean Dice scores of 0.457 ±0.317 and 0.302 ±0.273 for the inference of left ventricle scars and border zone, respectively. The sensitivity analysis enhances our understanding of the complex relationship between infarct characteristics and electrophysiological features. The in silico experimental results show that the model can effectively capture the relationship for the inverse inference, with promising potential for clinical application in the future. The code is available at https://github.com/lileitech/MI_inverse_inference.
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Affiliation(s)
- Lei Li
- Department of Engineering Science, Institute of Biomedical
Engineering, University of Oxford, OX3 7DQ,
Oxford, U.K.
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常 益, 董 明, 王 彬, 范 力. [Developments of ex vivo cardiac electrical mapping and intelligent labeling of atrial fibrillation substrates]. SHENG WU YI XUE GONG CHENG XUE ZA ZHI = JOURNAL OF BIOMEDICAL ENGINEERING = SHENGWU YIXUE GONGCHENGXUE ZAZHI 2024; 41:184-190. [PMID: 38403620 PMCID: PMC10894749 DOI: 10.7507/1001-5515.202211046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Revised: 10/13/2023] [Indexed: 02/27/2024]
Abstract
Cardiac three-dimensional electrophysiological labeling technology is the prerequisite and foundation of atrial fibrillation (AF) ablation surgery, and invasive labeling is the current clinical method, but there are many shortcomings such as large trauma, long procedure duration, and low success rate. In recent years, because of its non-invasive and convenient characteristics, ex vivo labeling has become a new direction for the development of electrophysiological labeling technology. With the rapid development of computer hardware and software as well as the accumulation of clinical database, the application of deep learning technology in electrocardiogram (ECG) data is becoming more extensive and has made great progress, which provides new ideas for the research of ex vivo cardiac mapping and intelligent labeling of AF substrates. This paper reviewed the research progress in the fields of ECG forward problem, ECG inverse problem, and the application of deep learning in AF labeling, discussed the problems of ex vivo intelligent labeling of AF substrates and the possible approaches to solve them, prospected the challenges and future directions for ex vivo cardiac electrophysiology labeling.
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Affiliation(s)
- 益 常
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - 明 董
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - 彬 王
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
| | - 力宏 范
- 西安交通大学 电工材料电气绝缘国家重点实验室(西安 710049)State Key Library of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, P. R. China
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Manche M, El Houari K, Kachenoura A, Albera L, Rochette M, Hernández A, Moussaoui S. A reduced complexity ECG imaging model for regularized inversion optimization. Comput Biol Med 2023; 167:107698. [PMID: 37956624 DOI: 10.1016/j.compbiomed.2023.107698] [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] [Received: 06/02/2023] [Revised: 10/27/2023] [Accepted: 11/07/2023] [Indexed: 11/15/2023]
Abstract
The resolution of the inverse problem of electrocardiography represents a major interest in the diagnosis and catheter-based therapy of cardiac arrhythmia. In this context, the ability to simulate several cardiac electrical behaviors was crucial for evaluating and comparing the performance of inversion methods. For this application, existing models are either too complex or do not produce realistic cardiac patterns. In this work, a low-resolution heart-torso model generating realistic whole heart cardiac mappings and electrocardiograms in healthy and pathological cases is designed. This model was built upon a simplified heart-torso geometry and implements the monodomain formalism by using the finite element method. In addition, a model reduction step through a sensitivity analysis was proposed where parameters were identified using an evolutionary optimization approach. Finally, the study illustrates the usefulness of the proposed model by comparing the performance of different variants of Tikhonov-based inversion methods for the determination of the regularization parameter in healthy, ischemic and ventricular tachycardia scenarios. First, results of the sensitivity analysis show that among 58 parameters only 25 are influent. Note also that the level of influence of the parameters depends on the heart region. Besides, the synthesized electrocardiograms globally present the same characteristic shape compared to the reference once with a correlation value that reaches 88%. Regarding inverse problem, results highlight that only Robust Generalized Cross Validation and Discrepancy Principle provide best performance, with a quasi-perfect success rate for both, and a respective relative error, between the generated electrocardiograms to the reference one, of 0.75 and 0.62.
