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Dogrusoz YS, Rasoolzadeh N, Ondrusova B, Hlivak P, Zelinka J, Tysler M, Svehlikova J. Comparison of dipole-based and potential-based ECGI methods for premature ventricular contraction beat localization with clinical data. Front Physiol 2023; 14:1197778. [PMID: 37362428 PMCID: PMC10288213 DOI: 10.3389/fphys.2023.1197778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/22/2023] [Indexed: 06/28/2023] Open
Abstract
Introduction: Localization of premature ventricular contraction (PVC) origin to guide the radiofrequency ablation (RFA) procedure is one of the prominent clinical goals of non-invasive electrocardiographic imaging. However, the results reported in the literature vary significantly depending on the source model and the level of complexity in the forward model. This study aims to compare the paced and spontaneous PVC localization performances of dipole-based and potential-based source models and corresponding inverse methods using the same clinical data and to evaluate the effects of torso inhomogeneities on these performances. Methods: The publicly available EP solution data from the EDGAR data repository (BSPs from a maximum of 240 electrodes) with known pacing locations and the Bratislava data (BSPs in 128 leads) with spontaneous PVCs from patients who underwent successful RFA procedures were used. Homogeneous and inhomogeneous torso models and corresponding forward problem solutions were used to relate sources on the closed epicardial and epicardial-endocardial surfaces. The localization error (LE) between the true and estimated pacing site/PVC origin was evaluated. Results: For paced data, the median LE values were 25.2 and 13.9 mm for the dipole-based and potential-based models, respectively. These median LE values were higher for the spontaneous PVC data: 30.2-33.0 mm for the dipole-based model and 28.9-39.2 mm for the potential-based model. The assumption of inhomogeneities in the torso model did not change the dipole-based solutions much, but using an inhomogeneous model improved the potential-based solutions on the epicardial-endocardial ventricular surface. Conclusion: For the specific task of localization of pacing site/PVC origin, the dipole-based source model is more stable and robust than the potential-based source model. The torso inhomogeneities affect the performances of PVC origin localization in each source model differently. Hence, care must be taken in generating patient-specific geometric and forward models depending on the source model representation used in electrocardiographic imaging (ECGI).
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Affiliation(s)
- Yesim Serinagaoglu Dogrusoz
- Department of Electrical-Electronics Engineering, Middle East Technical University, Ankara, Türkiye
- Department of Scientific Computing, Middle East Technical University, Institute of Applied Mathematics, Ankara, Türkiye
| | - Nika Rasoolzadeh
- Department of Electrical-Electronics Engineering, Middle East Technical University, Ankara, Türkiye
- Department of Scientific Computing, Middle East Technical University, Institute of Applied Mathematics, Ankara, Türkiye
| | - Beata Ondrusova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
- Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Bratislava, Slovakia
| | - Peter Hlivak
- National Institute for Cardiovascular Diseases, Bratislava, Slovakia
| | - Jan Zelinka
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Milan Tysler
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Jana Svehlikova
- Institute of Measurement Science, Slovak Academy of Sciences, Bratislava, Slovakia
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2
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Koh Y, Voskoboinik A, Neil C. Arrhythmias and Their Electrophysiological Mechanisms in Takotsubo Syndrome: A Narrative Review. Heart Lung Circ 2022; 31:1075-1084. [PMID: 35562239 DOI: 10.1016/j.hlc.2022.03.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/19/2022] [Accepted: 03/30/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Takotsubo syndrome (TTS), an acute and usually reversible condition, is associated with both tachy- and bradyarrhythmias. Such arrhythmias can be life-threatening, e.g. ventricular tachycardia and fibrillation, and associated with cardiac arrest. Others, such as atrioventricular block, persist and require long-term device therapy. In this narrative review, we aim to provide a summary of the current literature on arrhythmias in TTS and their clinical sequelae. METHODS PubMed and Medline databases were searched with various permutations of TTS, arrhythmias and beta-adrenoceptors. After application of exclusion criteria and review, 84 articles were included. RESULTS Although there are no specific electrocardiograph (ECG) findings in TTS to differentiate it from ST-elevation myocardial infarction, suggestive patterns include small QRS amplitude, ST segment elevation without reciprocal ST depression and prolonged QT interval. Atrial tachyarrhythmias (incidence of 5-15%) are associated with a more unwell patient cohort. Ventricular arrhythmias (incidence 4-14%) are often associated with prolonged QT interval and are a cause of sudden death in TTS. Bradyarrhythmias are less common (incidence 1.3-2.5%), but have been reported with TTS, and usually persist beyond the acute phase. CONCLUSIONS Takotsubo syndrome, though considered primarily a disease of the myocardium, carries multiple arrhythmic manifestations that affect short- and long-term prognosis. The management of such arrhythmias represents a constantly evolving area of research.
