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Trayanova NA, Lyon A, Shade J, Heijman J. Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation. Physiol Rev 2024; 104:1265-1333. [PMID: 38153307 DOI: 10.1152/physrev.00017.2023] [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: 04/05/2023] [Revised: 12/19/2023] [Accepted: 12/21/2023] [Indexed: 12/29/2023] Open
Abstract
The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.
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Affiliation(s)
- Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Aurore Lyon
- Department of Biomedical Engineering, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
- Division of Heart and Lungs, Department of Medical Physiology, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Julie Shade
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland, United States
| | - Jordi Heijman
- Department of Cardiology, CARIM School for Cardiovascular Diseases, Maastricht University, Maastricht, The Netherlands
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Vermoortele D, Amoni M, Ingelaere S, Sipido KR, Willems R, Claus P. Electric Field-Based Spatial Analysis of Noncontact Unipolar Electrograms to Map Regional Activation-Repolarization Intervals. JACC Clin Electrophysiol 2023; 9:1217-1231. [PMID: 37558285 DOI: 10.1016/j.jacep.2023.02.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2022] [Revised: 01/30/2023] [Accepted: 02/01/2023] [Indexed: 08/11/2023]
Abstract
BACKGROUND Spatial heterogeneity in repolarization plays an important role in generating and sustaining cardiac arrhythmias. Reliable determination of repolarization times remains challenging. OBJECTIVES The goal of this study was to improve processing of densely sampled noncontact unipolar electrograms to yield reliable high-resolution activation and repolarization maps. METHODS Endocardial noncontact unipolar electrograms were both simulated and recorded in pig left ventricle. Electrical activity on the endocardial surface was processed in terms of a pseudo-electric field. Activation and repolarization times were calculated by using an amplitude-weighted average on QRS and T waves (ie, the E-field method). This was compared vs the conventional Wyatt method on unipolar electrograms. Timing maps were validated against timing on endocardial action potentials in a simulation study. In vivo, activation and repolarization times determined by using this alternative E-field method were validated against simultaneously recorded endocardial monophasic action potentials (MAPs). RESULTS Simulation showed that the E-field method provides viable measurements of local endocardial action potential activation and repolarization times. In vivo, correlation of E-field activation times with MAP activation times (rE = 0.76; P < 0.001) was similar to those of Wyatt (rWyatt = 0.80, P < 0.001; P[h1:rE > rWyatt] = 0.82); for repolarization times, correlation improved significantly (rE = 0.96, P < 0.001; rWyatt = 0.82, P < 0.001; P[h1:rE > rWyatt] < 0.00001). This resulted in improved correlations of activation-repolarization intervals to endocardial action potential duration on MAP (rE = 0.96, P < 0.001; rWyatt = 0.86, P < 0.001; P[h1:rE > rWyatt] < 0.00001). Spatial beat-to-beat variation of repolarization could only be calculated by using the E-field methodology and correlated well with the MAP beat-to-beat variation of repolarization (rE = 0.76; P = 0.001). CONCLUSIONS The E-field method substantially enhances information from endocardial noncontact electrogram data, allowing for dense maps of activation and repolarization times and derived parameters.
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Affiliation(s)
- Dylan Vermoortele
- Department of Cardiovascular Sciences, Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium
| | - Matthew Amoni
- Department of Cardiovascular Sciences, Experimental Cardiology, KU Leuven, Leuven, Belgium
| | - Sebastian Ingelaere
- Department of Cardiovascular Sciences, Experimental Cardiology, KU Leuven, Leuven, Belgium; Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Karin R Sipido
- Department of Cardiovascular Sciences, Experimental Cardiology, KU Leuven, Leuven, Belgium
| | - Rik Willems
- Department of Cardiovascular Sciences, Experimental Cardiology, KU Leuven, Leuven, Belgium; Division of Cardiology, University Hospitals Leuven, Leuven, Belgium
| | - Piet Claus
- Department of Cardiovascular Sciences, Cardiovascular Imaging and Dynamics, KU Leuven, Leuven, Belgium.