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Affiliation(s)
- Maureen Manche
- University of Rennes (LTSI), Inserm - UMR 1099, Rennes, 35000, France; Nantes Université, Ecole Centrale Nantes, LS2N UMR CNRS 6004, Nantes, 44000, France
| | | | - Amar Kachenoura
- University of Rennes (LTSI), Inserm - UMR 1099, Rennes, 35000, France.
| | - Laurent Albera
- University of Rennes (LTSI), Inserm - UMR 1099, Rennes, 35000, France
| | | | - Alfredo Hernández
- University of Rennes (LTSI), Inserm - UMR 1099, Rennes, 35000, France
| | - Saïd Moussaoui
- Nantes Université, Ecole Centrale Nantes, LS2N UMR CNRS 6004, Nantes, 44000, France
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Yadan Z, Jian L, Jian W, Yifu L, Haiying L, Hairui L. An expert review of the inverse problem in electrocardiographic imaging for the non-invasive identification of atrial fibrillation drivers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107676. [PMID: 37343376 DOI: 10.1016/j.cmpb.2023.107676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 06/06/2023] [Accepted: 06/09/2023] [Indexed: 06/23/2023]
Abstract
BACKGROUND AND OBJECTIVE Electrocardiographic imaging (ECGI) has emerged as a non-invasive approach to identify atrial fibrillation (AF) driver sources. This paper aims to collect and review the current research literature on the ECGI inverse problem, summarize the research progress, and propose potential research directions for the future. METHODS AND RESULTS The effectiveness and feasibility of using ECGI to map AF driver sources may be influenced by several factors, such as inaccuracies in the atrial model due to heart movement or deformation, noise interference in high-density body surface potential (BSP), inconvenient and time-consuming BSP acquisition, errors in solving the inverse problem, and incomplete interpretation of the AF driving source information derived from the reconstructed epicardial potential. We review the current research progress on these factors and discuss possible improvement directions. Additionally, we highlight the limitations of ECGI itself, including the lack of a gold standard to validate the accuracy of ECGI technology in locating AF drivers and the challenges associated with guiding AF ablation based on post-processed epicardial potentials due to the intrinsic difference between epicardial and endocardial potentials. CONCLUSIONS Before performing ablation, ECGI can provide operators with predictive information about the underlying locations of AF driver by non-invasively and globally mapping the biatrial electrical activity. In the future, endocardial catheter mapping technology may benefit from the use of ECGI to enhance the diagnosis and ablation of AF.
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Affiliation(s)
- Zhang Yadan
- Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China
| | - Liang Jian
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China
| | - Wu Jian
- Institute of Biomedical Engineering, Shenzhen International Graduate School, Tsinghua University, Shenzhen, Guangdong, China.
| | - Li Yifu
- Fuwai Hospital Chinese Academy of Medical Sciences, Shenzhen, Guangdong, China
| | - Li Haiying
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
| | - Li Hairui
- The University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong, China
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Wang T, Karel J, Bonizzi P, Peeters RLM. Influence of the Tikhonov Regularization Parameter on the Accuracy of the Inverse Problem in Electrocardiography. SENSORS (BASEL, SWITZERLAND) 2023; 23:1841. [PMID: 36850438 PMCID: PMC9964356 DOI: 10.3390/s23041841] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 02/04/2023] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The electrocardiogram (ECG) is the standard method in clinical practice to non-invasively analyze the electrical activity of the heart, from electrodes placed on the body's surface. The ECG can provide a cardiologist with relevant information to assess the condition of the heart and the possible presence of cardiac pathology. Nonetheless, the global view of the heart's electrical activity given by the ECG cannot provide fully detailed and localized information about abnormal electrical propagation patterns and corresponding substrates on the surface of the heart. Electrocardiographic imaging, also known as the inverse problem in electrocardiography, tries to overcome these limitations by non-invasively reconstructing the heart surface potentials, starting from the corresponding body surface potentials, and the geometry of the torso and the heart. This problem is ill-posed, and regularization techniques are needed to achieve a stable and accurate solution. The standard approach is to use zero-order Tikhonov regularization and the L-curve approach to choose the optimal value for the regularization parameter. However, different methods have been proposed for computing the optimal value of the regularization parameter. Moreover, regardless of the estimation method used, this may still lead to over-regularization or under-regularization. In order to gain a better understanding of the effects of the choice of regularization parameter value, in this study, we first focused on the regularization parameter itself, and investigated its influence on the accuracy of the reconstruction of heart surface potentials, by assessing the reconstruction accuracy with high-precision simultaneous heart and torso