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Affiliation(s)
- Y Koh
- Department of Cardiology, Western Health, Melbourne, Vic, Australia.
| | - A Voskoboinik
- Department of Cardiology, Western Health, Melbourne, Vic, Australia
| | - C Neil
- Department of Cardiology, Western Health, Melbourne, Vic, Australia
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3
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Gyawali PK, Murkute JV, Toloubidokhti M, Jiang X, Horacek BM, Sapp JL, Wang L. Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data. IEEE Trans Biomed Eng 2022; 69:860-870. [PMID: 34460360 PMCID: PMC8858595 DOI: 10.1109/tbme.2021.3108164] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE This work investigates the possibility of disentangled representation learning of inter-subject anatomical variations within electrocardiographic (ECG) data. METHODS Since ground truth anatomical factors are generally not known in clinical ECG for assessing the disentangling ability of the models, the presented work first proposes the SimECG data set, a 12-lead ECG data set procedurally generated with a controlled set of anatomical generative factors. Second, to perform such disentanglement, the presented method evaluates and compares deep generative models with latent density modeled by nonparametric Indian Buffet Process to account for the complex generative process of ECG data. RESULTS In the simulated data, the experiments demonstrate, for the first time, concrete evidence of the possibility to disentangle key generative anatomical factors within ECG data in separation from task-relevant generative factors. We achieve a disentanglement score of 92.1% while disentangling five anatomical generative factors and the task-relevant generative factor. In both simulated and real-data experiments, this work further provides quantitative evidence for the benefit of disentanglement learning on the downstream clinical task of localizing the origin of ventricular activation. Overall, the presented method achieves an improvement of around 18.5%, and 11.3% for the simulated dataset, and around 7.2%, and 3.6% for the real dataset, over baseline CNN, and standard generative model, respectively. CONCLUSION These results demonstrate the importance as well as the feasibility of the disentangled representation learning of inter-subject anatomical variations within ECG data. SIGNIFICANCE This work suggests the important research direction to deal with the well-known challenge posed by the presence of significant inter-subject variations during an automated analysis of ECG data.
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4
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Bergquist J, Rupp L, Zenger B, Brundage J, Busatto A, MacLeod RS. Body Surface Potential Mapping: Contemporary Applications and Future Perspectives. HEARTS 2021; 2:514-542. [PMID: 35665072 PMCID: PMC9164986 DOI: 10.3390/hearts2040040] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/01/2023] Open
Abstract
Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.
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Affiliation(s)
- Jake Bergquist
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Lindsay Rupp
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Brian Zenger
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
- School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - James Brundage
- School of Medicine, University of Utah, Salt Lake City, UT 84112, USA
| | - Anna Busatto
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
| | - Rob S. MacLeod
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT 84112, USA
- Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT 84112, USA
- Nora Eccles Harrison Cardiovascular Research and Training Institute, University of Utah, Salt Lake City, UT 84112, USA
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5
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Graham AJ, Schilling RJ. The Use of Electrocardiographic Imaging in Localising the Origin of Arrhythmias During Catheter Ablation of Ventricular Tachycardia. Arrhythm Electrophysiol Rev 2021; 10:211-217. [PMID: 34777827 PMCID: PMC8576495 DOI: 10.15420/aer.2021.27] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/08/2021] [Accepted: 08/09/2021] [Indexed: 11/10/2022] Open
Abstract
Non-invasive electrocardiographic imaging (ECGI) is a novel clinical tool for mapping ventricular arrhythmia. Using multiple body surface electrodes to collect unipolar electrograms and conventional medical imaging of the heart, an epicardial shell can be created to display calculated electrograms. This calculation is achieved by solving the inverse problem and allows activation times to be calculated from a single beat. The technology was initially pioneered in the US using an experimental torso-shaped tank. Accuracy from studies in humans has varied. Early data was promising, with more recent work suggesting only moderate accuracy when reproducing cardiac activation. Despite these limitations, the system has been successfully used in pioneering work with non-invasive cardiac radioablation to treat ventricular arrhythmia. This suggests that the resolution may be sufficient for treatment of large target areas. Although untested in a well conducted clinical study it is likely that it would not be accurate enough to guide more discreet radiofrequency ablation.