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Sánchez J, Loewe A. A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms. Front Physiol 2022; 13:908069. [PMID: 35620600 PMCID: PMC9127661 DOI: 10.3389/fphys.2022.908069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 04/21/2022] [Indexed: 11/13/2022] Open
Abstract
Computational simulations of cardiac electrophysiology provide detailed information on the depolarization phenomena at different spatial and temporal scales. With the development of new hardware and software, in silico experiments have gained more importance in cardiac electrophysiology research. For plane waves in healthy tissue, in vivo and in silico electrograms at the surface of the tissue demonstrate symmetric morphology and high peak-to-peak amplitude. Simulations provided insight into the factors that alter the morphology and amplitude of the electrograms. The situation is more complex in remodeled tissue with fibrotic infiltrations. Clinically, different changes including fractionation of the signal, extended duration and reduced amplitude have been described. In silico, numerous approaches have been proposed to represent the pathological changes on different spatial and functional scales. Different modeling approaches can reproduce distinct subsets of the clinically observed electrogram phenomena. This review provides an overview of how different modeling approaches to incorporate fibrotic and structural remodeling affect the electrogram and highlights open challenges to be addressed in future research.
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Sung E, Etoz S, Zhang Y, Trayanova NA. Whole-heart ventricular arrhythmia modeling moving forward: Mechanistic insights and translational applications. BIOPHYSICS REVIEWS 2021; 2:031304. [PMID: 36281224 PMCID: PMC9588428 DOI: 10.1063/5.0058050] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Ventricular arrhythmias are the primary cause of sudden cardiac death and one of the leading causes of mortality worldwide. Whole-heart computational modeling offers a unique approach for studying ventricular arrhythmias, offering vast potential for developing both a mechanistic understanding of ventricular arrhythmias and clinical applications for treatment. In this review, the fundamentals of whole-heart ventricular modeling and current methods of personalizing models using clinical data are presented. From this foundation, the authors summarize recent advances in whole-heart ventricular arrhythmia modeling. Efforts in gaining mechanistic insights into ventricular arrhythmias are discussed, in addition to other applications of models such as the assessment of novel therapeutics. The review emphasizes the unique benefits of computational modeling that allow for insights that are not obtainable by contemporary experimental or clinical means. Additionally, the clinical impact of modeling is explored, demonstrating how patient care is influenced by the information gained from ventricular arrhythmia models. The authors conclude with future perspectives about the direction of whole-heart ventricular arrhythmia modeling, outlining how advances in neural network methodologies hold the potential to reduce computational expense and permit for efficient whole-heart modeling.
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Affiliation(s)
- Eric Sung
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Sevde Etoz
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Yingnan Zhang
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
| | - Natalia A. Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Alliance for Cardiovascular Diagnostic and Treatment Innovation, Johns Hopkins University, Baltimore, Maryland 21218, USA
- Author to whom correspondence should be addressed: . Tel.: 410-516-4375
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Sánchez J, Luongo G, Nothstein M, Unger LA, Saiz J, Trenor B, Luik A, Dössel O, Loewe A. Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset. Front Physiol 2021; 12:699291. [PMID: 34290623 PMCID: PMC8287829 DOI: 10.3389/fphys.2021.699291] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 11/15/2022] Open
Abstract
In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.
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Affiliation(s)
- Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Laura A. Unger
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
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Koneshloo A, Du D, Du Y. An Uncertainty Modeling Framework for Intracardiac Electrogram Analysis. Bioengineering (Basel) 2020; 7:E62. [PMID: 32604784 PMCID: PMC7355499 DOI: 10.3390/bioengineering7020062] [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] [Received: 05/25/2020] [Revised: 06/22/2020] [Accepted: 06/24/2020] [Indexed: 11/16/2022] Open
Abstract
Intracardiac electrograms (EGMs) are electrical signals measured within the chambers of the heart, which can be used to locate abnormal cardiac tissue and guide catheter ablations to treat cardiac arrhythmias. EGMs may contain large amounts of uncertainty and irregular variations, which pose significant challenges in data analysis. This study aims to introduce a statistical approach to account for the data uncertainty while analyzing EGMs for abnormal electrical impulse identification. The activation order of catheter sensors was modeled with a multinomial distribution, and maximum likelihood estimations were done to track the electrical wave conduction path in the presence of uncertainty. Robust optimization was performed to locate the electrical impulses based on the local conduction velocity and the geodesic distances between catheter sensors. The proposed algorithm can identify the focal sources when the electrical conduction is initiated by irregular electrical impulses and involves wave collisions, breakups, and spiral waves. The statistical modeling framework can efficiently deal with data uncertainties and provide a reliable estimation of the focal source locations. This shows the great potential of a statistical approach for the quantitative analysis of the stochastic activity of electrical waves in cardiac disorders and suggests future investigations integrating statistical methods with a deterministic geometry-based method to achieve advanced diagnostic performance.