recordings from four dogs. For this, we analyzed a sufficiently large range of parameter values. Secondly, we evaluated the performance of five different methods for the estimation of the regularization parameter, also in view of the results of the first analysis. Thirdly, we investigated the effect of using a fixed value of the regularization parameter across all reconstructed beats. Accuracy was measured in terms of the quality of reconstruction of the heart surface potentials and estimation of the activation and recovery times, when compared with ground truth recordings from the experimental dog data. Results show that values of the regularization parameter in the range (0.01-0.03) provide the best accuracy, and that the three best-performing estimation methods (L-Curve, Zero-Crossing, and CRESO) give values in this range. Moreover, a fixed value of the regularization parameter could achieve very similar performance to the beat-specific parameter values calculated by the different estimation methods. These findings are relevant as they suggest that regularization parameter estimation methods may provide the accurate reconstruction of heart surface potentials only for specific ranges of regularization parameter values, and that using a fixed value of the regularization parameter may represent a valid alternative, especially when computational efficiency or consistency across time is required.
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8
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MUSIC: Cardiac Imaging, Modelling and Visualisation Software for Diagnosis and Therapy. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12126145] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
The tremendous advancement of cardiac imaging methods, the substantial progress in predictive modelling, along with the amount of new investigative multimodalities, challenge the current technologies in the cardiology field. Innovative, robust and multimodal tools need to be created in order to fuse imaging data (e.g., MR, CT) with mapped electrical activity and to integrate those into 3D biophysical models. In the past years, several cross-platform toolkits have been developed to provide image analysis tools to help build such software. The aim of this study is to introduce a novel multimodality software platform dedicated to cardiovascular diagnosis and therapy guidance: MUSIC. This platform was created to improve the image-guided cardiovascular interventional procedures and is a robust platform for AI/Deep Learning, image analysis and modelling in a newly created consortium with international hospitals. It also helps our researchers develop new techniques and have a better understanding of the cardiac tissue properties and physiological signals. Thus, this extraction of quantitative information from medical data leads to more repeatable and reliable medical diagnoses.
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Graham AJ, Orini M, Zacur E, Dhillon G, Jones D, Prabhu S, Pugliese F, Lowe M, Ahsan S, Earley MJ, Chow A, Sporton S, Dhinoja M, Hunter RJ, Schilling RJ, Lambiase PD. Assessing Noninvasive Delineation of Low-Voltage Zones Using ECG Imaging in Patients With Structural Heart Disease. JACC Clin Electrophysiol 2022; 8:426-436. [PMID: 35450597 DOI: 10.1016/j.jacep.2021.11.011] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 11/12/2021] [Accepted: 11/16/2021] [Indexed: 11/16/2022]
Abstract
OBJECTIVES This study sought to assess the association between electrocardiographic imaging (ECGI) parameters and voltage from simultaneous electroanatomic mapping (EAM). BACKGROUND ECGI offers noninvasive assessment of electrophysiologic features relevant for mapping ventricular arrhythmia and its substrate, but the accuracy of ECGI in the delineation of scar is unclear. METHODS Sixteen patients with structural heart disease underwent simultaneous ECGI (CardioInsight, Medtronic) and contact EAM (CARTO, Biosense-Webster) during ventricular tachycardia catheter ablation, with 7 mapped epicardially. ECGI and EAM geometries were coregistered using anatomic landmarks. ECGI points were paired to the closest site on the EAM within 10 mm. The association between EAM voltage and ECGI features from reconstructed epicardial unipolar electrograms was assessed by mixed-effects regression models. The classification of low-voltage regions was performed using receiver-operating characteristic analysis. RESULTS A total of 9,541 ECGI points (median: 596; interquartile range: 377-737 across patients) were paired to an EAM site. Epicardial EAM voltage was associated with ECGI features of signal fractionation and local repolarization dispersion (N = 7; P < 0.05), but they poorly classified sites with bipolar voltage of <1.5 mV or <0.5 mV thresholds (median area under the curve across patients: 0.50-0.62). No association was found between bipolar EAM voltage and low-amplitude reconstructed epicardial unipolar electrograms or ECGI-derived bipolar electrograms. Similar results were found in the combined cohort (n = 16), including endocardial EAM voltage compared to epicardial ECGI features (n = 9). CONCLUSIONS Despite a statistically significant association between ECGI features and EAM voltage, the accuracy of the delineation of low-voltage zones was modest. This may limit ECGI use for pr-procedural substrate analysis in ventricular tachycardia ablation, but it could provide value in risk assessment for ventricular arrhythmias.