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Affiliation(s)
- Adam J Graham
- Barts Heart Centre, St Bartholomew's Hospital, London, UK
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6
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Zaman MS, Dhamala J, Bajracharya P, Sapp JL, Horácek BM, Wu KC, Trayanova NA, Wang L. Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning. Front Physiol 2021; 12:740306. [PMID: 34759835 PMCID: PMC8573318 DOI: 10.3389/fphys.2021.740306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 09/24/2021] [Indexed: 11/13/2022] Open
Abstract
Probabilistic estimation of cardiac electrophysiological model parameters serves an important step toward model personalization and uncertain quantification. The expensive computation associated with these model simulations, however, makes direct Markov Chain Monte Carlo (MCMC) sampling of the posterior probability density function (pdf) of model parameters computationally intensive. Approximated posterior pdfs resulting from replacing the simulation model with a computationally efficient surrogate, on the other hand, have seen limited accuracy. In this study, we present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to query the simulation model in order to learn the posterior pdf using a small number of samples. We integrate a generative model into Bayesian active learning to allow approximating posterior pdf of high-dimensional model parameters at the resolution of the cardiac mesh. We further introduce new acquisition functions to focus the selection of training points on better approximating the shape rather than the modes of the posterior pdf of interest. We evaluated the presented method in estimating tissue excitability in a 3D cardiac electrophysiological model in a range of synthetic and real-data experiments. We demonstrated its improved accuracy in approximating the posterior pdf compared to Bayesian active learning using regular acquisition functions, and substantially reduced computational cost in comparison to existing standard or accelerated MCMC sampling.
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Affiliation(s)
- Md Shakil Zaman
- Rochester Institute of Technology, Rochester, NY, United States
| | - Jwala Dhamala
- Rochester Institute of Technology, Rochester, NY, United States
| | | | - John L Sapp
- Department of Medicine, Dalhousie University, Halifax, NS, Canada
| | - B Milan Horácek
- Department of Electrical and Computer Engineering, Halifax, NS, Canada
| | - Katherine C Wu
- Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Natalia A Trayanova
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, United States
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7
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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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8
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Kim Y, Chen S, Ernst S, Guzman CE, Han S, Kalarus Z, Labadet C, Lin Y, Lo L, Nogami A, Saad EB, Sapp J, Sticherling C, Tilz R, Tung R, Kim YG, Stiles MK. 2019 APHRS expert consensus statement on three-dimensional mapping systems for tachycardia developed in collaboration with HRS, EHRA, and LAHRS. J Arrhythm 2020; 36:215-270. [PMID: 32256872 PMCID: PMC7132207 DOI: 10.1002/joa3.12308] [Citation(s) in RCA: 54] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Accepted: 01/20/2020] [Indexed: 12/24/2022] Open
Affiliation(s)
- Young‐Hoon Kim
- Department of Internal MedicineArrhythmia CenterKorea University Medicine Anam HospitalSeoulRepublic of Korea
| | - Shih‐Ann Chen
- Division of CardiologyDepartment of MedicineTaipei Veterans General HospitalTaipeiROC
| | - Sabine Ernst
- Department of CardiologyRoyal Brompton and Harefield HospitalImperial College LondonLondonUK
| | | | - Seongwook Han
- Division of CardiologyDepartment of Internal MedicineKeimyung University School of MedicineDaeguRepublic of Korea
| | - Zbigniew Kalarus
- Department of CardiologyMedical University of SilesiaKatowicePoland
| | - Carlos Labadet
- Cardiology DepartmentArrhythmias and Electrophysiology ServiceClinica y Maternidad Suizo ArgentinaBuenos AiresArgentina
| | - Yenn‐Jian Lin
- Division of CardiologyDepartment of MedicineTaipei Veterans General HospitalTaipeiROC
| | - Li‐Wei Lo
- Division of CardiologyDepartment of MedicineTaipei Veterans General HospitalTaipeiROC
| | - Akihiko Nogami
- Department of CardiologyFaculty of MedicineUniversity of TsukubaTsukubaJapan
| | - Eduardo B. Saad
- Center for Atrial FibrillationHospital Pro‐CardiacoRio de JaneiroBrazil
| | - John Sapp
- Division of CardiologyDepartment of MedicineQEII Health Sciences CentreDalhousie UniversityHalifaxNSCanada
| | | | - Roland Tilz
- Medical Clinic II (Department of Cardiology, Angiology and Intensive Care Medicine)University Hospital Schleswig‐Holstein (UKSH) – Campus LuebeckLuebeckGermany
| | - Roderick Tung
- Center for Arrhythmia CarePritzker School of MedicineUniversity of Chicago MedicineChicagoILUSA
| | - Yun Gi Kim
- Department of Internal MedicineArrhythmia CenterKorea University Medicine Anam HospitalSeoulRepublic of Korea
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Xie S, Wang L, Zhang H, Liu H. Non-invasive reconstruction of dynamic myocardial transmembrane potential with graph-based total variation constraints. Healthc Technol Lett 2020; 6:181-186. [PMID: 32038854 PMCID: PMC6945684 DOI: 10.1049/htl.2019.0065] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Accepted: 10/02/2019] [Indexed: 11/28/2022] Open
Abstract
Non-invasive reconstruction of electrophysiological activity in the heart is of great significance for clinical disease prevention and surgical treatment. The distribution of transmembrane potential (TMP) in three-dimensional myocardium can help us diagnose heart diseases such as myocardial ischemia and ectopic pacing. However, the problem of solving TMP is ill-posed, and appropriate constraints need to be added. The existing state-of-art method total variation minimisation only takes advantage of the local similarity in space, which has the problem of over-smoothing, and fails to take into account the relationship among frames in the dynamic TMP sequence. In this work, the authors introduce a novel regularisation method called graph-based total variation to make up for the above shortcomings. The graph structure takes the TMP value of a time sequence on each heart node as the criterion to establish the similarity relationship among the heart. Two sets of phantom experiments were set to verify the superiority of the proposed method over the traditional constraints: infarct scar reconstruction and activation wavefront reconstruction. In addition, experiments with ten real premature ventricular contractions patient data were used to demonstrate the accuracy of the authors’ method in clinical applications.