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Affiliation(s)
- Amirhossein Koneshloo
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Dongping Du
- Department of Industrial, Manufacturing and Systems Engineering, Texas Tech University, Lubbock, TX 79409, USA
| | - Yuncheng Du
- Department of Chemical & Biomolecular Engineering, Clarkson University, Potsdam, NY 13699, USA
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Cedilnik N, Duchateau J, Dubois R, Sacher F, Jaïs P, Cochet H, Sermesant M. Fast personalized electrophysiological models from computed tomography images for ventricular tachycardia ablation planning. Europace 2019; 20:iii94-iii101. [PMID: 30476056 DOI: 10.1093/europace/euy228] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2018] [Accepted: 09/18/2018] [Indexed: 11/12/2022] Open
Abstract
Aims Clinical application of patient-specific cardiac computer models requires fast and robust processing pipelines that can be seamlessly integrated into clinical workflows. We aim at building such a pipeline from computed tomography (CT) images to personalized cardiac electrophysiology (EP) model. The simulation output could be useful in the context of post-infarct ventricular tachycardia (VT) radiofrequency ablation (RFA) planning for pre-operative targets prediction. Methods and results The support for model personalization is a patient-specific virtual three-dimensional heart obtained from CT images. Here, the scar is identified as thinning of the myocardial wall on automatically computed thickness maps. We then use an Eikonal model of wave front propagation with reduced velocity in the damaged areas. An image-based vessel enhancement algorithm can automatically identify VT isthmuses. The personalized model is used for virtual pacing. We obtained a very fast pipeline that enables simulations in only a few minutes. It is fully automated starting from the semi-automated image segmentation phase. The computational time frame is compatible with the construction of a virtual pacing tool. In this tool, onset points and an optional directional block could be interactively selected. The directional block is a simple way to model tissue refractoriness. Output activation maps are compared with EP data acquired pre-operatively. We show that this framework allows the reproduction of recorded re-entrant VT activation patterns. Conclusion Our simulation framework has an application in VT RFA intervention planning. It could be used to guide EP explorations and even predict ablation targets pre-operatively. This could reduce intervention duration and improve success rate.
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Affiliation(s)
- Nicolas Cedilnik
- Université Côte d'Azur, Inria, Epione, Sophia Antipolis, France & Liryc Institute, Bordeaux, France
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Odille F, Battaglia A, Hoyland P, Sellal JM, Voilliot D, de Chillou C, Felblinger J. Catheter Treatment of Ventricular Tachycardia: A Reference-Less Pace-Mapping Method to Identify Ablation Targets. IEEE Trans Biomed Eng 2019; 66:3278-3287. [PMID: 30843798 DOI: 10.1109/tbme.2019.2903631] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE A novel method is developed to identify ablation targets for the catheter treatment of ventricular tachycardia (VT). METHODS The method is based on pace-mapping, which is a validated technique to determine the catheter ablation targets. Conventionally, it consists of stimulating the heart ventricle from various sites and comparing the resulting activation pathways to that of a clinical VT by the analysis of surface electrocardiograms (ECG). In this paper, a novel pace-mapping method is presented, which does not require a reference ECG recording of the VT. A three-dimensional correlation gradient map is reconstructed by semiautomatic analysis of ECG morphological changes within the network of pace-mapping sites. In these maps, abnormal points are identified by high correlation gradient values (i.e., corresponding to slow propagation of the electric influx, as in the core of the reentrant VT circuit). The relation between the conventional and reference-less method is described theoretically and evaluated in a retrospective study including 24 VT ablation procedures. RESULTS The "reference-less" method was able to identify normal points with a high accuracy (negative predictive value: NPV = 97%), and to detect more abnormal points, as predicted by the theory. Correlation gradients computed by the proposed method were significantly higher in ablation zones than in other zones of the ventricle (p < 10-12), indicating excellent prediction of the ablation targets. SIGNIFICANCE The reference-less method might either be used in complement of the conventional method or to treat patients in whom VT cannot be induced during the intervention.