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Affiliation(s)
- Adam J Graham
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Michele Orini
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom
| | - Ernesto Zacur
- Institute of Biomedical Engineering, University of Oxford, Oxford, United Kingdom
| | - Gurpreet Dhillon
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Daniel Jones
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Sandeep Prabhu
- Department of Cardiology, The Alfred Hospital, Melbourne, Australia
| | - Francesca Pugliese
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Martin Lowe
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Syed Ahsan
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Mark J Earley
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Anthony Chow
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Simon Sporton
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Mehul Dhinoja
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Ross J Hunter
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Richard J Schilling
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom
| | - Pier D Lambiase
- Barts Heart Centre, Barts Health National Health Service Trust, London, United Kingdom; Institute of Cardiovascular Science, University College London, London, United Kingdom.
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Melgarejo-Meseguer FM, Everss-Villalba E, Gutierrez-Fernandez-Calvillo M, Munoz-Romero S, Gimeno-Blanes FJ, Garcia-Alberola A, Rojo-Alvarez JL. Generalization and Regularization for Inverse Cardiac Estimators. IEEE Trans Biomed Eng 2022; 69:3029-3038. [PMID: 35294340 DOI: 10.1109/tbme.2022.3159733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECGI.
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11
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Gander L, Krause R, Multerer M, Pezzuto S. Space-time shape uncertainties in the forward and inverse problem of electrocardiography. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3522. [PMID: 34410040 PMCID: PMC9285968 DOI: 10.1002/cnm.3522] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/28/2020] [Revised: 06/27/2021] [Accepted: 08/13/2021] [Indexed: 06/08/2023]
Abstract
In electrocardiography, the "classic" inverse problem is the reconstruction of electric potentials at a surface enclosing the heart from remote recordings at the body surface and an accurate description of the anatomy. The latter being affected by noise and obtained with limited resolution due to clinical constraints, a possibly large uncertainty may be perpetuated in the inverse reconstruction. The purpose of this work is to study the effect of shape uncertainty on the forward and the inverse problem of electrocardiography. To this aim, the problem is first recast into a boundary integral formulation and then discretised with a collocation method to achieve high convergence rates and a fast time to solution. The shape uncertainty of the domain is represented by a random deformation field defined on a reference configuration. We propose a periodic-in-time covariance kernel for the random field and approximate the Karhunen-Loève expansion using low-rank techniques for fast sampling. The space-time uncertainty in the expected potential and its variance is evaluated with an anisotropic sparse quadrature approach and validated by a quasi-Monte Carlo method. We present several numerical experiments on a simplified but physiologically grounded two-dimensional geometry to illustrate the validity of the approach. The tested parametric dimension ranged from 100 up to 600. For the forward problem, the sparse quadrature is very effective. In the inverse problem, the sparse quadrature and the quasi-Monte Carlo method perform as expected, except for the total variation regularisation, where convergence is limited by lack of regularity. We finally investigate an H1/2 regularisation, which naturally stems from the boundary integral formulation, and compare it to more classical approaches.