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Affiliation(s)
- Shuting Xie
- State Key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Linwei Wang
- Computational Biomedicine Laboratory, Golisano College of Computing and Information Sciences, Rochester Institute of Technology, Rochester, NY 14623, USA
| | - Heye Zhang
- School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen 510006, People's Republic of China
| | - Huafeng Liu
- State Key Laboratory of Modern Optical Instrumentation, Department of Optical Engineering, Zhejiang University, Hangzhou 310027, People's Republic of China
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10
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Zenger B, Good WW, Bergquist JA, Burton BM, Tate JD, Berkenbile L, Sharma V, MacLeod RS. Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform. Physiol Meas 2020; 41:015002. [PMID: 31860892 DOI: 10.1088/1361-6579/ab64b9] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Myocardial ischemia is one of the most common cardiovascular pathologies and can indicate many severe and life threatening diseases. Despite these risks, current electrocardiographic detection techniques for ischemia are mediocre at best, with reported sensitivity and specificity ranging from 50%-70% and 70%-90%, respectively. OBJECTIVE To improve this performance, we set out to develop an experimental preparation to induce, detect, and analyze bioelectric sources of myocardial ischemia and determine how these sources reflect changes in body-surface potential measurements. APPROACH We designed the experimental preparation with three important characteristics: (1) enable comprehensive and simultaneous high-resolution electrical recordings within the myocardial wall, on the heart surface, and on the torso surface; (2) develop techniques to visualize these recorded electrical signals in time and space; and (3) accurately and controllably simulate ischemic stress within the heart by modulating the supply of blood, the demand for perfusion, or a combination of both. MAIN RESULTS To achieve these goals we designed comprehensive system that includes (1) custom electrode arrays (2) signal acquisition and multiplexing units, (3) a surgical technique to place electrical recording and myocardial ischemic control equipment, and (4) an image based modeling pipeline to acquire, process, and visualize the results. With this setup, we are uniquely able to capture simultaneously and continuously the electrical signatures of acute myocardial ischemia within the heart, on the heart surface, and on the body surface. SIGNIFICANCE This novel experimental preparation enables investigation of the complex and dynamic nature of acute myocardial ischemia that should lead to new, clinically translatable results.
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Affiliation(s)
- Brian Zenger
- Scientific Computing and Imaging Institute, SLC, UT, United States of America. Nora Eccles Cardiovascular Research and Training Institute, SLC, UT, United States of America. School of Medicine, University of Utah, SLC, UT, United States of America. Department of Biomedical Engineering, University of Utah, SLC, UT, United States of America. Author to whom any correspondence should be addressed
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11
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Erenler T, Serinagaoglu Dogrusoz Y. ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging. Med Biol Eng Comput 2019; 57:2093-2113. [PMID: 31363890 DOI: 10.1007/s11517-019-02018-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2018] [Accepted: 07/16/2019] [Indexed: 11/27/2022]
Abstract
In electrocardiographic imaging (ECGI), one solves the inverse problem of electrocardiography (ECG) to reconstruct equivalent cardiac sources based on the body surface potential measurements and a mathematical model of the torso. Due to attenuation and spatial smoothing within the torso, this inverse problem is ill-posed. Among many regularization approaches used in the ECG literature to overcome this ill-posedness, statistical techniques have received great attention because of their flexibility to represent the data, and ability to provide performance evaluation tools for quantification of uncertainties and errors in the model. However, despite their potential to accurately reconstruct the equivalent cardiac sources, one major challenge in these methods is how to best utilize the prior information available in terms of training data. In this paper, we address the question of how to define the prior probability distributions (pdf) of the sources and the error terms so that we can obtain more accurate and robust inverse solutions. We employ two methods, maximum likelihood (ML) and maximum a posteriori (MAP), for estimating the model parameters such as the prior pdfs, error pdfs, and the state-transition matrix, based on the same training data. These model parameters are then used for the state-space representation and estimation of the epicardial potentials, which constitute the equivalent cardiac sources in this study. The performances of ML- and MAP-based model parameter estimation methods are evaluated qualitatively and quantitatively at various noise levels and geometric disturbances using two different simulated datasets. Bayesian MAP estimation, which is also a well-known statistical inversion technique, and Tikhonov regularization, which can be formulated as a special and simplified version of Bayesian MAP estimation, have been included here for comparison with the Kalman filtering method. Our results show that the state-space approach outperforms Bayesian MAP estimation in all cases; ML yields accurate results when the test and training beats come from the same physiological model, but MAP is superior to ML, especially if the test and training beats are from different physiological models. Graphical Abstract ML and MAP estimation of parameters for the Kalman filter and smoother applied to electrocardiographic imaging.