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Yang T, Yu L, Jin Q, Wu L, He B. Localization of Origins of Premature Ventricular Contraction by Means of Convolutional Neural Network From 12-Lead ECG. IEEE Trans Biomed Eng 2018; 65:1662-1671. [PMID: 28952932 PMCID: PMC6089373 DOI: 10.1109/tbme.2017.2756869] [Citation(s) in RCA: 39] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
OBJECTIVE This paper proposes a novel method to localize origins of premature ventricular contractions (PVCs) from 12-lead electrocardiography (ECG) using convolutional neural network (CNN) and a realistic computer heart model. METHODS The proposed method consists of two CNNs (Segment CNN and Epi-Endo CNN) to classify among ventricular sources from 25 segments and from epicardium (Epi) or endocardium (Endo). The inputs are the full time courses and the first half of QRS complexes of 12-lead ECG, respectively. After registering the ventricle computer model with an individual patient's heart, the training datasets were generated by multiplying ventricular current dipoles derived from single pacing at various locations with patient-specific lead field. The origins of PVC are localized by calculating the weighted center of gravity of classification returned by the CNNs. A number of computer simulations were conducted to evaluate the proposed method under a variety of noise levels and heart registration errors. Furthermore, the proposed method was evaluated on 90 PVC beats from nine human patients with PVCs and compared against ablation outcome in the same patients. RESULTS The computer simulation evaluation returned relatively high accuracies for Segment CNN (∼78%) and Epi-Endo CNN (∼90%). Clinical testing in nine PVC patients resulted an averaged localization error of 11 mm. CONCLUSION Our simulation and clinical evaluation results demonstrate the capability and merits of the proposed CNN-based method for localization of PVC. SIGNIFICANCE This paper suggests a new approach for cardiac source localization of origin of arrhythmias using only the 12-lead ECG by means of CNN, and may have important applications for future real-time monitoring and localizing origins of cardiac arrhythmias guiding ablation treatment.
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Lozoya RC, Berte B, Cochet H, Jais P, Ayache N, Sermesant M. Model-Based Feature Augmentation for Cardiac Ablation Target Learning From Images. IEEE Trans Biomed Eng 2018; 66:30-40. [PMID: 29993400 DOI: 10.1109/tbme.2018.2818300] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
GOAL We present a model-based feature augmentation scheme to improve the performance of a learning algorithm for the detection of cardiac radio-frequency ablation (RFA) targets with respect to learning from images alone. METHODS Initially, we compute image features from delayed-enhanced magnetic resonance imaging (DE-MRI) to describe local tissue heterogeneities and feed them into a machine learning framework with uncertainty assessment for the identification of potential ablation targets. Next, we introduce the use of a patient-specific image-based model derived from DE-MRI coupled with the Mitchell-Schaeffer electrophysiology model and a dipole formulation for the simulation of intracardiac electrograms. Relevant features are extracted from these simulated signals which serve as a feature augmentation scheme for the learning algorithm. We assess the classifier's performance when using only image features and with model-based feature augmentation. RESULTS We obtained average classification scores of 97.2 % accuracy, 82.4 % sensitivity, and 95.0 % positive predictive value by using a model-based feature augmentation scheme. Preliminary results also show that training the algorithm on the closest patient from the database, instead of using all the patients, improves the classification results. CONCLUSION We presented a feature augmentation scheme based on biophysical cardiac electrophysiology modeling to increase the prediction scores of a machine learning framework for the RFA target prediction. SIGNIFICANCE The results derived from this study are a proof of concept that the use of model-based feature augmentation strengthens the performance of a purely image driven learning scheme for the prediction of cardiac ablation targets.
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