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Affiliation(s)
- Lia Gander
- Center for Computational Medicine in CardiologyEuler Institute, Università della Svizzera italianaLuganoSwitzerland
| | - Rolf Krause
- Center for Computational Medicine in CardiologyEuler Institute, Università della Svizzera italianaLuganoSwitzerland
| | - Michael Multerer
- Center for Computational Medicine in CardiologyEuler Institute, Università della Svizzera italianaLuganoSwitzerland
| | - Simone Pezzuto
- Center for Computational Medicine in CardiologyEuler Institute, Università della Svizzera italianaLuganoSwitzerland
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12
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Karoui A, Bendahmane M, Zemzemi N. Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods. Front Physiol 2021; 12:686136. [PMID: 34512373 PMCID: PMC8428526 DOI: 10.3389/fphys.2021.686136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2021] [Accepted: 07/19/2021] [Indexed: 01/29/2023] Open
Abstract
One of the essential diagnostic tools of cardiac arrhythmia is activation mapping. Noninvasive current mapping procedures include electrocardiographic imaging. It allows reconstructing heart surface potentials from measured body surface potentials. Then, activation maps are generated using the heart surface potentials. Recently, a study suggests to deploy artificial neural networks to estimate activation maps directly from body surface potential measurements. Here we carry out a comparative study between the data-driven approach DirectMap and noninvasive classic technique based on reconstructed heart surface potentials using both Finite element method combined with L1-norm regularization (FEM-L1) and the spatial adaptation of Time-delay neural networks (SATDNN-AT). In this work, we assess the performance of the three approaches using a synthetic single paced-rhythm dataset generated on the atria surface. The results show that data-driven approach DirectMap quantitatively outperforms the two other methods. In fact, we observe an absolute activation time error and a correlation coefficient, respectively, equal to 7.20 ms, 93.2% using DirectMap, 14.60 ms, 76.2% using FEM-L1 and 13.58 ms, 79.6% using SATDNN-AT. In addition, results show that data-driven approaches (DirectMap and SATDNN-AT) are strongly robust against additive gaussian noise compared to FEM-L1.
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Affiliation(s)
- Amel Karoui
- Institute of Mathematics, University of Bordeaux, Bordeaux, France
- INRIA Bordeaux Sud-Ouest, Bordeaux, France
- IHU-Liryc, Bordeaux, France
| | - Mostafa Bendahmane
- Institute of Mathematics, University of Bordeaux, Bordeaux, France
- INRIA Bordeaux Sud-Ouest, Bordeaux, France
| | - Nejib Zemzemi
- Institute of Mathematics, University of Bordeaux, Bordeaux, France
- INRIA Bordeaux Sud-Ouest, Bordeaux, France
- IHU-Liryc, Bordeaux, France
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13
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Peng T, Malik A, Bear LR, Trew ML. Impulse Data Model For Solving The Inverse Problem of Electrocardiography. IEEE J Biomed Health Inform 2021; 26:1353-1361. [PMID: 34428164 DOI: 10.1109/jbhi.2021.3106645] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE To develop, train and test neural networks for predicting heart surface potentials (HSPs) from body surface potentials (BSPs). The method re-frames traditional inverse problems of electrocardiograpy into regression problems, constraining the solution space by decomposing signals with multidimensional Gaussian impulse basis functions. METHODS Impulse HSPs were generated with single Gaussian basis functions at discrete heart surface locations and projected to corresponding BSPs using a volume conductor torso model. Both BSP (inputs) and HSP (outputs) were mapped to regular 2D surface meshes and used to train a neural network. Predictive capabilities of the network were tested with unseen synthetic and experimental data. RESULTS A dense full connected single hidden layer neural network was trained to map body surface impulses to heart surface Gaussian basis functions for reconstructing HSP. Synthetic pulses moving across the heart surface were predicted from the neural network with root mean squared error of 9.1 +/ 1.4%. Predicted signals were robust to noise up to 20 dB and errors due to displacement and rotation of the heart within the torso were bounded and predictable. A shift of the heart 40 mm toward the spine resulted in a 4% increase in signal feature localization error. The set of training impulse function data could be reduced and prediction error remained bounded. Recorded HSPs from in-vitro pig hearts were reliably decomposed using space-time Gaussian basis functions. Activation times calculated from predicted HSPs for left-ventricular pacing had a mean absolute error of 10.4 +/ 11.4 ms. Other pacing scenarios were analyzed with similar success. CONCLUSION Impulses from Gaussian basis functions are potentially an effective and robust way to train simple neural network data models for reconstructing HSPs from decomposed BSPs. SIGNIFICANCE The HSPs predicted by the neural network can be used to generate activation maps that non-invasively identify features of cardiac electrical dysfunction and can guide subsequent treatment options.