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Affiliation(s)
- Taha Erenler
- Department of Electrical and Electronics Engineering, Middle East Technical University, Üniversiteler Mahallesi Dumlupınar Bulvarı No:1, 06800, Çankaya, Ankara, Turkey
| | - Yesim Serinagaoglu Dogrusoz
- Department of Electrical and Electronics Engineering, Middle East Technical University, Üniversiteler Mahallesi Dumlupınar Bulvarı No:1, 06800, Çankaya, Ankara, Turkey.
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12
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Alawad M, Wang L. Learning Domain Shift in Simulated and Clinical Data: Localizing the Origin of Ventricular Activation From 12-Lead Electrocardiograms. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:1172-1184. [PMID: 30418900 PMCID: PMC6601334 DOI: 10.1109/tmi.2018.2880092] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
Building a data-driven model to localize the origin of ventricular activation from 12-lead electrocardiograms (ECG) requires addressing the challenge of large anatomical and physiological variations across individuals. The alternative of a patient-specific model is, however, difficult to implement in clinical practice because the training data must be obtained through invasive procedures. In this paper, we present a novel approach that overcomes this problem of the scarcity of clinical data by transferring the knowledge from a large set of patient-specific simulation data while utilizing domain adaptation to address the discrepancy between the simulation and clinical data. The method that we have developed quantifies non-uniformly distributed simulation errors, which are then incorporated into the process of domain adaptation in the context of both classification and regression. This yields a quantitative model that, with the addition of 12-lead ECG data from each patient, provides progressively improved patient-specific localizations of the origin of ventricular activation. We evaluated the performance of the presented method in localizing 75 pacing sites on three in-vivo premature ventricular contraction (PVC) patients. We found that the presented model showed an improvement in localization accuracy relative to a model trained on clinical ECG data alone or a model trained on combined simulation and clinical data without considering domain shift. Furthermore, we demonstrated the ability of the presented model to improve the real-time prediction of the origin of ventricular activation with each added clinical ECG data, progressively guiding the clinician towards the target site.
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13
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Bear LR, Walton RD, Abell E, Coudière Y, Haissaguerre M, Bernus O, Dubois R. Optical Imaging of Ventricular Action Potentials in a Torso Tank: A New Platform for Non-Invasive Electrocardiographic Imaging Validation. Front Physiol 2019; 10:146. [PMID: 30863318 PMCID: PMC6399141 DOI: 10.3389/fphys.2019.00146] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Accepted: 02/07/2019] [Indexed: 12/12/2022] Open
Abstract
Background: Non-invasive electrocardiographic imaging (ECGI) is a promising tool to provide high-resolution panoramic imaging of cardiac electrical activity noninvasively from body surface potential measurements. Current experimental methods for ECGI validation are limited to comparison with unipolar electrograms and the relatively low spatial resolution of cardiac mapping arrays. We aim to develop a novel experimental set up combining a human shaped torso tank with high-resolution optical mapping allowing the validation of ECGI reconstructions. Methods: Langendorff-perfused pig hearts (n = 3) were suspended in a human torso-shaped tank, with the left anterior descending artery (LAD) cannulated on a separate perfusion. Electrical signals were recorded from an 108-electrode epicardial sock and 128 electrodes embedded in the tank surface. Simultaneously, optical mapping of the heart was performed through the anterior surface of the tank. Recordings were made in sinus rhythm and ventricular pacing (n = 55), with activation and repolarization heterogeneities induced by perfusion of hot and cold solutions as well as Sotalol through the LAD. Fluoroscopy provided 3D cardiac and electrode geometries in the tank that were transformed to the 2D optical mapping window using an optimization algorithm. Epicardial unipolar electrograms were reconstructed from torso potentials using ECGI and validated using optical activation and repolarization maps. Results: The transformation and alignment of the 3D geometries onto the 2D optical mapping window was good with an average correlation of 0.87 ± 0.10 and error of 7.7 ± 3.1 ms with activation derived from the sock. The difference in repolarization times were more substantial (error = 17.4 ± 3.7 ms) although the sock and optical repolarization patterns themselves were very similar (correlation = 0.83 ± 0.13). Validation of ECGI reconstructions revealed ECGI accurately captures the pattern of activation (correlation = 0.79 ± 0.11) and identified regions of late and/or early repolarization during different perfusions through LAD. ECGI also correctly demonstrated gradients in both activation and repolarization, although in some cases these were under or over-estimated or shifted slightly in space. Conclusion: A novel experimental setup has been developed, combining a human-shaped torso tank with optical mapping, which can be effectively used in the validation of ECGI techniques; including the reconstruction of activation and repolarization patterns and gradients.