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14
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Bacoyannis T, Ly B, Cedilnik N, Cochet H, Sermesant M. Deep learning formulation of electrocardiographic imaging integrating image and signal information with data-driven regularization. Europace 2021; 23:i55-i62. [PMID: 33751073 DOI: 10.1093/europace/euaa391] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 12/07/2020] [Indexed: 12/22/2022] Open
Abstract
AIMS Electrocardiographic imaging (ECGI) is a promising tool to map the electrical activity of the heart non-invasively using body surface potentials (BSP). However, it is still challenging due to the mathematically ill-posed nature of the inverse problem to solve. Novel approaches leveraging progress in artificial intelligence could alleviate these difficulties. METHODS AND RESULTS We propose a deep learning (DL) formulation of ECGI in order to learn the statistical relation between BSP and cardiac activation. The presented method is based on Conditional Variational AutoEncoders using deep generative neural networks. To quantify the accuracy of this method, we simulated activation maps and BSP data on six cardiac anatomies.We evaluated our model by training it on five different cardiac anatomies (5000 activation maps) and by testing it on a new patient anatomy over 200 activation maps. Due to the probabilistic property of our method, we predicted 10 distinct activation maps for each BSP data. The proposed method is able to generate volumetric activation maps with a good accuracy on the simulated data: the mean absolute error is 9.40 ms with 2.16 ms standard deviation on this testing set. CONCLUSION The proposed formulation of ECGI enables to naturally include imaging information in the estimation of cardiac electrical activity from BSP. It naturally takes into account all the spatio-temporal correlations present in the data. We believe these features can help improve ECGI results.
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Affiliation(s)
- Tania Bacoyannis
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Buntheng Ly
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France
| | - Nicolas Cedilnik
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France.,IHU Liryc, University of Bordeaux, Bordeaux, France
| | | | - Maxime Sermesant
- Inria, Université Côte d'Azur, Epione team, Sophia Antipolis, France.,IHU Liryc, University of Bordeaux, Bordeaux, France
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15
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Laurenti MC, Matveyenko A, Vella A. Measurement of Pulsatile Insulin Secretion: Rationale and Methodology. Metabolites 2021; 11:409. [PMID: 34206296 PMCID: PMC8305896 DOI: 10.3390/metabo11070409] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Revised: 06/17/2021] [Accepted: 06/18/2021] [Indexed: 12/29/2022] Open
Abstract
Pancreatic β-cells are responsible for the synthesis and exocytosis of insulin in response to an increase in circulating glucose. Insulin secretion occurs in a pulsatile manner, with oscillatory pulses superimposed on a basal secretion rate. Insulin pulses are a marker of β-cell health, and secretory parameters, such as pulse amplitude, time interval and frequency distribution, are impaired in obesity, aging and type 2 diabetes. In this review, we detail the mechanisms of insulin production and β-cell synchronization that regulate pulsatile insulin secretion, and we discuss the challenges to consider when measuring fast oscillatory secretion in vivo. These include the anatomical difficulties of measuring portal vein insulin noninvasively in humans before the hormone is extracted by the liver and quickly removed from the circulation. Peripheral concentrations of insulin or C-peptide, a peptide cosecreted with insulin, can be used to estimate their secretion profile, but mathematical deconvolution is required. Parametric and nonparametric approaches to the deconvolution problem are evaluated, alongside the assumptions and trade-offs required for their application in the quantification of unknown insulin secretory rates from known peripheral concentrations. Finally, we discuss the therapeutical implication of targeting impaired pulsatile secretion and its diagnostic value as an early indicator of β-cell stress.
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Affiliation(s)
- Marcello C. Laurenti
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN 55905, USA; (M.C.L.); (A.M.)
- Biomedical Engineering and Physiology Graduate Program, Mayo Clinic Graduate School of Biomedical Sciences, Rochester, MN 55905, USA
| | - Aleksey Matveyenko
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN 55905, USA; (M.C.L.); (A.M.)
| | - Adrian Vella
- Division of Endocrinology, Diabetes & Metabolism, Mayo Clinic, Rochester, MN 55905, USA; (M.C.L.); (A.M.)
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16
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Pereira H, Niederer S, Rinaldi CA. Electrocardiographic imaging for cardiac arrhythmias and resynchronization therapy. Europace 2020; 22:euaa165. [PMID: 32754737 PMCID: PMC7544539 DOI: 10.1093/europace/euaa165] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2020] [Accepted: 05/25/2020] [Indexed: 12/12/2022] Open
Abstract
Use of the 12-lead electrocardiogram (ECG) is fundamental for the assessment of heart disease, including arrhythmias, but cannot always reveal the underlying mechanism or the location of the arrhythmia origin. Electrocardiographic imaging (ECGi) is a non-invasive multi-lead ECG-type imaging tool that enhances conventional 12-lead ECG. Although it is an established technology, its continuous development has been shown to assist in arrhythmic activation mapping and provide insights into the mechanism of cardiac resynchronization therapy (CRT). This review addresses the validity, reliability, and overall feasibility of ECGi for use in a diverse range of arrhythmias. A systematic search limited to full-text human studies published in peer-reviewed journals was performed through Medline via PubMed, using various combinations of three key concepts: ECGi, arrhythmia, and CRT. A total of 456 studies were screened through titles and abstracts. Ultimately, 42 studies were included for literature review. Evidence to date suggests that ECGi can be used to provide diagnostic insights regarding the mechanistic basis of arrhythmias and the location of arrhythmia origin. Furthermore, ECGi can yield valuable information to guide therapeutic decision-making, including during CRT. Several studies have used ECGi as a diagnostic tool for atrial and ventricular arrhythmias. More recently, studies have tested the value of this technique in predicting outcomes of CRT. As a non-invasive method for assessing cardiovascular disease, particularly arrhythmias, ECGi represents a significant advancement over standard procedures in contemporary cardiology. Its full potential has yet to be fully explored.
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Affiliation(s)
- Helder Pereira
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, 4th Floor, Lambeth Wing, St. Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
- Cardiac Physiology Services—Clinical Investigation Centre, Bupa Cromwell Hospital, London, UK
| | - Steven Niederer
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, 4th Floor, Lambeth Wing, St. Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
| | - Christopher A Rinaldi
- Division of Imaging Sciences and Biomedical Engineering, King’s College London, 4th Floor, Lambeth Wing, St. Thomas’ Hospital, Westminster Bridge Rd, London SE1 7EH, UK
- Cardiovascular Department, Guys and St Thomas NHS Foundation Trust, London, UK
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17
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Song LY, Lin Q, Li LB, Cheng X. Clinical exploration of marking targeting biopsy in the intraoperative localization value of colon polypectomy. Pak J Med Sci 2020; 36:100-104. [PMID: 32063940 PMCID: PMC6994903 DOI: 10.12669/pjms.36.2.756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Objective To evaluate the feasibility and safety of marking targeting biopsy (MTB) in the intraoperative localization value of colon polypectomy. Methods The clinical data from patients with polyp of colon discovered under colonoscopy from January 2014 to January 2016 were retrospectively analyzed. A total of 87 patients conformed to the inclusion criteria, among them, 43 received colonoscopic polypectomy one week after MTB (MTB group), while 44 underwent colonoscopic polypectomy one week after conventional biopsy (conventional group). The time consumption in colonoscopic treatment, polypectomy rate and postoperative complications between two groups were compared. Results The time consumed in operation in the MTB group was 25.5 (±8.6) minutes, while that in conventional group was 42.0 (±20.5) minutes, and the difference was statistically significant (P<0.01). There were a total of 86 polyps in the MTB group, among which 83 were removed, yielding the removal rate of 96.5%. There were altogether 88 polyps in the conventional group, among which 54 were removed, resulting in the removal rate of 61.4%, and the difference was statistically significant (P<0.05). three polyps in the MTB group were detached after MTB, or the wound surface became flat after gross polyp removal, and no polypectomy was required, but the marking targeting solution was clearly visible. two respective polyps in 12 cases in conventional group could not be found in colonoscopic treatment, and 10 of them had respective one polyp that could not be found again. 12 cases in MTB group suffered from abdominal pain after surgery, and no hemorrhage was seen intraoperatively and postoperatively. 10 cases in the conventional group had abdominal pain after surgery, and one case had delayed hemorrhage after surgery. The results between two groups displayed no statistical significance (P>0.05). Conclusions The localization value of MTB in colon polypectomy is definitely feasible, safe and effective, which can greatly shorten the time of endoscopic colon polypectomy, mitigate patient sufferings, and reduce the incidence of false negative rate of polyp. It displays favorable clinical application value and is worthy of being promoted in clinic.