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Affiliation(s)
- Laura R Bear
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
| | - Richard D Walton
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
| | - Emma Abell
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
| | - Yves Coudière
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,CARMEN Research Team, INRIA, Talence, France.,CNRS, IMB, UMR 5251, Talence, France
| | - Michel Haissaguerre
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,Bordeaux University Hospital (CHU), Electrophysiology and Ablation Unit, Pessac, France
| | - Olivier Bernus
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
| | - Rémi Dubois
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Bordeaux, France.,Univ. Bordeaux, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France.,INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, Bordeaux, France
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Potyagaylo D, Chmelevsky M, van Dam P, Budanova M, Zubarev S, Treshkur T, Lebedev D. ECG Adapted Fastest Route Algorithm to Localize the Ectopic Excitation Origin in CRT Patients. Front Physiol 2019; 10:183. [PMID: 30914963 PMCID: PMC6421262 DOI: 10.3389/fphys.2019.00183] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 02/14/2019] [Indexed: 01/15/2023] Open
Abstract
Although model-based solution strategies for the ECGI were reported to deliver promising clinical results, they strongly rely on some a priori assumptions, which do not hold true for many pathological cases. The fastest route algorithm (FRA) is a well-established method for noninvasive imaging of ectopic activities. It generates test activation sequences on the heart and compares the corresponding test body surface potential maps (BSPMs) to the measured ones. The test excitation propagation patterns are constructed under the assumption of a global conduction velocity in the heart, which is violated in the cardiac resynchronization (CRT) patients suffering from conduction disturbances. In the present work, we propose to apply dynamic time warping (DTW) to the test and measured ECGs before measuring their similarity. The warping step is a non-linear pattern matching that compensates for local delays in the temporal sequences, thus accounting for the inhomogeneous excitation propagation, while aligning them in an optimal way with respect to a distance function. To evaluate benefits of the temporal warping for FRA-based BSPMs, we considered three scenarios. In the first setting, a simplified simulation example was constructed to illustrate the temporal warping and display the resulting distance map. Then, we applied the proposed method to eight BSPMs produced by realistic ectopic activation sequences and compared its performance to FRA. Finally, we assessed localization accuracy of both techniques in ten CRT patients. For each patient, we noninvasively imaged two paced ECGs: from left and right ventricular implanted leads. In all scenarios, FRA-DTW outperformed FRA in terms of LEs. For the clinical cases, the median (25-75% range) distance errors were reduced from 16 (8-23)mm to 5 (2-10)mm for all pacings, from 15 (11-25)mm to 8 (3-13)mm in the left, and from 19 (6-23)mm to 4 (2-8)mm in the right ventricle, respectively. The obtained results suggest the ability of temporal ECG warping to compensate for an inhomogeneous conduction profile, while retaining computational efficiency intrinsic to FRA.
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Affiliation(s)
| | - Mikhail Chmelevsky
- EP Solutions SA, Yverdon-les-Bains, Switzerland.,Almazov National Medical Research Center, Saint Petersburg, Russia
| | - Peter van Dam
- Cardiology Department, University Medical Center Utrecht, Utrecht, Netherlands
| | | | - Stepan Zubarev
- Almazov National Medical Research Center, Saint Petersburg, Russia
| | - Tatjana Treshkur
- Almazov National Medical Research Center, Saint Petersburg, Russia
| | - Dmitry Lebedev
- Almazov National Medical Research Center, Saint Petersburg, Russia
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15
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Kalinin A, Potyagaylo D, Kalinin V. Solving the Inverse Problem of Electrocardiography on the Endocardium Using a Single Layer Source. Front Physiol 2019; 10:58. [PMID: 30804802 PMCID: PMC6370732 DOI: 10.3389/fphys.2019.00058] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Accepted: 01/18/2019] [Indexed: 12/04/2022] Open
Abstract
The inverse problem of electrocardiography consists in reconstructing cardiac electrical activity from given body surface electrocardiographic measurements. Despite tremendous progress in the field over the last decades, the solution of this problem in terms of electrical potentials on both epi- and the endocardial heart surfaces with acceptable accuracy remains challenging. This paper presents a novel numerical approach aimed at improving the solution quality on the endocardium. Our method exploits the solution representation in the form of electrical single layer densities on the myocardial surface. We demonstrate that this representation brings twofold benefits: first, the inverse problem can be solved for the physiologically meaningful single layer densities. Secondly, a conventional transfer matrix for electrical potentials can be split into two parts, one of which turned out to posess regularizing properties leading to improved endocardial reconstructions. The method was tested in-silico for ventricular pacings utilizing realistic CT-based heart and torso geometries. The proposed approach provided more accurate solution on the ventricular endocardium compared to the conventional potential-based solutions with Tikhonov regularization of the 0th, 1st, and 2nd orders. Furthermore, we show a uniform spatio-temporal behavior of the single layer densities over the heart surface, which could be conveniently employed in the regularization procedure.
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16
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Yang T, Pogwizd SM, Walcott GP, Yu L, He B. Noninvasive Activation Imaging of Ventricular Arrhythmias by Spatial Gradient Sparse in Frequency Domain-Application to Mapping Reentrant Ventricular Tachycardia. IEEE TRANSACTIONS ON MEDICAL IMAGING 2019; 38:525-539. [PMID: 30136937 PMCID: PMC6372101 DOI: 10.1109/tmi.2018.2866951] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
The aim of this paper is to develop and evaluate a novel imaging method [spatial gradient sparse in frequency domain (SSF)] for the reconstruction of activation sequences of ventricular arrhythmia from noninvasive body surface potential map (BSPM) measurements. We formulated and solved the electrocardiographic inverse problem in the frequency domain, and the activation time was encoded in the phase information of the imaging solution. A cellular automaton heart model was used to generate focal ventricular tachycardia (VT). Different levels of Gaussian white noise were added to simulate noise-contaminated BSPM. The performance of SSF was compared with that of weighted minimum norm inverse solution. We also evaluated the method in a swine model with simultaneous intracardiac and body surface recordings. Four reentrant VTs were observed in pigs with myocardial infarction generated by left anterior descending artery occlusion. The imaged activation sequences of reentrant VTs were compared with those obtained from intracardiac electrograms. In focal VT simulation, SSF has increased the correlation coefficient (CC) by 5% and decreased localization errors (LEs) by 2.7 mm on average under different noise levels. In the animal validation with reentrant VT, SSF has achieved an average CC of 88% and an average LE of 6.3 mm in localizing the earliest and latest activation site in the reentry circuit. Our promising results suggest that the SSF provides noninvasive imaging capability of detecting and mapping macro-reentrant circuits in 3-D ventricular space. The SSF may become a useful imaging tool of identifying and localizing the potential targets for ablation of focal and reentrant VT.
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Affiliation(s)
- Ting Yang
- Biomedical Engineering Department, University of Minnesota, Minneapolis, MN 55455, USA
| | - Steven M. Pogwizd
- Division of Cardiovascular Disease, School of Medicine, the University of Alabama at Birmingham, Birmingham, AL 0019, USA
| | - Gregory P. Walcott
- Division of Cardiovascular Disease, School of Medicine, the University of Alabama at Birmingham, Birmingham, AL 0019, USA
| | - Long Yu
- Biomedical Engineering Department, University of Minnesota, Minneapolis, MN 55455, USA
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Fang L, Xu J, Hu H, Chen Y, Shi P, Wang L, Liu H. Noninvasive Imaging of Epicardial and Endocardial Potentials With Low Rank and Sparsity Constraints. IEEE Trans Biomed Eng 2019; 66:2651-2662. [PMID: 30668450 DOI: 10.1109/tbme.2019.2894286] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
In this study, we explore the use of low rank and sparse constraints for the noninvasive estimation of epicardial and endocardial extracellular potentials from body-surface electrocardiographic data to locate the focus of premature ventricular contractions (PVCs). The proposed strategy formulates the dynamic spatiotemporal distribution of cardiac potentials by means of low rank and sparse decomposition, where the low rank term represents the smooth background and the anomalous potentials are extracted in the sparse matrix. Compared to the most previous potential-based approaches, the proposed low rank and sparse constraints are batch spatiotemporal constraints that capture the underlying relationship of dynamic potentials. The resulting optimization problem is solved using alternating direction method of multipliers. Three sets of simulation experiments with eight different ventricular pacing sites demonstrate that the proposed model outperforms the existing Tikhonov regularization (zero-order, second-order) and L1-norm based method at accurately reconstructing the potentials and locating the ventricular pacing sites. Experiments on a total of 39 cases of real PVC data also validate the ability of the proposed method to correctly locate ectopic pacing sites.