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Affiliation(s)
- Ling Yun Song
- Dr. Ling Yun Song, M.D. Department of Gastroenterology, Yinzhou No 2. Hospital, Ningbo, Zhejiang, China
| | - Qi Lin
- Dr. Qi Lin, M.D, Department of Gastroenterology, Yinzhou No 2. Hospital, Ningbo, Zhejiang, China
| | - Lian Biao Li
- Dr. Lian-Biao Li, M.D, Department of Gastroenterology, Yinzhou No 2. Hospital, Ningbo, Zhejiang, China
| | - Xiu Cheng
- Dr. Xiu Cheng, M.D, Department of Gastroenterology, Yinzhou No 2. Hospital, Ningbo, Zhejiang, China
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18
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Bear LR, Bouhamama O, Cluitmans M, Duchateau J, Walton RD, Abell E, Belterman C, Haissaguerre M, Bernus O, Coronel R, Dubois R. Advantages and pitfalls of noninvasive electrocardiographic imaging. J Electrocardiol 2019; 57S:S15-S20. [PMID: 31477238 DOI: 10.1016/j.jelectrocard.2019.08.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 07/29/2019] [Accepted: 08/08/2019] [Indexed: 11/15/2022]
Abstract
BACKGROUND With increasing clinical use of Electrocardiographic Imaging (ECGI), it is imperative to understand the limits of this technique. The objective of this study is to evaluate a potential-based ECGI approach for activation and repolarization mapping in sinus rhythm. METHOD Langendorff-perfused pig hearts were suspended in a human-shaped torso tank. Electrograms were recorded with a 108-electrode sock and ECGs with 256 electrodes embedded in the tank surface. Left bundle branch block (LBBB) was developed in 4 hearts through ablation, and repolarization abnormalities in another 4 hearts through regional perfusion of dofetilide and pinacidil. Electrograms were noninvasively reconstructed and reconstructed activation and repolarization features were compared to those recorded. RESULTS Visual consistency between ECGI and recorded activation and repolarization maps was high. While reconstructed repolarization times showed significantly more error than activation times quantitatively, patterns were reconstructed with a similar level of accuracy. The number of epicardial breakthrough sites was underestimated by ECGI and these were misplaced (>20 mm) in location. Likewise, ECGI reconstructed activation maps demonstrated artificial lines of block resulting from a W-shaped QRS waveform that were not present in recorded maps. Nevertheless, ECGI allowed identification of regions of abnormal repolarization reasonably accurately in terms of size, location and timing. CONCLUSIONS This study validates a potential-based ECGI approach to noninvasively image activation and recovery in sinus rhythm. Despite inaccuracies in epicardial breakthroughs and lines of conduction block, other important clinical features such as regions of abnormal repolarization can be accurately derived making ECGI a valuable clinical tool.
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Affiliation(s)
- Laura R Bear
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France.
| | - Oumayma Bouhamama
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INRIA Bordeaux Sud-Ouest, Carmen team, Bordeaux, France
| | - Matthijs Cluitmans
- CARIM School for Cardiovascular Diseases, Maastricht UMC, Maastricht, Netherlands
| | - Josselin Duchateau
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; Bordeaux University Hospital (CHU), Electrophysiology and Ablation Unit, F-33600 Pessac, France
| | - Richard D Walton
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France
| | - Emma Abell
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France
| | - Charly Belterman
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Department of Experimental Cardiology, Academic Medical Center, the Netherlands
| | - Michel Haissaguerre
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; Bordeaux University Hospital (CHU), Electrophysiology and Ablation Unit, F-33600 Pessac, France
| | - Olivier Bernus
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France
| | - Ruben Coronel
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Department of Experimental Cardiology, Academic Medical Center, the Netherlands
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Pessac, Bordeaux, France; Université de Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France; INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, F-33000 Bordeaux, France
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