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18
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Zhou S, Sapp JL, Stovicek P, Horacek BM. Localization of Activation Origin on Patient-Specific Endocardial Surface by the Equivalent Double Layer (EDL) Source Model With Sparse Bayesian Learning. IEEE Trans Biomed Eng 2018; 66:2287-2295. [PMID: 30571613 DOI: 10.1109/tbme.2018.2887041] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Ablation treatment of ventricular arrhythmias can be facilitated by pre-procedure planning aided by electrocardiographic inverse solution, which can help to localize the origin of arrhythmia. Our aim was to improve localization accuracy of the inverse solution for activation originating on the left-ventricular endocardial surface, by using a sparse Bayesian learning (SBL). METHODS The inverse problem of electrocardiography was solved by reconstructing endocardial potentials from time integrals of body-surface electrocardiograms and from patient-specific geometry of the heart and torso for three patients with structurally normal ventricular myocardium, who underwent endocardial catheter mapping that included pace mapping. Complementary simulations using dipole sources in patient-specific geometry were also performed. The proposed method is using sparse property of the equivalent-double-layer (EDL) model of cardiac sources; it employs the SBL and makes use of the spatio-temporal features of the cardiac action potentials. RESULTS The mean localization error of the proposed method for pooled pacing sites ( n=52) was significantly smaller ( p=0.0039) than that achieved for the same patients in the study of Erem et al. Simulation experiments localized the source dipoles ( n=48) from forward-simulated potentials with the error of 9.4 ± 4.5 mm (mean ± SD). CONCLUSION The results of our clinical and simulation experiments demonstrate that localization of left-ventricular endocardial activation by means of the Bayesian approach, based on sparse representation of sources by EDL, is feasible and accurate. SIGNIFICANCE The proposed approach to localizing endocardial sources may have important applications in pre-procedure assessment of arrhythmias and in guiding their ablation treatment.
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Rodrigo M, Narayan SM. Statistical guidance of VT ablation. J Cardiovasc Electrophysiol 2018; 29:987-989. [PMID: 29771455 PMCID: PMC6467226 DOI: 10.1111/jce.13633] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/02/2018] [Accepted: 05/06/2018] [Indexed: 01/26/2023]
Affiliation(s)
- Miguel Rodrigo
- Stanford University, Stanford, CA, USA
- Universitat Politècnicade València, València, Spain
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Misra S, van Dam P, Chrispin J, Assis F, Keramati A, Kolandaivelu A, Berger R, Tandri H. Initial validation of a novel ECGI system for localization of premature ventricular contractions and ventricular tachycardia in structurally normal and abnormal hearts. J Electrocardiol 2018; 51:801-808. [PMID: 30177316 DOI: 10.1016/j.jelectrocard.2018.05.018] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 05/23/2018] [Accepted: 05/29/2018] [Indexed: 10/14/2022]
Abstract
BACKGROUND View into Ventricular Onset (VIVO) is a novel ECGI system that uses 3D body surface imaging, myocardial CT/MRI, and 12‑lead ECG to localize earliest ventricular activation through analysis of simulated and clinical vector cardiograms. OBJECTIVE To evaluate the accuracy of VIVO for the localization of ventricular arrhythmias (VA). METHODS In twenty patients presenting for catheter ablation of VT [8] or PVC [12], VIVO was used to predict the site earliest activation using 12‑lead ECG of the VA. Results were compared to invasive electroanatomic mapping (EAM). RESULTS A total of 22 PVC/VT morphologies were analyzed using VIVO. VIVO accurately predicted the location of the VA in 11/13 PVC cases and 8/9 VT cases. VIVO correctly predicted right vs left ventricular foci in 20/22 cases. CONCLUSION View into Ventricular Onset (VIVO) can accurately predict earliest activation of VA, which could aid in catheter ablation, and should be studied further.
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Affiliation(s)
- Satish Misra
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States.
| | - Peter van Dam
- Cardiac Arrhythmia Center, University of California - Los Angeles, 100 UCLA Medical Plaza, Suite 660, Los Angeles, CA 90095, United States
| | - Jonathan Chrispin
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States
| | - Fabrizio Assis
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States
| | - Ali Keramati
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States
| | - Aravindan Kolandaivelu
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States
| | - Ronald Berger
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States
| | - Harikrishna Tandri
- Division of Cardiology, The Johns Hopkins University School of Medicine, 1800 Orleans St, Zayed 7125, Baltimore, MD 21287, United States
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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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Mapping of ventricular arrhythmias using a novel noninvasive epicardial and endocardial electrophysiology system. J Electrocardiol 2018; 51:92-98. [DOI: 10.1016/j.jelectrocard.2017.07.018] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2017] [Indexed: 11/22/2022]
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